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2016ApJ...828...41S__Chatterjee_et_al._2004_Instance_1

Figure 1 shows the main features of the butterfly diagram: (1) the onset of the cycle at mid-latitudes; (2) the sunspot drift toward the equator and its slowdown represented by a change in the slope of the butterfly wing (Maunder 1904; Li et al. 2001); (3) the tail-like attachment over the minimum phase that is more prominent when the activity is stronger, which might lead to the overlap of successive cycles; (4) the length of the overlap varies within 1–2 years. It characterizes only the minimum phase and it is confined at latitudes ≤15° (Cliver 2014). This feature is also seen in torsional oscillations shown in the bottom panels of Figure 1 (Wilson et al. 1988; Howe et al. 2009). The rate of drift of sunspots toward the equator slows as the sunspot band approaches the equator, and halts at about 8° latitude (Hathaway et al. 2003). The end of the migration does not correspond to the end of the activity because it produces the tail-like attachment. When the new cycle at mid-latitudes starts before the end of the old cycle at low latitudes, it causes successive cycles to overlap. FTD models driven only by the Babcock–Leighton mechanism (Chatterjee et al. 2004), or along with the α-turbulent effect operating in the bulk of the convection zone, currently have the best agreement with observations (Passos et al. 2014), because the length of the simulated overlap is short and it occurs only during the minimum at low latitudes. Conversely, thin-shell dynamo wave models (Moss & Brooke 2000; Schüssler & Schmitt 2004; Bushby 2006) or the thin-shell flux transport dynamo (Dikpati & Gilman 2001) tend to produce dynamo waves with too short a wavelength, leading to excessive overlap between adjacent cycles because this involves a wider range of latitudes. Furthermore they also fail to reproduce the tail-like attachment over the minimum phase. Moreover the direction of the migration of activity could also provide information on the nature of the α mechanism. Both formalisms make strong assumptions to initiate the sunspot cycle at mid-latitudes. The Babcock–Leighton FTD models assume that the deep equatorward meridional flow penetrates slightly below the convection zone to a greater depth than usually believed (Nandy & Choudhuri 2002), in order to prevent the onset and occurrence of a sunspot cycle above 45° as well as any other kind of cyclic activity. The same result is achieved with the αΩ dynamo wave by inhibiting the α-turbulent effect at higher latitudes (Schüssler & Schmitt 2004). Based on these assumptions, the magnetic activity in any type of FTD model starts at higher latitudes and then propagates only equatorward, while in the thin-shell αΩ dynamo wave the magnetic activity can propagate poleward as well as equatorward (Bushby 2006). These two branches are also clearly seen in the torsional oscillation pattern (e.g., Howe et al. 2009). This results from the solar-like differential profile, which is characterized by a sign change in in the tachocline at high and low latitudes (Ruediger & Brandenburg 1995). This sign change, however, has not yet been confirmed by helioseismic observations.

2018AandA...619A.105T__Matt_et_al._2015_Instance_1

We have presented MoCA, a Monte Carlo code for Comptonisation in Astrophysics which includes polarisation. To our knowledge MoCA is the first code operating with single photons and including all special relativity and quantum effects. The main disadvantage of this approach is the long computing time, which implies the need to parallelise the code on clusters of computers. The advantage with respect to pure analytical models such as those available in XSPEC is that we can explore the totality of the parameters space for the Comptonising medium (i.e. thermal energy and optical thickness of the corona) without any restriction and this approach will also allow a better understanding of the whole process. We also included all corrections such as Klein–Nishina cross-section and scattering angle distribution. These effects, small below 100 keV, must nonetheless be taken into account when inferring the thermal energy of the corona from observations. In some sources it has been found that coronae can have extremely high energy cut-off (e.g. NGC 5506, Matt et al. 2015) and therefore thermal energy, which is inferred by measuring the curvature of NuSTAR spectra at high energy and in this context K–N effects cannot be neglected. From the polarimetric point of view we did not seen any deviation due to K–N effects, but this was to expected as we focussed our attention below 100 keV where these effects are small. However, one can imagine a scenario in which the thermal energy of the corona is few tens of keV and in that case we expect to see a difference both on the spectrum and the polarisation but we defer such investigation to future papers focussed on the exploration of the coronal parameters space. In its actual form the code is fast enough to explore different geometries of the corona with different parameters. Spectra can then be compared with those obtained by NuSTAR to derive coronal parameters, especially in the high optical depth regime where analytical models are not reliable. As already mentioned, much observational evidence points in the direction of compact coronae above or around the compact object. In order to properly treat such coronae, gravitational effects must be taken into account. We have recently included a ray-tracing routine to take into account GR effects: this new version of MoCA, and applications of the code to different astrophysical scenarios, will be discussed in future papers. Nonetheless we have shown the geometrical effect of more compact coronae on the spectra and the polarisation signal: the spectra become softer as the corona shrinks and the polarisation changes dramatically as we approach a more symmetrical shape of the corona. The study we performed will also be useful to quantify the impact of GR effects on compact coronae with respect to a purely geometrical effect.

2020ApJ...899..147F__Venot_et_al._2015_Instance_2

The C/O ratio varies across exoplanets’ host star populations (Delgado Mena et al. 2010; Brewer & Fischer 2016; Brewer et al. 2017), and this variation is likely to be reflected in the composition of exoplanet atmospheres, assuming that they are formed with the same materials as their stars. Moreover, various processes in the protoplanetary disks and the planet formation process can affect the exoplanet compositions and have a significant impact on the final C/O ratio (Öberg et al. 2011; Mordasini et al. 2016; Espinoza et al. 2017; Madhusudhan et al. 2017). For these reasons, it is necessary to consider the effects of the C/O ratio on the atmospheric chemistry and the formation of aerosols. Numerous studies have been performed using chemical models (Madhusudhan 2012; Moses et al. 2013; Venot et al. 2015; Tsai et al. 2017; Heng & Lyons 2016; Goyal et al. 2018; Drummond et al. 2019), but corresponding laboratory experiments are still largely nonexistent. Laboratory investigations can provide essential insight into the effects of the C/O ratio on the atmospheric photochemistry and the formation of aerosols. In a previous work, we performed the first laboratory experiments dedicated to the study of the chemistry in hot Jupiter atmospheres (Fleury et al. 2019). This work focused on the chemistry in atmospheres with T > 1000 K and a C/O ratio of 1 (representing C enhancement compared to the solar value of 0.54), because chemical models predict that the abundances of hydrocarbon and nitrile species increase by several orders of magnitude in these atmospheres compared to atmospheres with a low C/O ratio (Venot et al. 2015). Therefore, they can be considered as better candidates for the formation of complex organic molecules with longer carbon chains. This first study revealed that photochemical aerosols could be produced at temperatures as high as 1500 K and that water could be efficiently formed through photochemical channels. In the present work, we performed new experiments to study the chemistry in hot Jupiter atmospheres at similar temperatures (1173–1473 K) but with lower C/O ratios. We used a gas mixture of H2, H2O, and CO that represents the simplest plausible atmosphere for a hot Jupiter with a C/O ratio 1. This new study, compared with our previous work, allows us to assess the evolution of the chemistry in hot Jupiter atmospheres as a function of the C/O ratio and atmospheric composition.

2021MNRAS.500.1772N__Fernandez_et_al._2015_Instance_1

While these early studies demonstrated the viability of neutron star mergers as a major r-process site, they identified only one ejection channel: ‘dynamical ejecta’ that are tidally flung out by gravitational torques. Since they are never substantially heated, these ejecta carry their original β −equilibrium electron fraction from the original neutron star, Ye ≈ 0.05, and this enormous neutron-richness allows them to undergo a ‘fission cycling’ process (Goriely, Bauswein & Janka 2011; Korobkin et al. 2012), which produces a very robust r-process abundance distribution close to the solar pattern for A ≥ 130, but hardly any lighter r-process elements. Oechslin, Janka & Marek (2007) pointed out that there is a second channel of mass ejection that also happens on a dynamical time-scale: shock-heated matter from the interface where the stars come into contact. As of today, many more mass ejection channels have been discussed: matter that becomes unbound on secular time-scales (∼1 s) from the post-merger accretion torus (Beloborodov 2008; Metzger, Piro & Quataert 2008; Fernandez & Metzger 2013; Fernandez et al. 2015; Just et al. 2015; Siegel & Metzger 2017, 2018; Fernandez et al. 2019; Miller et al. 2019a), as MHD-driven winds (Siegel & Ciolfi 2015) and by viscous effects (Shibata, Kiuchi & Sekiguchi 2017; Radice et al. 2018a; Shibata & Hotokezaka 2019) from a long-lived neutron star merger remnant. Similar to the case of proto-neutron stars, the enormous neutrino luminosities (>1053 erg s−1) after a neutron star merger can also drive substantial matter outflows (Ruffert et al. 1997; Rosswog & Ramirez-Ruiz 2002; Dessart et al. 2009; Perego et al. 2014; Martin et al. 2015; Radice et al. 2018b). The secular torus ejecta contain approximately 40 per cent of the initial torus mass and, although the latter may vary substantially from case to case, they likely contribute the lion’s share to the total ejecta mass. Due to their different thermal histories and exposure times to neutrinos, the ejecta channels can have different electron fractions Ye and therefore different nucleosynthesis yields.1 For electron fractions below a critical value, $Y_{\rm e}^{\rm crit}\approx 0.25$ (Korobkin et al. 2012; Lippuner & Roberts 2015), lanthanides and actinides are efficiently produced, which, due to their open f-shells, have particularly high bound–bound opacities (Barnes & Kasen 2013; Kasen, Badnell & Barnes 2013; Tanaka & Hotokezaka 2013; Tanaka et al. 2020) and therefore lead to red transients that peak days after the merger. Ejecta with electron fractions above $Y_{\rm e}^{\rm crit}$, in contrast, only produce ‘lighter’ elements with lower opacities and thus result in bluer transients that peak after about 1 d. Opaque, low-Ye ejecta blocking the view on high-Ye ejecta can lead to a ‘lanthanide curtaining’ effect (Kasen, Fernández & Metzger 2015; Wollaeger et al. 2018), which will efficiently block blue light. Therefore, it is important to understand the layering, dynamics, interaction and potential mixing of different ejecta channels.

2022MNRAS.515.1795B__Yang_et_al._2021_Instance_2

The most widely adopted parametrization of the observed Universe is based on the so-called Λ cold dark matter (ΛCDM) model (Peebles 1984), relying on the existence of cold dark matter and dark energy (Λ) associated with a cosmological constant (Carroll 2001) in a spatially flat geometry. Predictions from this model have been found to agree with most of the observational probes such as the cosmic microwave background (CMB; e.g. Planck Collaboration 2020), the baryon acoustic oscillations (BAO; e.g. Alam et al. 2021), and the present accelerated expansion of the Hubble flow, based on the distance modulus–redshift relation (the so-called Hubble–Lemaître, or simply Hubble diagram) of type Ia supernovae (SNe Ia; e.g. Riess et al. 1998; Perlmutter et al. 1999), where a dominant dynamical contribution, dubbed dark energy (DE) and related to the cosmological constant, should drive such an acceleration. However, the fundamental physical origin and the properties of DE are still unknown, as the interpretation of Λ is plagued by a severe fine-tuning issue to obtain the right amount of DE observed today. Moreover, the data sets listed above do not fully fit the evolution of DE ranging from early to late epochs (Benetti et al. 2019; Yang et al. 2021) and do not fully rule out a spatially non-flat Universe (Park & Ratra 2019; Di Valentino, Melchiorri & Silk 2020, 2021; Handley 2021; Yang et al. 2021). The latter possibility has raised a remarkable debate about the importance of properly combining CMB data to infer significant statistical interpretations from the analysis (Efstathiou & Gratton 2020; Planck Collaboration 2020) and, by extension, the importance of combining data sets that do not reveal manifest tension (Gonzalez et al. 2021; Vagnozzi, Loeb & Moresco 2021). Deviations from the spatially flat ΛCDM model would imply important theoretical and observational consequences and a change in our current understanding of cosmic evolution (e.g. Capozziello, Benetti & Spallicci 2020). Statistically significant deviations in this directions have already been found in cosmological analyses with high-redshift probes such as Gamma-Ray Bursts (see Dainotti, Cardone & Capozziello 2008; Dainotti et al. 2011b, 2013a,b, 2015, 2017, 2020a; Dainotti, Ostrowaki & Willingale 2011a; Dainotti et al. 2020b for the standardization of these sources as cosmological candles) and quasars (QSOs) combined with SNe Ia (Risaliti & Lusso 2019; Lusso et al. 2019, 2020; Bargiacchi et al. 2021). Such a joint analysis (SNe + QSO) makes use of the observed non-linear relation between the ultraviolet and the X-ray luminosity in QSOs (e.g. Steffen et al. 2006; Just et al. 2007; Lusso et al. 2010; Lusso & Risaliti 2016; Bisogni et al. 2021; Dainotti et al. 2022) to provide an independent measurement of their distance (see e.g. Risaliti & Lusso 2015, 2019; Lusso et al. 2020, for details). The methodology is complementary to the traditional resort to type Ia SNe to estimate the cosmological parameters, yet it extends the Hubble–Lemaître diagram to a redshift range currently inaccessible to SNe ($\mathit{ z}$ = 2.4–7.5). Within a model where an evolution of the DE equation of state (EoS) in form w($\mathit{ z}$) = w0 + wa × $\mathit{ z}$/(1 + $\mathit{ z}$) is assumed, the data suggest that the DE parameter is increasing with time (Risaliti & Lusso 2019; Lusso et al. 2020). Therefore, it is compelling to further study extensions of the ΛCDM model that could produce such behaviour of DE.

2021ApJ...920..145H__Damone_et_al._2018_Instance_2

Over the past decade, many attempts to address this issue have been carried out, such as from the perspective of conventional nuclear physics and even exotic physics beyond the standard BBN framework (Angulo et al. 2005; Cyburt et al. 2008, 2016; Boyd et al. 2010; Pospelov & Pradler 2010; Fields 2011; Kirsebom & Davids 2011; Wang et al. 2011; Broggini et al. 2012; Coc et al. 2012, 2013, 2014; Cyburt & Pospelov 2012; Kang et al. 2012; Voronchev et al. 2012; Bertulani et al. 2013; Hammache et al. 2013; He et al. 2013; Kusakabe et al. 2014; Pizzone et al. 2014; Yamazaki et al. 2014; Hou et al. 2015, 2017; Famiano et al. 2016; Damone et al. 2018; Hartos et al. 2018; Luo et al. 2019; Rijal et al. 2019; Clara & Martins 2020). However, despite the fact some solutions using exotic physics have succeeded in resolving this issue, it appears there is still no universally accepted solution in the academic community since validations of these mysterious exotic physics are beyond the capabilities of current science. Conversely, it seems more worthwhile to exclude any potential possibility of resolving the 7Li discrepancy from the perspective of nuclear physics. It is known that the majority of the primordial 7Li production arises from the decay of 7Be by electron capture during the 2 months after BBN stops. Thus, for the solution of the Li problem, reactions involving 7Be could be more significant than those involving 7Li. Therefore, many reactions that potentially destroy 7Be were investigated to solve this discrepancy over past 10 yr (Kirsebom & Davids 2011; Broggini et al. 2012; Hammache et al. 2013; Hou et al. 2015; Hartos et al. 2018). Meanwhile, enormous efforts have been made to refine the reaction rates of key BBN reactions in the past 20 yr (Smith et al. 1993; Descouvemont et al. 2004; Serpico et al. 2004; Cyburt & Davids 2008; Neff 2011; Pizzone et al. 2014; Tumino et al. 2014; Hou et al. 2015; Barbagallo et al. 2016; Iliadis et al. 2016; Kawabata et al. 2017; Lamia et al. 2017, 2019; Damone et al. 2018; Rijal et al. 2019; Mossa et al. 2020), but the probability of solving or alleviating the 7Li problem by improving our knowledge of relevant nuclear reaction rates still cannot be eliminated. Recent experiments for key nuclear reactions like 7Be(n,p)7Li and 7Be(d,p)24He allow for a reduction of the 7Li production by about 12% (Damone et al. 2018; Rijal et al. 2019) compared to previous calculations. At present, nuclear uncertainties cannot rule out that some of the reactions destroying 7Li are indeed more efficient than those currently used (Boyd et al. 2010; Chakraborty et al. 2011).

2017MNRAS.469S.238L__Massironi_et_al._2015_Instance_1

ESA's Rosetta spacecraft orbited closely around comet 67P/Churyumov–Gerasimenko (hereafter 67P) during its 2 yr mission. From 2014 August to 2016 September, the camera system Optical, Spectroscopic, and Infrared Remote Imaging System (OSIRIS; Keller et al. 2007) onboard Rosetta observed the comet nucleus down to few centimetres per pixel providing detailed images of the comet's surface and activity. The study of 67P morphology has been performed with unprecedented detail thanks to the OSIRIS image spatial coverage and resolution. A first OSIRIS analysis on the nucleus structure, morphology and jets activity is reported in Sierks et al. (2015) and Thomas et al. (2015). The comet's surface revealed a wide diversity in morphology such as layering (Massironi et al. 2015), pits (Vincent et al. 2015), boulders and fractures (Pajola et al. 2015, 2016c; El-Maarry et al. 2015a), and high reflectivity boulders (Pommerol et al. 2015). During the entire mission, surface changes have been detected thanks to images acquired before and after perihelion passage (Groussin et al. 2015; Pajola 2017; El-Maarry 2017). The global spectrophotometric properties of the comet were investigated in detail by Fornasier et al. (2015) identifying three different groups of terrains depending on spectral slope values. In addition to a global characterization of the comet, OSIRIS allowed a detailed analysis of specific regions identified on the comet by means of high resolution and multifilter images (La Forgia et al. 2015; Deshapriya et al. 2016; Lucchetti et al. 2016; Oklay et al. 2016a; Pajola et al. 2016d; Oklay et al. 2017). We decided to follow the approach of characterizing at the highest resolution possible specific regions of interest (ROIs) on 67P. We therefore performed a detailed analysis on an area belonging to the Seth region (El-Maarry et al. 2015b) that is characterized by flat-floored and steep-walled circular depressions (El-Maarry et al. 2015b; Giacomini et al. 2016). We focused our attention on the circular niches of the Seth area (Fig. 1) and performed a multidisciplinary study of these structures investigating their geomorphological and spectrophotometric properties. In addition, thanks to images acquired pre- and post-perihelion, we conducted a comparative analysis to find if this area has been subjected to surface changes. This can be useful to provide constraints about Seth's niches properties as well as their possible origin being one of the interesting features located on the surface of 67P. Specifically, the circular niches deposits can be considered the result of landslide events that occurred on the comet surface, as the recent Aswan cliff collapse (Pajola 2017), where it has been reported the occurring of falling material from the adjacent cliff after the Rosetta perihelion passage. Hence, with this work we plan to understand if the formation of these Seth's circular niches occurred recently or if it is correlated to older events that have shaped the comet's surface.

2018AandA...616A..34H__Mohamed_&_Podsiadlowski_(2012)_Instance_2

The CO emission, tracking the bulk of the gas, reveals an almost face-on one-armed spiral, of which almost two full windings can be traced. What could be the origin of this spiral structure? As the majority of AGB stars are in binary systems, and perhaps all host planets, interaction between the outflow and a sufficiently massive and nearby companion may be the explanation of the observed CO morphology. The intricate emission features in the inner 2″ of the central CO emission maps is strongly reminiscent of hydrodynamical simulations of wind–binary interaction by Mastrodemos & Morris (1998) and Mohamed & Podsiadlowski (2012), where the latter authors performed tailored simulations for the Mira AB system in which the outflow of the AGB star Mira A is perturbed by the presence of its close companion Mira B. The wind–binary interaction that ensues leads to what is known as wind Roche-lobe overflow (WRLOF), where the slow AGB wind is confined to the star’s Roche lobe, while overflowing through the L1 Lagrange point. Gravitational interaction of the overflowing material with the companion produces an intricate feedback system where the stellar outflow material is ejected into the surrounding CSE through two distinct streams (through L2 and the stagnation point3) which combine to form an annular stream. As this stream travels outwards, it creates the larger scale spiral observed in the wind. The morphology resulting from this particular type of wind–binary interaction is shown in Fig. 3 in Mohamed & Podsiadlowski (2012). In Fig. 11 we show the emission pattern seen in the central regions of the CO channel at υ*. We compare this image with the bottom left panel of Fig. 3 in Mohamed & Podsiadlowski (2012), an opacity map of the interaction zone. Though the two properties that are compared differ in nature, they likely still trace the same global morphological structure. Indeed, several of the predicted morphological features can be identified in the data of EP Aqr. The bright central region with a north and southward hook-like extension are strikingly similar, as are the eastern and western crescent-shaped “voids”, the overall shape, and the morphological properties of the small-scale instabilities.

2020ApJ...898....4C__Shipp_et_al._2018_Instance_1

Detecting the halo response to the LMC-induced DM wake would be an exciting advancement in testing our assumptions about the properties of DM, as well as providing key constraints on the potential of the MW and the mass and orbital history of the LMC. However, the GC19 simulations give predictions for the response in the context of smooth MW DM and stellar halos. In reality, the MW stellar halo contains a wealth of substructure that is not yet phase mixed, in the form of stellar streams (e.g., Odenkirchen et al. 2001; Newberg et al. 2002; Belokurov et al. 2006; Grillmair 2006; Shipp et al. 2018; also see Newberg & Carlin 2016 for a recent review) and stellar clouds (e.g., Newberg et al. 2002; Rocha-Pinto et al. 2004; Jurić et al. 2008; Li et al. 2016). In addition, using a sample of MW halo main-sequence turnoff stars from the HALO7D survey (Cunningham et al. 2019a), Cunningham et al. (2019b) observed that the estimated parameters of the velocity ellipsoid (i.e., ) were different in the different survey fields; these differing estimates could be interpreted as evidence that the halo is not phase mixed over the survey range ( kpc). They also showed maps of the halo velocity anisotropy β in two halos from the Latte suite of FIRE-2 simulations (introduced in Wetzel et al. 2016), finding that the anisotropy can vary over the range across the sky. Some of the variation in the β estimates appeared to correlate with stellar overdensities in the halos, indicating that galactic substructure is at least in part responsible for the different velocity distributions. While some substructure in the halo can be clearly identified as overdensities in phase space and removed from analysis, the presence of velocity substructure in the halo could complicate attempts to detect signatures of the LMC-induced DM wake. For example, Belokurov et al. (2019) recently argued that the Pisces Overdensity (Sesar et al. 2007; Watkins et al. 2009; Nie et al. 2015) might be stars in the wake trailing the LMC in its orbit, because of their net negative radial velocities. However, it remains difficult to conclusively argue this scenario given that these stars could also be in Galactic substructure (or, perhaps, stars that are in substructure and have been perturbed by the DM wake).

2019ApJ...883...53T__Armano_et_al._2016_Instance_1

LISA Pathfinder (LPF; Antonucci et al. 2011), a European Space Agency (ESA) mission that operated near the first Sun–Earth Lagrange point (L1) from 2016 January through 2017 July, is in an ideal orbit to make such measurements. However, LPF flew no instrumentation dedicated to micrometeoroid or dust detection. LPF’s primary objective was to demonstrate technologies for a future space-based observatory of millihertz-band gravitational waves. The key achievement of LPF was placing two gold-platinum cubes known as “test masses” into a freefall so pure that it was characterized by accelerations at the femto-g level (e.g., Armano et al. 2016, 2018b), the level required to detect the minute disturbances caused by passing gravitational waves. In order to reach this level of performance, the test masses were released into cavities inside the spacecraft and a control system was employed to keep the spacecraft centered on the test masses. This control system was designed to counteract disturbances on the spacecraft, including those caused by impacts from micrometeoroids. Shortly before LPF’s launch, it was realized that data from the control system, if properly calibrated, could be used to detect and characterize these impacts and infer information about the impacting particles (e.g., Thorpe et al. 2016). While such impact events have been reported by other spacecraft, LPF’s unique instrumentation makes it sensitive to much smaller and much more numerous impacts and allows the impact geometry to be more fully constrained. Early results from the first few months of LPF operations suggested that such events could indeed be identified and were roughly consistent with the pre-launch predictions of their effect on the control system (e.g., Thorpe et al. 2017). In this paper we present results from the first systematic search for micrometeoroid impacts in the LPF data set. Our data set consists of 4348 hr of data in both the nominal LPF configuration and the “Disturbance Reduction System” (DRS) configuration, in which a NASA-supplied controller and thruster system took over control of the spacecraft (Anderson et al. 2018). Our data set corresponds to the times when LPF was operating in a “quiet” mode, without any intentional signal injections or other disturbances. During this period, we have identified 54 impact candidates using our detection pipeline and manual vetoing. We have characterized the properties of this data set and compared it to several theoretical models for the underlying dust population.

2020MNRAS.498.6013A__Magaña_et_al._2015_Instance_1

On the other hand, observational data are used to test these models. Among the most frequently used are the cosmic microwave background radiation (CMB; Planck Collaboration XIII 2016; Aghanim et al. 2018), baryonic acoustic oscillations (BAO; Eisenstein et al. 2005; Blake et al. 2012; Alam et al. 2017; Bautista et al. 2017), Type Ia supernovae (SNe Ia; Scolnic et al. 2018), and observational Hubble data (OHD; Jimenez & Loeb 2002; Moresco et al. 2016; Magaña et al. 2018). Consistency in the cosmological parameters among different techniques, rather than more accurate measurements, is desirable to better understand the nature of DE. In the last years, several efforts have been made by the community to include gravitational lens systems in the study of the Universe’s evolution. Some of the pioneers are Futamase & Yoshida (2001) and Biesiada (2006), who used only one strong-lens system to study some of the most popular cosmological models. Grillo, Lombardi & Bertin (2008) introduced a methodology to estimate cosmological parameters using strong-lensing systems (SLS; see also Jullo et al. 2010; Magaña et al. 2015, 2018). They apply the relation between the Einstein radius and the central stellar velocity dispersion, assuming an isothermal profile for the total density distribution of the lens (elliptical) galaxy. Their simulations found that the method is accurate enough to obtain information about the underlying cosmology. They concluded that the stellar velocity dispersion and velocity dispersion of the isothermal lens model are very similar in the w cold dark matter (wCDM) model. Biesiada, Piórkowska & Malec (2010) used the same procedure comparing a distance ratio, Dobs, constructed from SLS observations such as the Einstein radius and spectroscopic velocity dispersion of the lens galaxy, with a theoretical counterpart, Dth. By using a sample containing 20 SLS, they demonstrated that this technique is useful to provide insights into DE. Cao et al. (2012) updated the sample to 80 systems and proposed a modification that takes into account deviations from sphericity, i.e. from the singular isothermal sphere (SIS). Later on, Cao et al. (2015) considered lens profile deviations due to the redshift evolution of elliptical galaxies by using spherically symmetric power-law mass distributions for the lenses and also increased the compilation up to 118 points. They also explore the consequences of using aperture-corrected velocity dispersions on the parameter estimations. Some authors have pointed out the need for a sufficiently large sample to test DE models with higher precision (Yennapureddy & Melia 2018). For instance, Melia, Wei & Wu (2015) have emphasized that a sample of ∼200 SLS can discern the Rh = ct model from the standard one. Qi et al. (2018) simulated strong lensing data to constrain the curvature of the Universe and found that, by increasing the sample (16000 lenses) and combining with compact radio quasars, it could be constrained with an accuracy of ∼10−3. Recently, Leaf & Melia (2018) have revisited this cosmological tool with the largest sample of SLS (158) until now, including 40 new systems presented by Shu et al. (2017). The authors proposed a new approach to improve this technique by introducing in the observational distance ratio error (δDobs), a parameter σx to take into account the SIE scatter and any other source of errors in the measurements. In their analysis, they excluded 29 SLS that are outside the region 0 Dobs 1, and the system SL2SJ085019−034710 (Sonnenfeld et al. 2013b), which seems to be an extreme outlier for their models. Their results show that a $\sigma _x = 12.2{{\ \rm per\ cent}}$ provides more statistically significative cosmological constraints. Finally, Chen, Li & Shu (2018) used 157 SLS to analyse the Lambda cold dark matter (ΛCDM) model. They considered a lens mass distribution ρ(r) = ρ0r−γ and three possibilities for the γ parameter: a constant value, a dependence with the lens redshift (zl), and a dependence with both the surface mass density and the lens redshift. They concluded that although Ω0m, used as the only free parameter in ΛCDM scenario, is very sensitive to the lens mass model, it provides weak constraints that are also in tension with Planck measurements.

2018ApJ...856...51R__Reale_2014_Instance_2

Close to the end of their formation, stars are surrounded by a gas and dust disk, from which planets form. Magnetic fields are known to play a key role in the star-disk system (Johns-Krull 2014). It is believed that the inner regions of the disk are significantly ionized by the stellar radiation and that accreting material flows along magnetic channels that connect the disk to the star (Koenigl 1991). Very long and intense X-ray flares in star-forming regions might occur in such long channels (Favata et al. 2005), but this is highly debated (Getman et al. 2008). These flux tubes might resemble those observed in the solar corona and diagnosed in the stellar coronae, but on a much larger scale. On the Sun we see the so-called coronal loops on the scale of several thousand kilometers in active regions, but some faint large-scale structures can extend up to ∼1 R⊙ (Reale 2014). Most solar flares occur in active region loops, but the long-lasting ones can involve more and more loops aligned in arcades. The other stars are so distant that we cannot resolve the flaring regions, but it is supposed that they occur in loops and even in arcades. Whereas the duration of solar flares typically ranges from a few minutes to several hours, stellar flares can be very intense, more than the solar bolometric luminosity, and long-lasting, including longer than one day, in very active stars. Several such gigantic coronal flares have been surveyed in star-forming regions (Favata et al. 2005) and where they occur is a big question. Magnetic instabilities in flux tubes were proposed to be the origin of the flaring activity also in T Tauri stars (Birk 1998; Birk et al. 2000), and long-lasting stellar flares might be expected to involve loop arcades (Getman et al. 2008), like those on the Sun. In long-lasting solar flares, the duration is mainly due to the progressive involvement of more and similar loops, therefore duration is not directly linked to the size of the flaring structures. This might also be the case for giant stellar flares. However, if a single stellar loop were flaring, the cooling time of the confined plasma would be proportional to the loop length (Serio et al. 1991; Reale 2014), and day-long flares would correspond to giant loops, as long as they possibly connected the star with the disk (Hartmann et al. 2016). There are ways to distinguish between a pure cooling in a single loop and a decay only due to progressive reduction of the energy release in a loop arcade (Reale et al. 1997), but the explanations are contested and the uncertainties are large (Getman et al. 2008). Several studies (Favata et al. 2005; Giardino et al. 2007) find results compatible with long magnetic channels in pre-main sequence (PMS) stars, but the derivation of the loop length is based on the assumption of a flare occurring in a single loop (Reale 2007).

2018ApJ...856..144M__McIntosh_et_al._2011_Instance_1

The solar corona is still enigmatic from a scientific point of view, with important unsolved questions about its nature, such as solar wind acceleration and coronal heating (McComas et al. 2007; Parnell & De Moortel 2012). Although finding solutions to these questions is important on its own, it is also expected to have major implications for related fields, such as space weather (Singh et al. 2010), which is set to grow in importance as we make advances in technology and space exploration. One impediment toward solving coronal mysteries is the notorious difficulty in determining its key physical parameters, such as the magnetic field, by direct spectroscopic or polarimetric measurements. Early observational evidence of waves in coronal structures using SOHO/TRACE (Aschwanden et al. 1999; Berghmans & Clette 1999) paved the way for the previously theorized coronal seismology (Uchida 1970; Roberts et al. 1984) to be a tool for coronal plasma diagnostics. The first attempt to seismologically determine the magnetic field of transversely oscillating coronal loops was applied to the first such event observed by Nakariakov & Ofman (2001). Since then, coronal seismology has successfully been applied to numerous oscillation events (for reviews, see, e.g., De Moortel 2005; De Moortel & Nakariakov 2012; Stepanov et al. 2012). A common feature of all previously diagnosed events is their localization in time, i.e., single events. This obviously limits the applicability of seismology to the brief duration of the oscillation event. Moreover, these single-oscillation events tend to be rare, as they are mostly related to flaring events or eruptions, meaning that seismology is restricted to brief diagnostics highly localized in time, or in time and space for non-global oscillations. However, the discovery of ubiquitous propagating transverse oscillations with CoMP (Tomczyk et al. 2007; Tomczyk & McIntosh 2009) and SDO (McIntosh et al. 2011) or the more recently identified nearly ubiquitous, decay-less low-amplitude kink coronal loop oscillations (Anfinogentov et al. 2013, 2015; Nisticò et al. 2013), as well as oscillations in plumes (Thurgood et al. 2014), led to the possibility of continuous diagnostics in time, i.e., dynamic coronal seismology. Consequently, very recently, the first seismologic “magnetic field image” was obtained, based on the ubiquitous transverse waves (Long et al. 2017), using a methodology put forth by Morton et al. (2015). In this study, the authors use the magnetohydrodynamic (MHD) kink phase speed of a flux tube (Edwin & Roberts 1983) as the inversion tool for the magnetic field. These ubiquitous propagating transverse waves are now widely regarded as Alfvénic waves (Goossens et al. 2012), although this reinterpretation of the nature of the waves does not modify the phase speed formula used in the inversion. However, it is still assumed that the observed ubiquitous waves are transverse oscillations of flux tubes, and while the fine structure in the corona is still unknown (Peter et al. 2013; Reale 2014; Aschwanden & Peter 2017), it is unlikely that the magnetic cylinder model is satisfactory. Structuring across the magnetic field, among other factors (De Moortel & Pascoe 2009; Pascoe & De Moortel 2014), is an important detail for seismology, as it can greatly influence the nature and propagation of MHD waves (Luna et al. 2010; Terradas et al. 2010; Verth et al. 2010), altering the dependence of the observed phase speed on physical properties such as the magnetic field or mass density (e.g., Verth et al. 2007; Arregui et al. 2013), which might lead to erroneous inversions if not considered. Some light was shed recently on the weaknesses of modeling the corona as a bundle of independent thin magnetic strands by Magyar & Van Doorsselaere (2016). In their simulations, a loop consisting of packed strands is quickly deformed and mixed when disturbed by propagating transverse waves, leading to a turbulent cross-section (Magyar et al. 2017). This result reiterates the above-mentioned need to move away from rigid cylindrical models in favor of more realistic descriptions that account for nonlinearities.

2018MNRAS.478.2541F__Bertin_&_Arnouts_1996_Instance_1

The distance between the peak of the main source and the transient source (blue POSS I) is ≈6 pixels or ≈6 arcsec (≈0.3 kpc) towards the North, at an angle of ≈− 10° (Fig. 2; left-hand column; rows 4–5). From the PSF characteristics of the blue POSS I and blue subtracted POSS images (Section 2.1.1), the transient source is slightly resolved ( 2 × PSF FWHM). Because of the faintness of the transient and its proximity to the larger, brighter main source, an automated detection algorithm such as SExtractor (Bertin & Arnouts 1996), implemented in GAIA36 2016A, was unsuccessful in deblending the two sources. Therefore, a more rudimentary method was employed to determine the magnitude of the transient: using ds9, the transient and the brightest North-western source (Sections 2.2 and 2.3; Fig. 2; left-hand column; rows 4–5; Fig. 3) were fitted with ellipses to perform relative aperture photometry on the photometrically calibrated unconvolved POSS images, after sky subtraction (see below). This procedure shows that the transient source corresponds to ≳96 per cent of the flux of the North-western source (m ≈ 20.9 mag; USNO B1.0; Monet et al. 2003), which is equivalent to the transient being Δm ≲ 0.05 mag fainter than the North-western source (Fig. 2; left-hand column; rows 4–5). Similarly, the flux ratio between the main source and the transient is approximately a factor of 6 (Section 2.1.4). Given this very simplistic procedure to obtain the transient photometry, the magnitude of the transient is quite uncertain. A magnitude of m ≈ 21.0±0.2 mag will be adopted in the following discussion (Section 3), with the confidence interval provided assuming a 20 per cent error in the flux (detection at a 5σ level; see below), corresponding to the estimated photometric error at the maximum intensity extended to the full source, and neglecting unquantifiable systematics errors; the error estimation is included in AppendixA. Favourably, changes in the magnitude as large as Δm ≈ 1.5 will not significantly alter the conclusions of this work (Section 3).

2020MNRAS.498.4906B__Stinson_et_al._2013_Instance_1

Throughout their short lifetimes, high-mass stars (>8 M⊙) inject large amounts of energy and momentum into their host environments through a variety of feedback processes (e.g. Krumholz et al. 2014). The most potentially disruptive of these feedback mechanisms occurs when the stars eventually die, exploding as supernovae (SNe). Indeed, SNe are thought to play a major role in the self-regulation of star formation in galaxies through their contribution to the total energy and momentum budget of the interstellar medium (ISM; McKee & Ostriker 1977; Mac Low & Klessen 2004; Klessen & Glover 2016). As the rate of cooling in the ISM is proportional to the gas density squared, the efficiency with which SNe inject energy and momentum into the local galactic environment strongly depends on the density distribution of the gas into which they explode (see Girichidis et al. 2016 and references therein). For example, SNe that explode within dense molecular clouds may be limited to disrupting their natal gas clouds, whilst SNe that explode into lower density environments can drive hot expanding bubbles to much larger distances (tens to hundreds of pc) and influence galactic scale processes (e.g. kpc-scale galactic outflows; Veilleux, Cecil & Bland-Hawthorn 2005; Agertz et al. 2013; Stinson et al. 2013; Keller, Kruijssen & Wadsley 2020; Veilleux et al. 2020). Feedback from the pre-SNe stages of high-mass stars plays a significant role in determining the environment into which SNe subsequently explode. Simulations have long predicted that this ‘pre-processing’ can potentially even destroy the host molecular cloud before the first SN explosion (e.g. Dale, Ercolano & Bonnell 2012, 2013), and observations of molecular clouds and H ii regions in nearby galaxies now show that pre-SN feedback is primarily responsible for the destruction of molecular clouds across the local galaxy population (Kruijssen et al. 2019b; Chevance et al. 2020b,c). Studying the effects of these earliest stages of stellar feedback on their environment is then crucial to quantifying the contribution of SNe in driving the galaxy-scale energy and momentum cycle of the ISM in galaxies. In light of this, significant observational effort has been invested to better disentangle and quantify the effect of various feedback mechanisms within young stellar systems (e.g. Oey 1996a,b; Pellegrini, Baldwin & Ferland 2010, 2011). More recent efforts have focused on measuring and comparing the internal pressure components from different feedback mechanisms in H ii regions located within the Small and Large Magellanic Clouds (SMC and LMC, respectively), such as the well-known 30 Doradus complex (e.g. Lopez et al. 2011, 2014; Chevance et al. 2016; McLeod et al. 2019), as well as other nearby galaxies (e.g. McLeod et al. 2020). These studies have provided important insights into early-stage feedback, but further work is needed to understand how pre-processing varies with environment, particularly to higher density, pressure, and metallicity regimes such as those in galactic nuclei and high-redshift galaxies.

2016AandA...592A..74S__Roming_et_al._2005_Instance_1

We observed our full sample of all 24 candidate highly variable AGN with Swift (Gehrels et al. 2004) for ~2 ks each, between 2010 and 2014 as part of a fill-in programme. All XRT (Burrows et al. 2005) observations were made in photon counting mode with exposure times ranging from 1.6–3.7 ks. The Swift-XRT data were obtained from the UK Swift Science Data Centre1 and reduced following the procedures of Evans et al. (2009) using the Swift software and calibration database available within HEASOFT v.6.12. Simultaneous observations were made with the Swift BAT (Barthelmy et al. 2005) at 14–195 keV and the Swift UV/Optical Telescope (UVOT; Roming et al. 2005) with the u filter applied. For ten sources, additional archival Swift observations were available at the time of writing which we have included and analysed in an identical manner. Details of all the observations used in this paper are given in the appendix (see Table A.1). With the XRT we detected 16 (or two-thirds) of the sample sources in our fill-in observations. Widening our search, we looked at data stacks in the Swift XRT Point Source Catalogue (1SXPS; Evans et al. 2014) and other pointed XRT observations and found that a further five sources were detected. Two of the three XRT non-detected sources have only ever been detected in the XMM slew survey (Figs. 1b and f), and cannot be identified with any source detected in other wavelength surveys such as 2MASS, WISE, SDSS or 6dF in our searches. One of these, XMMSL1 J015510.9-140028, lies at the detection threshold of the slew survey. The other, XMMSL1 J113001.8+020007, has a higher significance, however the photons at the source location are aligned along a row which indicates that this might not be an astrophysical point source. We conclude therefore that those two detections in the slew survey are highly likely to be spurious. We discuss the spurious fraction further in Sect. 8. The remaining XRT-undetected source is XMMSL1 J193439.3+490922, which has three detections in XMM slews and is hence likely real. All XRT-detected sources are also detected with UVOT. Three sources (Mrk 352, ESO 362-G018, ESO 139-G012) can be found in the Swift BAT 70-month All-Sky Hard X-ray Survey Source Catalog (Baumgartner et al. 2013).

2019AandA...625A..12G__Wunderlich_et_al._2019_Instance_1

To calculate the boundaries of the HZ of a stagnant-lid Earth around M, K, G, and F dwarfs, we applied a 1D, cloud-free, radiative-convective climate model, which has been described in detail by von Paris et al. (2010) and von Paris et al. (2015), and is based on Kasting et al. (1984) and Segura et al. (2003). The radiative transfer is split into a stellar and a thermal wavelength regime. The short wavelength regime treats the absorption and scattering of stellar irradiation using a δ-two-stream method including Rayleigh scattering coefficients following the approach of Allen (1973) and four-term correlated-k exponential sums covering a wavelength regime from 273.5 nm to 4.545 μm. This wavelength coverage is optimized for solar irradiation. Especially for late M dwarfs the cut-off at 4.545 μm leads to non-negligible loss in incoming radiation of up to ≈5% (see also Wunderlich et al. 2019). Hence, HZ boundaries obtained with the models lie closer to the star than would be expected when accounting for this missing portion of irradiation. The long-wavelength regime treats the absorption by CO2 and H2O in the wavelength regime from 1 to 500 μm via correlated-ks computed based on HITEMP 1995 (Rothman et al. 1995). The ckd continuum (Clough et al. 1989), and the collision-induced absorption as described in Kasting et al. (1984) for CO2 and as described in von Paris et al. (2013) for N2 –N2 are included. Convection is treated by applying a convective adjustment when the adiabatic lapse exceeds the radiative lapse rate, including latent heat release from H2O or CO2 where applicable. The water mixing ratio profile ( $C_{\mathrm{H}_2\mathrm{O}}$ C H 2 O ) is calculated from the temperature profile, the saturation vapour pressure (psat), and by assuming a relative humidity (RH): $C_{\mathrm{H}_2\mathrm{O}}=\textrm{RH}\frac{p_{\mathrm{sat}}}{p}$ C H 2 O =RH p sat p , with p the pressure of the atmosphere. By making use of our 1D climate model we estimate global diurnal mean values without accounting for effects such as slow planetary rotation or an interactive hydrological cycle. A discussion on the potential influence of 3D processes is given in Sect. 4.

2016ApJ...833..216G___2010_Instance_1

SEP events with gigaelectronvolt particles are generally rare. Typically about a dozen events occur during each solar cycle, although only two GLEs were reported in cycle 24, probably due to the change in the state of the heliosphere (Gopalswamy et al. 2013a, 2014a; Thakur et al. 2014). It appears that the 2012 July 23 event would have been another GLE event if it had occurred on the front side of the Sun. The purpose of this paper is to examine the event from the perspectives of CME kinematics, SEP intensity and spectrum, and radio-burst association to see if the 2012 July 23 event can be considered as an extreme particle event. The reason for considering these properties is clear from the following facts. Particles up to gigaelectronvolt energies are accelerated by strong shocks driven by CMEs of very high speeds (∼2000 km s−1) and intense, soft X-ray flares (see Gopalswamy et al. 2010, 2012b). The high speed is typically attained very close to the Sun, so the density and magnetic field in the corona are high for efficient particle acceleration (e.g., Mewaldt et al. 2012; Gopalswamy et al. 2014a). The high CME speed implies that a fast-mode MHD shock forms close to the Sun, as indicated by the onset of metric type II radio bursts, typically at heights 1.5 solar radii (Rs). CMEs attaining high speeds near the Sun have to accelerate impulsively, so these events are characterized by high initial acceleration (∼2 km s−2, see Gopalswamy et al. 2012b). This is in contrast to slowly accelerating CMEs (from filament regions outside active regions) that form shocks at large distances from the Sun and do not accelerate particles to energies more than a few tens of megaelectronvolts (Gopalswamy et al. 2015a, 2015d). Accordingly, the SEP spectra of such events are very soft, as opposed to the hard spectra of GLE events. Whether an event has a soft or hard spectrum is important information because the hard-spectrum events have stronger space weather impacts (see, e.g., Reames 2013). SEP events with gigaelectronvolt components are accompanied by type II radio bursts from meter (m) wavelengths to kilometer (km) wavelengths (Gopalswamy et al. 2005b, 2010). Type II bursts occurring at such wide-ranging wavelengths imply strong shocks throughout the inner heliosphere (Gopalswamy et al. 2005a).

2017MNRAS.470..755H__Toomre_&_Toomre_1972_Instance_1

Supermassive black holes (SMBHs) are believed to exist in the centres of all massive galaxies (Kormendy & Richstone 1995). A small proportion of these are growing, with gas accretion rates ranging from ∼10−4 to 10 M⊙ yr−1 and a proportionately wide range of bolometric luminosities (∼1042–1047 erg s−1). These are active galactic nuclei (AGNs) and may accrete large fractions of their mass in bursts of rapid accretion (Croton et al. 2006), requiring rapid inflow of gas from galaxy length-scales. Stripping the gas of enough angular momentum to allow for such rapid accretion, thereby powering the most luminous AGN, proves extremely challenging. Theoretical work suggests major mergers can provide the torque to displace such an overwhelming fraction of the angular momentum of the gas, allowing for the highest accretion rates on to the central black hole whilst transforming the galaxy morphology (Toomre & Toomre 1972; Barnes 1988; BarnesBarnes & Hernquist 1991; Di Matteo, Springel & Hernquist 2005; Cox et al. 2008). Gas rich mergers may trigger nuclear and global starbursts (Mihos & Hernquist 1994, 1996; Hopkins et al. 2006) and major mergers disrupt the morphologies of the colliding galaxies, often exhibiting long tidal tails or shells of expelled gas and stars soon after the merger has begun. Detecting this can be challenging however, since the single new galaxy has a relaxation time-scale after which morphological features of mergers fade (Tinsley 1978; Kennicutt et al. 1987; Ellison et al. 2013). Observational evidence suggesting a link between major mergers and SMBH accretion has been mixed (e.g. Gabor et al. 2009; Cisternas et al. 2011; Schawinski et al. 2011; Kocevski et al. 2012; Treister et al. 2012; Ellison et al. 2013; Villforth et al. 2014; Kocevski et al. 2015; Villforth et al. 2017). Alternatively, AGNs may be triggered secularly through, for example, disc instabilities (Bournaud et al. 2011), bars (Knapen, Shlosman & Peletier 2000; Oh, Oh & Yi 2012) or otherwise by minor mergers (Kaviraj 2013). It remains unclear whether alternatives to major merger triggering can drive several M⊙ yr−1 of gas to the central SMBHs, as is necessary to power the most luminous AGN.

2021ApJ...909..173B__Bandiera_&_Petruk_2016_Instance_1

Taking advantage of modern computer architecture, numerical simulations have become a powerful tool for investigating SNR dynamical evolution by means of testing various scenarios with specific purposes (Ferrand 2020). As relevant observational evidence is steadily growing, particular attention has been devoted to tackling with SNR temporal evolution in an inhomogeneous medium, aiming at deciphering the observed asymmetries of emission morphology or clarifying the effects on the synthetic radio polarization maps exerted by various configurations of the interstellar magnetic field (e.g., Orlando et al. 2007; Schneiter et al. 2015; Yang et al. 2015; Bandiera & Petruk 2016; Petruk et al. 2017). As a first attempt, magnetohydrodynamic (MHD) simulations of a benchmark type Ia SNR expanding into a turbulent background were carried out by Balsara et al. (2001), and several obtained results are different from those in a uniform medium: significant azimuthal variations observed in density and magnetic field profiles, a patchy and time-independent synchrotron shell which may in turn serve as a probe into the nature of ISM turbulence, and amplification of the magnetic field in the post-shock region due to interactions between the forward shock and the turbulent background. Under the assumption that both density and magnetic field fluctuations follow a Kolmogorov-like power spectrum, the temporal evolution of an SNR propagating into a turbulent medium was investigated by Guo et al. (2012), with a focus on the structures and amplification of the magnetic field in the shock downstream. Recently, two-dimensional cylindrical MHD simulations were implemented to investigate the dynamical properties of young type Ia SNRs undergoing shock acceleration in a turbulent medium by Peng et al. (2020), where an initial power-law density profile is adopted and the derived relative contact discontinuity positions are compared with the observed results of two typical type Ia SNRs: SN 1006 and Tycho.

2020AandA...639A.116Y__Wenger_et_al._2000_Instance_1

The main RSG sample contains 1405 candidates from the SMC source catalog. However, targets with Rank 4 and 5 are selected only in one CMD by either the MIST models or the theoretical J − KS color cuts, and many of them reach down close to the tip of the red giant branch (TRGB) and AGB population (see also Figs. 13 and 14 of Yang et al. 2019). To be on the safe side we adopted only targets with ranks from 0 to 3 (targets identified in at least two CMDs) as our initial sample, which resulted in 1107 targets. Due to the photometric quality cuts and uncertainties of the Spitzer and Gaia data, the strict constraints on the astrometric solution, and the deblending applied during the construction of the source catalog, some of the spectroscopically confirmed RSGs were also rejected. In order to make the sample as complete as possible, we retrieved and added all known spectroscopic RSGs in both optical and mid-infrared (MIR) bands from Simbad (Wenger et al. 2000) and data taken by Spitzer Infrared Spectrograph (IRS; Houck et al. 2004), respectively. From Simbad, we selected 322 RSGs with RV ≥ 90 km s−1, spectral type later than G0, and luminosity class brighter than II by using criteria query (Levesque 2013; González-Fernández et al. 2015), for which 192 targets were matched with our initial sample within 1″. Additionally, a crossmatching with the main RSG sample of 1405 candidates indicated that three Rank 4 candidates were also matched within 1″. Surprisingly, there are two spectroscopic RSGs matched with the source catalog within 1″, but not selected as the RSG candidates by either the MIST models or the theoretical J − KS color cuts. Visual inspection of Gaia and 2MASS (Two Micron All Sky Survey; Skrutskie et al. 2006) CMDs (shown below) indicated that these two targets were slightly off the blue and red boundaries of the RSG region, respectively, which was likely due to the intrinsic variability of the RSGs (Kiss et al. 2006; Yang & Jiang 2011; Ren et al. 2019). Consequently, in total, there are 127 unselected spectroscopic RSGs from Simbad. For data taken by Spitzer/IRS, there were 22 RSGs from Ruffle et al. (2015), who classified 209 point sources observed by Spitzer/IRS using a decision tree method, based on IR spectral features, continuum and spectral energy distribution shape, bolometric luminosity, cluster membership, and variability information (all the targets from Kraemer et al. 2017 were also included). Of these 22 RSGs, 16 of them were matched with our initial sample within 1″, and 4 of them were matched with the previous unselected Simbad RSGs within 1″. Thus, there are only two unselected spectroscopic RSGs from Spitzer/IRS. In total, there are additional 129 spectroscopic RSGs from both Simbad and Spitzer/IRS, for which we give them Rank −1.

2018AandA...619A..13V__Saviane_et_al._2012_Instance_5

The EWs were measured with the methods described in Vásquez et al. (2015). As in Paper I, we used the sum of the EWs of the two strongest CaT lines (λ8542, λ8662) as a metallicity estimator, following the Ca II triplet method of Armandroff & Da Costa (1991). Different functions have been tested in the literature to measure the EWs of the CaT lines, depending on the metallicity regime. For metal-poor stars ([Fe/H] ≲ −0.7 dex) a Gaussian function was used with excellent results (Armandroff & Da Costa 1991), while a more general function (such as a Moffat function or the sum of a Gaussian and Lorentzian, G + L) is needed to fit the strong wings of the CaT lines observed in metal-rich stars (Rutledge et al. 1997; Cole et al. 2004). Following our previous work (Gullieuszik et al. 2009; Saviane et al. 2012) we have adopted here a G+L profile fit. To measure the EWs, each spectrum was normalised with a low-order polynomial using the IRAF continuum task, and set to the rest frame by correcting for the observed radial velocity. The two strongest CaT lines were fitted by a G+L profile using a non-linear least squares fitting routine part of the R programming language. Five clusters from the sample of Saviane et al. (2012) covering a wide metallicity range were re-reduced and analysed with our code to ensure that our EWs measurements are on the same scale as the template clusters used to define the metallicity calibration. Figure 5 shows the comparison between our EWs measurements and the line strengths measured by Saviane et al. (2012) (in both cases the sum of the two strongest lines) for the five calibration clusters. The observed scatter is consistent with the internal errors of the EW measurements, computed as in Vásquez et al. (2015). The measurements show a small deviation from the unity relation, which is more evident at low metallicity. A linear fit to this trend gives the relation: ΣEW(S12) = 0.97 ΣEW(this work) + 0.21, with an rms about the fit of 0.13 Å. This fit is shown in Fig. 5 as a dashed black line. For internal consistency, all EWs in this work have been adjusted to the measurement scale of Saviane et al. (2012) by using this relation. In Table 3 we provide the coordinates, radial velocities, and the sum of the equivalent widths for the cluster member stars, both measured (“m”) and corrected (“c”) to the system of Saviane et al. 2012.

2015ApJ...809..117Y__Hayasaki_et_al._2008_Instance_1

Considering a BBH system resulting from a gas rich merger, the BBH is probably surrounded by a circumbinary disk, and each of the two SMBHs is associated with a mini-disk (see Figure 1). In between the circumbinary disk and the inner mini-disks, a gap (or hole) is opened by the secondary SMBH, which is probably the most distinct feature of a BBH–disk accretion system, in analogy to a system in which a gap or hole is opened by a planet migrating in the planetary disk around a star (Lin et al. 1996; Quanz et al. 2013). This type of geometric configurations for the BBH–disk accretion systems has been revealed by many numerical simulations and analysis (Artymowicz & Lubow 1996; Escala et al. 2005; Hayasaki et al. 2008; Cuadra et al. 2009; D’Orazio et al. 2013; Farris et al. 2014; Roedig et al. 2014).4 4 The width of the gap (or hole) is roughly determined by, but could be somewhat larger than, the Hill radius . However, set a slightly large gap size, e.g., , does not affect the results presented in this paper significantly. The continuum emission from disk accretion onto a BBH may be much more complicated than that from disk accretion onto a single SMBH, since the dynamical interaction between the BBH and the accretion flow onto it changes the disk structure (Gültekin & Miller 2012; Sesana et al. 2012; Rafikov 2013; Roedig et al. 2014; Yan et al. 2014; Farris et al. 2015). Nevertheless, we adopt a simple model to approximate the continuum emission from a BBH–disk accretion system as the combination of the emissions from an outer circumbinary disk and an inner mini-disk around the secondary SMBH, each approximated by multicolor blackbody radiation in the standard thin disk model (Novikov & Thorne 1973; Shakura & Sunyaev 1973). The emission from the mini-disk around the primary SMBH is insignificant for a BBH system with a small mass ratio (roughly in the range of a few percent to 0.25) due to its low accretion rate as suggested by the state of the art numerical simulations (Roedig et al. 2012; Farris et al. 2014), thus its emission can be neglected. Our analysis suggests that a large q cannot lead to a good fit to the observations.

2021AandA...655A..12T__Tang_et_al._2017b_Instance_4

Using the RADEX3 non local thermodynamic equilibrium (LTE) modeling program (van der Tak et al. 2007) with collisional rate coefficients from Wiesenfeld & Faure (2013), we modeled the relation between the gas kinetic temperature and the measured average of para-H2CO 0.5 × [(322–221 + 321–220)/303–202] ratios, adopting a 2.73 K background temperature, an average observational linewidth of 4.0 km s−1, and column densities N(para-H2CO) = 2.7 × 1012 and 3.7 × 1012 cm−2 for N113 and N159W, respectively. The results are shown in Fig. 5. The values of the para-H2CO column density were obtained with APEX data (beam size ~30″; Tang et al. 2017b), which cover similar regions. Different column densities of para-H2CO only weakly affect derived kinetic temperatures (see Fig. 3 in Tang et al. 2017b or Fig. 4 in Tang et al. 2018a; this was also shown in Fig. 13 and discussed in Sect. 4.3.1 of Mangum & Wootten 1993) as long as all lines are optically thin. Considering that the relation between the gas temperature and the para-H2CO line ratio may vary at different spatial densities (see Fig. 2 in Tang et al. 2017b), we modeled it at spatial densities 104, 105, and 106 cm−3 in Fig. 5. It appears that Tkin at n(H2) = 105 cm−3 is consistently lower than values at 104 and 106 cm−3 by ≲23% and ≲34%, respectively, for Tkin ≲ 100 K. Local thermodynamic equilibrium (LTE) is a good approximation for the H2CO level populations under optically thin and high-density conditions (Mangum & Wootten 1993; Tang et al. 2017a,b, 2018b). Following the method applied by Tang et al. (2017b) in their Eq. (2), we plot the relation between the LTE kinetic temperature, TLTE, and the para-H2CO (3–2) line ratio in Fig. 5. Apparently, TLTE agrees well with Tnon-LTE at volume densities n(H2) ~ 105 cm−3 as long as Tkin ≲ 100 K. Previous observations show that para-H2CO (3–2) is sensitive to gas temperature at density 105 cm−3 (Ginsburg et al. 2016; Immer et al. 2016; Tang et al. 2017b). The spatial density measured with para-H2CO (303–202) and C18O (2–1) in N113 and N159W is n(H2) ~ 105 cm−3 on a size of ~30″ (Tang et al. 2017b). Therefore, here we adopt 105 cm−3 as an averaged spatial gas density in the N113 and N159W regions.

2018AandA...610A..38F__Bisterzo_et_al._2017_Instance_3

Similarly to the [α/Fe] ratio, the ratio of the slow (s-) neutron capture process elements to iron can be regarded as a cosmic clock. Ba, Sr, La, and Y are mainly s-process elements produced on long timescales by low mass AGB stars (Matteucci 2012). Since a low mass star must evolve to the AGB phase before the s-process can occur, the s-process elements are characterized by a delay in the production, much like the delay of iron production by SNe Ia relative to the α elements production by core collapse SNe. Among the four s-process elements mentioned above, GES provides the abundances of Y II (the first s-process peak element) and Ba II (the second s-process peak element) for all our sample stars. Their abundances behave differently in the Galactic thick and thin discs (Bensby et al. 2005, 2014; Israelian et al. 2014; Bisterzo et al. 2017; Delgado Mena et al. 2017). Unlike the Galactic thick disc stars, which show an almost constant [Ba/Fe] abundance close to the solar value, the Galactic thin disc stars have their [Ba/Fe] abundances increasing with [Fe/H] and reaching their maximum values around solar metallicity, after which a clear decline is seen (see also Cristallo et al. 2015a,b, for the most recent s-process calculation in AGB yields). The same trend is observed in our sample. In Fig. 13 we display the Li-[Ba/Fe], [Ba/Fe] as a function of [Fe/H], and the evolution of absolute Ba abundance A(Ba), as derived from Ba II lines. Similar figures are also plotted for yttrium (Y II). [Ba/Fe] and [Y/Fe] values here are derived from MCMC simulations, taking into account the measurement uncertainties of A(Ba II)/A(Y II) and [Fe/H]. By applying the same MCMC setups used for [α/Fe] (see Sect. 3.1), we calculate the mean values of [Ba/Fe] and [Y/Fe] for each star. These values, together with their corresponding 1σ uncertainties, are listed in Table 1. In the literature there are several theoretical works on the evolution of [Ba/Fe] and [Y/Fe] in the Galactic thin disc (e.g. Pagel & Tautvaisiene 1997; Travaglio et al. 1999, 2004; Cescutti et al. 2006; Maiorca et al. 2012; Bisterzo et al. 2017). For comparison, we show in Fig. 13 the predictions of the most recent one (Bisterzo et al. 2017) where the updated nuclear reaction network was used.

2019MNRAS.485L..78V__Chatterjee_et_al._2017_Instance_1

The properties of the persistent radio source associated with FRB 121102 may be constrained independently of the Faraday-rotating medium. We assume equipartition between the relativistic gas and magnetic field as is common in synchrotron sources3 (Readhead 1994). The source becomes self-absorbed at $1.5$ GHz for radius $R_{\rm per} < 0.05$ pc; this is thus the lower bound on the source size. European Very Long Baseline Interferometry (VLBI) Network observations of the source at 5 GHz set an upper bound on the source radius of Rper ≲ 0.35 pc (Marcote et al. 2017). This is consistent with the ${\approx } 30\, {{\rm per\, cent}}$ amplitude modulations observed in the source at 3 GHz (Chatterjee et al. 2017) being caused by refractive interstellar scintillation in the Milky Way interstellar medium (ISM; Walker 1998). For any radius within the allowed range (0.05 Rper/pc 0.35), we can determine the equipartition magnetic field, Beq, and the column of relativistic electrons, Nrel, using the standard expressions for synchrotron emissivity and absorption coefficients (Rybicki & Lightman 1979, their equations 6.36 and 6.53). We assume a power-law energy distribution of radiating electrons with somewhat shallow index of b = −1.5 that can account for the relatively flat spectrum of the source (Chatterjee et al. 2017). The peak Lorentz factor of the distribution, γmax, is chosen to correspond to the observed spectral break frequency of $\nu _{\rm max}=10$ GHz. If the lower Lorentz factor cut-off corresponds to emission at $\nu _{\rm min}=1$ GHz,4 then the equipartition magnetic field and electron column thus determined for minimum and maximum source sizes are $B_{\rm eq}\approx 140$ mG, $\gamma _{ \rm min}\approx 50$, γmax ≈ 160, $N_{\rm rel} \approx 0.95\, {\rm pc}\, {\rm cm}^{-3}$ for $R_{\rm per}=0.05$ pc, and $B_{\rm eq}\approx 27$ mG, $\gamma _{ \rm min}\approx 120$, γmax ≈ 370, $N_{\rm rel} \approx 0.1\, {\rm pc}\, {\rm cm}^{-3}$ for $R_{\rm per}=0.35$ pc. The reader can scale the equipartition field to other source sizes using Beq(R) ∝ R−6/7. The total energy contained in the relativistic electrons and the magnetic field (‘equipartition energy’) is ∼1049.1 and ∼1050.2 erg, respectively. If the relativistic electrons were injected in a one-off event, the synchrotron cooling rates at γmax yield source ages of $14$ yr for R = 0.05 pc and 60 yr for $R=0.35$ pc. The corresponding expansion velocities are $0.011\, c$ and $0.02\, c$, respectively.

2020AandA...643A...5D__Saintonge_et_al._2011_Instance_1

There is significant scatter (larger than 1 dex) among the tdepl measurements in all redshift bins, even though we only consider MS galaxies with ΔMS = ±0.3 dex around the MS parametrization of Speagle et al. (2014). This scatter at a fixed redshift is believed to be a product of the multi-functional dependence of tdepl on many physical parameters, such as the offset from the MS, the star formation rate, the stellar mass, and possibly the environment (e.g., Dessauges-Zavadsky et al. 2015; Scoville et al. 2017; Noble et al. 2017; Silverman et al. 2018; Tacconi et al. 2018; Tadaki et al. 2019; Liu et al. 2019b). Given the strong anti-correlation found between tdepl and the offset from the MS (Genzel et al. 2015; Dessauges-Zavadsky et al. 2015; Tacconi et al. 2018), we still expect tdepl variations for galaxies on the MS while in their evolutionary process they are transiting up and down across the MS band (e.g., Sargent et al. 2014; Tacchella et al. 2016). The previously reported anti-correlation between tdepl and sSFR (Saintonge et al. 2011; Dessauges-Zavadsky et al. 2015) is also further supported by our galaxies at z = 4.4 − 5.9 (Fig. 6, middle panel). This highlights comparable timescales for gas consumption and stellar mass formation. We find a Spearman rank coefficient of −0.49 and p-value of 4.5 × 10−10 for the dependence of tdepl on sSFR when considering the MS SFGs at z ∼ 1 − 5.9. The observed offset of ALPINE galaxies with respect to the tdepl–sSFR relation of MS SFGs at z = 0 and to a smaller extent to the relations at z ∼ 1 and z ∼ 2 is compatible with the displacement of the z = 0 relation along the sSFR-axis by factors derived from the sSFR evolution with redshift of MS SFGs out to z ∼ 5 (Speagle et al. 2014). Nevertheless, a less steep sSFR redshift evolution toward z ∼ 5 than parametrized by Speagle et al. (2014) is suggested by the ALPINE sample, in line with the sSFR(z) results of Khusanova et al. (2020a). On the other hand, with tdepl measurements achieved down to Mstars ∼ 108.4 M⊙ for the ALPINE galaxies, we confirm that for MS SFGs at z ∼ 1 − 5.9 the tdepl dependence on Mstars, if any, must be weak as shown in Fig. 6 (right panel). This further supports the idea that the linear KS relation established for local galaxies (Kennicutt 1998b) might hold up to z ∼ 5.9 for MS SFGs.

2016MNRAS.461..344P__Bernardi_2009_Instance_2

The fact that all massive ETGs fall into a single FP, coupled with the segregation of the highest ranked objects from lesser galaxies, entails that any non-edge-on projection of this flat surface must also lead to segregated 2D scaling laws. To investigate this issue we now use M⋆ to represent scale, shifting to the (logarithmic) RVM coordinate system defined by the three most basic global parameters connected by the standard plane. In this manner we set the framework for the study of two of the most firmly established empirical scaling relations of elliptical galaxies: the Kormendy-like RM relation (Shen et al. 2003) and the VM relation, a mass analogue of the classical FJR. In good agreement with observational studies of BCG/BGGs (Bernardi et al. 2007; Lauer et al. 2007; Bernardi 2009; Méndez-Abreu et al. 2012), we find that our simulated first-ranked galaxies lie off the standard 2D scaling relations defined by the bulk of the ETG population. In particular, as shown in Fig. 3, BGGs are larger and have lower effective velocity dispersions than ordinary ellipticals of the same stellar mass. The combination of this latter result with the fact that light appears to be similarly concentrated for BGGs than for non-BGGs (Paper I), tells us that the total mass-to-light fraction interior to Re is lower for the former than for the latter. Our numerical experiments, however, do not seem to support claims of the steepening of the R ∝ Mα relation for CGs towards values α ≳ 1 (Lauer et al. 2007; Bernardi 2009). We find instead that our first-ranked objects, which occupy a locus moderately offset from the central axis of the observational data in Fig. 3A, are well fitted by a model with α = 0.60 ± 0.03. This value of the power-law index agrees well with the slope α ∼ 0.65 found for non-BCG galaxies in the Sloan r-band (Shen et al. 2003; Bernardi et al. 2007), while a plain orthogonal fit to the general elliptical population data included in Fig. 3A (green dots and small grey circles) also gives α ∼ 0.6. Approaching closer to our findings, Liu et al. (2008) obtain α ∼ 0.9 from surface measurements up an isophotal limit of 25 r-mag arcsec−2, which reduces to ∼0.8 when their magnitudes are transformed into mass in old stars, whereas they get α ∼ 0.75 for a control sample of non-BCGs. Differences in the photometry, the waveband of the observations, sample construction (i.e. incompletenesses and selection biases), and fitting methods would help explain the lack of a closer agreement between these results about the most robust of the 2D relationships.

2017AandA...599A...4K__hand,_Hotta_et_al._(2016)_Instance_1

Our results appear to stand apart from similar studies in full spherical shells (e.g., Nelson et al. 2013; Hotta et al. 2016) in that the differential rotation is strongly quenched as a function of the magnetic Reynolds number. However, in Nelson et al. (2013) the values of Rm′ (=2πReM) correspond to a range of 8...32 in ReM in our units where the radial and latitudinal differential rotation decrease by about 30 per cent. This is roughly consistent with our results. On the other hand, Hotta et al. (2016) reached higher values of ReM than in the present study, but no strong quenching was reported. The reason might be that their models are rotating substantially slower than ours, leading to weaker magnetic fields and a weaker back-reaction to the flow. Furthermore, in these models, the differential rotation is strongly influenced by their SGS heat flux, which transports one third of the luminosity. Another obvious candidate for explaining the difference is the wedge geometry used in the current simulations. However, we note that earlier simulations with a similar setup did not show a marked trend in the energy of the differential rotation as the azimuthal extent of the domain was varied (see Table 1 of Käpylä et al. 2013). However, results of Boussinesq simulations of convective dynamos have shown a similar change as a function of the magnetic Prandtl number (Schrinner et al. 2012). The drop in the amplitude of the differential rotation was associated with a change in the dynamo mode from an oscillatory multipolar solution to a quasi-steady dipolar configuration (cf. Fig. 15 of Schrinner et al. 2012) that prevents strong differential rotation from developing. We do not find a strong dipole component in our simulations (see Sect. 4.2.3). However, the strong suppression of the differential rotation often coincides with the appearance of a small-scale dynamo (see Table 1 and the discussion in the Sect. 4.1.2) or a change in the large-scale dynamo mode as discussed above.

2022MNRAS.516.5712T__Krumholz_et_al._2015_Instance_1

As discussed in Section 1, one of the primary motivations for this work is to attempt to understand the variations in apparent IMF that have been observed in early type galaxies (ETGs). While there are multiple lines of evidence for this variation, the most straightforward to extract from out simulations is the mass to light ratio of the stellar populations we produce. In this section we therefore look at the mass to light ratio in our simulations for the purpose of comparing to that in observed galaxies. We calculate this by using the slug stellar population synthesis code (da Silva, Fumagalli & Krumholz 2012; Krumholz et al. 2015) to generate isochrones at stellar population ages from 5 Gyr to 10 Gyr, using the MIST stellar evolution tracks (Choi et al. 2016) and Starburst99-style stellar atmosphere models (Leitherer et al. 1999). Each isochrone provides a prediction of present-day mass, bolometric luminosity, and luminosity in a range of photometric filters as a function of initial mass for stars with initial mass $\ge 0.1\, {\rm M}_{\odot }$; we assume that the luminosities of stars with initial masses less than 0.1 M⊙ are negligible, and that these stars also experience negligible mass loss. We further assume that all stars with initial mass $\lt 8\, {\rm M}_{\odot }$ (which are all the stars formed in our simulations) that reach the end of their lives leave behind 0.7 M⊙ white dwarf remnants. We use the isochrone to calculate the luminosity and present-day mass of all the stars formed in each of our simulations at ages from 5 − 10 Gyr, and from these we calculate the mass to light ratio of the stellar population as a function of age for each of our simulations. For comparison, we use the same isochrones to calculate the mass to light ratio of Chabrier (2005) and Salpeter (1955, truncated at a lower mass limit of $0.1\, {\rm M}_{\odot }$) IMFs at the same ages. We use the SDSS r band for this calculation, but results are qualitatively similar in other filters.

2020MNRAS.498.4906B__Mehringer_et_al._1992_Instance_1

The large radiation field directly produced from young stellar objects can exert a significant pressure on the surrounding material. This radiation pressure, Prad, at a given position within an H ii region, is related to the bolometric luminosity, Lbol, of the stellar population and the distance, r, from each star to that position within the region: (3)$$\begin{eqnarray*} P_\mathrm{rad} = \sum {\frac{L_\mathrm{bol}}{4 \pi r^{2} c}}, \end{eqnarray*}$$where the summation is over all stars within the region. The volume-averaged direct radiation pressure, Pdir, is then given as (Lopez et al. 2014), (4)$$\begin{eqnarray*} P_\mathrm{dir} = \frac{3 L_\mathrm{bol}}{4 \pi R^{2} c}, \end{eqnarray*}$$where R is the radius of the H ii region (or effective radius, Reff, that we define later and use throughout the rest of the paper), and Lbol is the bolometric luminosity from the population of massive stars within the H ii region. This form differs by a factor of three from McLeod et al. (2019, equation 4), as these authors calculate the radiation surface pressure rather than the volume average pressure. This expression is appropriate to compute the force balance at the surface of an empty shell. However, as this work aims at understanding the large-scale dynamics of the Galactic Centre H ii regions (e.g. the total energy and pressure budget for each source), the inclusion of a factor of three in the numerator of the above equation is required. We also note here that the higher metallicity within the Galactic Centre, or increasing the amount of dust, has no effect on the Pdir calculation. Direct radiation pressure is limited by the momentum supplied by the stellar radiation field, and, as long as there is enough dust around to absorb all the radiation, the momentum per unit time delivered is the same. Lopez et al. (2011) determine the bolometric luminosity of the H ii regions within the LMC and SMC from H $\alpha$ emission. However, this is not possible for the H ii regions investigated here, due to the high optical extinction towards the Galactic Centre (Av >20 mag), which completely obscures any H $\alpha$ emission. We, therefore, adopt two alternative methods of calculating the bolometric luminosity using radio and infrared observations (i.e. wavelengths where the emission is much less affected by dust extinction). First, we can make the assumption that the bolometric luminosity is proportional to the flux of ionizing photons, $\mathcal {N}_\mathrm{LyC}$, such that $L_\mathrm{bol} = \mathcal {N}_\mathrm{LyC} \left\langle h \nu \right\rangle$, where 〈hν〉 ∼ 15  eV is the mean photon energy (Pellegrini et al. 2007). We use the $\mathcal {N}_\mathrm{LyC}$ for each H ii region as determined from the radio observations outlined in Table  2 (i.e. Gaume & Claussen 1990; Mehringer et al. 1992; Schmiedeke et al. 2016), and solve for the direct radiation pressure using equation (4). The second method assumes that the luminosity integrated over infrared wavelengths approximately corresponds to the total bolometric luminosity. This is a common assumption made for embedded star-forming regions, where the luminosity from massive stars produced at ultraviolet wavelengths is absorbed and remitted by the dust in the infrared. Barnes et al. (2017) have produced maps of the total infrared luminosity across the Galactic Centre. These authors fit a two-component modified blackbody function to extinction corrected 5.8–24 $\mu $m (Carey et al. 2009; Churchwell et al. 2009) and 160 –500 $\mu $m (Molinari et al. 2010) emission maps (referred to as the warm and cool component of the bolometric luminosity; see fig. 2 of Barnes et al. 2017). These infrared (i.e. bolometric) luminosity maps are used with the two methods outlined below to also determine the direct radiation pressure within each of the Galactic Centre H ii regions. In comparison with the first method for calculating the direct radiation pressure from radio observations, we choose to identify individual sources within the infrared maps. These can be considered as discrete H ii regions each with a single value of the direct radiation pressure. We choose to identify these H ii regions in the map of the warm component of the bolometric luminosity using a dendrogram analysis (Rosolowsky et al. 2008). We choose to use a structure finding algorithm, as opposed to by-eye identification, to give reproducibility within regions with particularly complex morphology (the warm bolometric luminosity map is given in fig. 2 from Barnes et al. 2017).3 We make use of the ‘leaves’ identified from the dendrogram analysis, which are the highest level (i.e. smallest) structures in the analysis and here represent distinct H ii regions. We take the mask of each H ii regions (dendrogram leaf), and apply this to both the warm and cool bolometric luminosity component maps, which we sum to then get the total bolometric luminosity. This is used with equation (4) to get the direct radiation pressure (Pdir) within each H ii region. The effective radius (Reff) of each H ii region is defined as the radius for a circle with the corresponding area (A) of each structure (i.e. $R_{\rm eff}=\sqrt{A/\pi }$). In addition to the dendrogram analysis, we also calculate the direct radiation pressure within apertures of increasing radius from the centre of each H ii region complex. To do so, we place circular masks for each source on to both the warm and cool bolometric luminosity component maps, and sum the enclosed values to then get the total bolometric luminosity. The circle is then increased in radius, and the process repeated. We again use equation (4) to determine the direct radiation pressure within these increasing circular apertures. This method differs from the dendrogram analysis, as it returns a continuous radial distribution from the source centre, as opposed to a distribution of distinct H ii region with various sizes.

2020AandA...637A..59A__Ziurys_et_al._(2018)_Instance_2

Several molecules show a large discrepancy between the abundances derived from observations and calculated by chemical equilibrium, although it is not as severe as for the molecules discussed above. We refer to PN in O-rich stars and H2S in C-rich stars, which are indicated by hatched rectangles in Fig. 2. For PN in O-rich AGB atmospheres, the disagreement between the observed abundances, (1–2) × 10−8 (Ziurys et al. 2018), and the calculated maximum chemical equilibrium abundance is almost three orders of magnitude. However, uncertainties on the observational and theoretical sides mean that the true level of disagreement is unclear. For example, while Ziurys et al. (2018) derived a PN abundance of 10−8 relative to H2 in IK Tau, De Beck et al. (2013) and Velilla Prieto et al. (2017) derived higher abundances, (3–7) × 10−7, in this source. When we give preference to these latter abundances, the level of disagreement would be even higher. On the other hand, the formation enthalpy of PN is rather uncertain (see Lodders 1999), which directly translates into the calculated chemical equilibrium abundance. In this study we adopted the thermochemical data for PN from Lodders (1999), who gives preference to a formation enthalpy at 298.15 K of 171.5 kJ mol−1, while other compilations such as JANAF use lower values that would result in higher chemical equilibrium abundances for PN. This would reduce the level of disagreement. In the case of H2S in C-rich AGB stars, the calculated maximum chemical equilibrium abundance is 7 × 10−11, while the value derived from observations is ~50 times higher. In this case, the observed abundance is based on the detection of only one line in only one source (see Agúndez et al. 2012), and thus it has to be viewed with some caution. In summary, the main failures of chemical equilibrium to account for the observed abundances of parent molecules in circumstellar envelopes are NH3, HCN, CS, SO2, and possibly PN in M-type stars, H2O and NH3 in S-type stars, and the hydrides H2O, NH3, SiH4, PH3, and perhaps H2S as well in C-type stars. The large discrepancies between the abundances derived from observations and those calculated with chemical equilibrium necessarily imply that nonequilibrium chemical processes must be at work in AGB atmospheres. Any invoked nonequilibrium scenario must account for all these anomalously overabundant molecules, but must also reproduce the remaining molecular abundances that are reasonably well explained by chemical equilibrium. No scenario currently provides a fully satisfactory agreement with observations, although two mechanisms that can drive the chemical composition out of equilibrium have been proposed.

2021AandA...654A..89P__Poutanen_&_Svensson_1996_Instance_1

We now investigate relativistic reflection models with a primary Comptonisation continuum shape, which is more physical and has a sharper high-energy rollover compared to an exponential cutoff power law. In addition, such models have the advantage of having the hot corona temperature (kThot) as a physical parameter rather than a phenomenological exponential cutoff energy. We first apply the RELXILLCP model, which uses the NTHCOMP Comptonisation model (Zdziarski et al. 1996; Życki et al. 1999) as the incident spectrum. The other physical parameters are the same as those in the RELXILL model presented above. We find a good fit and infer similar hot corona temperatures of kThot ∼ 26 keV for all three epochs (Table 2). Then, we consider the REFLKERR where the hard X-ray Comptonisation spectrum is computed with the COMPPS model (Poutanen & Svensson 1996), which appears to be a better description of thermal Comptonisation when compared to Monte Carlo simulations (Zdziarski et al. 2020). Moreover, REFLKERR has, as physical parameter, either the Compton parameter (y) or the optical depth (τ). We choose to perform the fit with the optical depth as the direct inferred parameter. The temperature of the thermal seed photons (kTbb) Comptonised by the hot corona is an explicit physical parameter of this model. Here, we assume that the seed photons are provided by the cold disc, then kTbb was fixed at 10 eV corresponding to the expected maximum temperature of the accretion disc around a black hole mass of 1.4 × 108 M⊙ accreting at a ∼10% Eddington rate. In addition, REFLKERR allows us to choose either a slab or a spherical geometry for the hot corona. The latter corresponds to numerous active sphere regions above the disc surface. The hard X-ray shape of the reflected component is calculated using IREFLECT convolved with COMPPS rather than using XILLVERCP (see Niedźwiecki et al. 2019 for detailed explanations). Both models give good fits (Table 2, Fig. 3), though a larger χ2 value for the spherical geometry. Similar values of the hot corona temperature are measured for the three epochs: kThot ∼ 30−31 keV and kThot ∼ 21−22 keV for the slab and spherical geometries, respectively. From τ, we can infer the corresponding Compton-parameter of the hot corona (yhot) using the relation y = 4τ(kT/511 keV) (Beloborodov 1999). This correspond to yhot ∼ 0.5 and yhot ∼ 1.1 for the slab and spherical geometries, respectively.

2022AandA...664A..45A__Bergh_2000_Instance_1

We can compare Fig. 14 with the corresponding figure obtained by Mowlavi et al. (2019) in the Magellanic Clouds (see their Figs. 4 and 7 for the LMC and SMC, respectively) and in the Milky Way from DR2 data (their Fig. 10). Our conclusions here are similar: (i) There are far fewer C-rich than O-rich stars in the Galaxy than in the Clouds. The low number of carbon stars compared to O-rich stars between the Galaxy and the Clouds agrees with the decrease in TDU efficiency with increasing metallicity, and with a higher O abundance in the envelope of Galactic AGB stars on average. On the other hand, because some of the stars located in RGB and faint AGB region in Fig. 14 might be CH and/or R-hot type stars (i.e. C-rich objects, see previous sections), we could derive a lower limit for the ratio between carbon to M (O-rich) stars. It is well known that this ratio increases with the decreasing (average) metallicity of the galaxy. The primary reason for this correlation is that less C needs to be dredged-up in a metal-poor star to enable atmospheric carbon atoms to exceed those of oxygen. We derive a ratio ∼0.05, which is very similar to the average ratio derived in the disc of M 31 (see e.g. van den Bergh 2000; Hamren et al. 2015). (ii) Moreover, the distribution of O-rich stars of the Gaia-2MASS diagram covers at any given MKs magnitude a much wider range of WRP, BP − RP − WKs, J − Ks in the Galaxy than in the Clouds. The wider distribution in both axes of the O-rich zones in Fig. 14 compared with the corresponding feature in the Clouds would result from the combined effect of O-rich AGB stars turning much later into C-rich stars in the Galaxy, and the existence of a more ample range of stellar metallicities in the Galactic sample. Moreover, Fig. 14 clearly shows the high dispersion existing in MKs for the C-rich objects at a given WRP, BP − RP − WKs, J − Ks value. Part of this dispersion in MKs is compatible with the typical range in Teff (2500−3500 K) deduced for N-type stars (Bergeat et al. 2001), and to the circumstellar extinction (which we did not consider here) preferably for the objects in the extreme C-rich region. However, the mixing of carbon stars of different populations probably also contributes significantly to this dispersion in MKs (and also in Mbol). In fact, following the method outlined in Sect. 4, we have studied the kinematics of these stars and calculated the membership probability to the halo and to the thin and thick disc of the 2659 new carbon star candidates with a Vrad measurement according to EDR3 (∼40% of the sample, i.e. 1305 objects). Figure 15 shows the corresponding Toomre diagram for these carbon stars. Of this limited sample, roughly 50% belong to the thin disc (blue circles in Fig. 15), ∼30% to the thick disc or halo (blue crosses and triangles, respectively), and the rest (∼20%) have an ambiguous membership according to our membership criteria (open blue circles). Nevertheless, we note that assuming a less strict likelihood percentage to assign membership to a population (see Sect. 4), about 25% of the stars with ambiguous membership would be thick-disc and/or halo stars. Because thick-disc and halo stars are older than thin-disc stars on average, many of these C-rich objects are very probably not intrinsic AGB carbon stars, but extrinsic C-rich giants: stars with masses lower than ∼1.5 M⊙ that have become carbon rich through the mass transfer in a binary system. An alternative to this would be the possibility that the minimum mass for the formation of an intrinsic AGB stars could be as low as 1 M⊙. Some observational evidence for this can be found in the literature (Shetye et al. 2019). This conclusion is reinforced by the scale height onto the Galactic plane that can be estimated for all the C-rich stars in Fig. 14 similarly to what was done in Sect. 2: an exponential fit gives zo ∼ 600 pc, which is much larger that the scale height derived for the intrinsic N-type AGB carbon stars that clearly belong to the thin disc (see Sect. 2).

2021MNRAS.507.2115M__Isanto_&_Polsterer_2018_Instance_1

In astrophysics, the number of studies that apply ML techniques has risen substantially in the last years. Unsupervised learning algorithms have been used to identify different kinematic components of simulated galaxies (Obreja et al. 2018, 2019), to compare stellar spectra (Traven et al. 2017), to classify pulsars (Lee et al. 2012), and to find high-redshift quasars (Polsterer, Zinn & Gieseke 2013). Supervised learning has been used to classify variable stars (Richards et al. 2011), to classify galaxies morphologically (Huertas-Company et al. 2008), and to determine the redshift of galaxies (Hoyle et al. 2015; Hoyle 2016; D’Isanto & Polsterer 2018). Recently, ML has also been used to connect the properties of galaxies and dark matter haloes using supervised learning techniques. Kamdar, Turk & Brunner (2016a), Kamdar, Turk & Brunner (2016b) use tree-based methods to predict several galaxy properties from a set of halo properties and train the models on galaxy catalogues obtained from semi-analytic models and the Illustris hydrodynamic simulation (Vogelsberger et al. 2014). Sullivan, Iliev & Dixon (2018) train a simple neural network with one hidden layer to predict the baryon fraction within a dark matter halo at high redshift, given several halo properties (features). As training data they use the results of a cosmological hydrodynamic simulation with Ramses-RT. Similarly, Agarwal, Davé & Bassett (2018) use several ML methods to link input halo properties to galaxy properties, training on the Mufasa cosmological hydrodynamical simulation. Taking a reverse approach, Calderon & Berlind (2019) train tree-based methods and a neural network to derive halo mass from galaxy properties, training on an SDSS group catalogue. The limitation of all these studies is the supervised training and the training data. As labelled galaxy-halo data is not available for observed systems, the data for supervised learning has to be taken from a model. Even if the ML algorithms learn to reproduce the training data perfectly, the connection between galaxy and halo properties is the same as in the simulations. If the simulations predict the true relations poorly, so will the ML method. Therefore ML algorithms should not be trained on simulated data, but on observed data directly.

2015MNRAS.450.4364N__Wu_et_al._2004_Instance_1

Low- and intermediate-mass stars are formed by the gravitational collapse of the parental giant molecular cloud (GMC), followed by the accretion process (Palla 1996). During the accretion phase, material is ejected as well via collimated bipolar jets. However, when a YSO reaches 8 M⊙, the radiative flux becomes so intense (using ϕ = L/4πd2, the ratio between the radiative fluxes of an O5 and a B3 star – masses of ∼40 and ∼8 M⊙, respectively – is ≈250) that it may interrupt the accretion flow. A process that constrains the outcoming radiation field to narrower angles may leave some room for the accretion process to continue in some directions. This seems to be the case for the outflows driven by young stars from a very broad mass range, as previous reported by several authors (Bachiller 1996; Bontemps et al. 1996; Shepherd & Churchwell 1996; Beuther et al. 2002; Wu et al. 2004). Outflows associated with high-mass objects are expected to be more energetic than the outflows observed in lower mass YSOs (Beuther et al. 2005; Zhang et al. 2005; López-Sepulcre et al. 2009), with velocities greater than ∼100 km s−1 (Martí, Rodríguez & Reipurth 1998). Some authors have found evidences that outflows associated with massive stars are scaled up versions of their low-mass counterparts (Vaidya et al. 2011; Codella et al. 2013) while other works have reported that no well-collimated outflows have been found towards MYSOs (Shepherd, Testi & Stark 2003; Sollins et al. 2004). Massive YSO outflows mapped in high-velocity CO lines have collimation factors R = length/width ∼2.05 ± 0.96 as compared to R ∼ 2.81 ± 2.16 for low-mass stars (Wu et al. 2004), indicating a weak tendency that outflows associated with massive stars are less collimated than those from low-mass stars as previously thought (Richer et al. 2000). Besides the degree of collimation, these massive outflows would work removing mass from the plane of the accretion disc, lowering the density on the plane and, therefore, facilitating the accretion flow to reach the stellar core as shown in the recent 3D simulations presented by Krumholz et al. (2009). Although these authors have not included the outflow activity on their simulations, they argue that the presence of outflows would decrease the star formation efficiency from 70 per cent (considering purely radiation effects) to 50 per cent.

2019MNRAS.483.3022G__Mowlavi_et_al._2018_Instance_1

LPVs are known to exist on sets of sequences in period–luminosity space (e.g. Wood et al. 1999; Wood 2000) depending on their variability type and pulsation mode. The primary periods of Mira-like variables lie on the commonly called C and C′ sequences (Ita et al. 2004; Spano et al. 2011) with C′ lying at a lower period than C. Sequences A and B lie at lower periods still and are populated by the so-called OGLE small amplitude red giants or OSARGs (Soszynski et al. 2004a). Sequences D and E are populated by long secondary pulsators, whose nature is still unclear, as well as ellipsoidal and eclipsing binary systems (Soszynski et al. 2004b), with the latter two contained in a separate CRTS catalogue not utilized in this work. Soszyński & Wood (2013) also found that there is a low-amplitude population of SRVs whose primary period lies between the C and C′ sequences. Given that the selections of Fig. 1 produce a CRTS sample with a wide range of visual amplitudes, it is uncertain on which sequence our CRTS sources lie and to which class of LPVs they belong. The left-hand panel of Fig. 2 shows sources from Gaia’s DR2 LPV catalogue (Gaia Collaboration et al. 2016, 2018; Mowlavi et al. 2018) chosen to lie within a 15° aperture of the LMC. Cross-matching with 2MASS reveals three distinct sequences existing in the Ks band, with the middle sequence corresponding to the Mira C sequence. Belokurov et al. (2017) defined a variability parameter (the ‘Gaia amplitude’) based on Gaia’s flux information as: (1) \begin{eqnarray*} \mbox{Amp} = \mbox{log}_{10} \left(\sqrt{N_{\mbox{obs}}} \: \frac{\sigma _{\overline{I_{\mathrm{ G}}}}}{\overline{I_{\mathrm{ G}}}} \right) \end{eqnarray*} where $\sqrt{N_{\mbox{obs}}}$ is the number of observations, $\sigma _{\overline{I_{\mathrm{ G}}}}$ is the mean flux error in the G band, and $\overline{I_{\mathrm{ G}}}$ is the mean flux in the G band. Requiring a Gaia amplitude greater than −0.55 isolates a sample of LPVs lying on a single period–luminosity sequence, as evident in Fig. 2. We produce a secondary cleaned CRTS sample located on this middle sequence by cross-matching with the Gaia DR2 source catalogue and enforcing that the Gaia amplitude > −0.55. Further, we restrict the sample to visual amplitudes >1 to eliminate small amplitude variables. Application of these two cuts gives a cleaned CRTS sample with 225 members. We utilize this cleaned CRTS sample in Section 3.1, where we exploit the period–age correlation of LPVs, the interpretation of which may be muddied if low visual amplitude variables were inadvertently retained. For example, AGB variables residing in a common Magellanic Cloud globular cluster have been observed by Kamath et al. (2010) both at high and low amplitudes. They found stars with low amplitudes also had lower stellar periods, implying differing period–age relations.

2019ApJ...883...88B__Kleint_et_al._2016_Instance_1

The GALEX NUV observations span a wide wavelength range, from 1771 to 2831 Å. While these data provide no spectral information within that bandpass, we rely on solar and stellar flare studies to inform the likely contributors to the flare flux. The NUV spectral region has not had as many observational constraints as the far-UV region in flare studies. Few NUV stellar flare spectra exist at all, and the few that do were obtained either for solar flares or on nearby M dwarfs. Flares observed in the UV are often associated with the more impulsive phases of solar flares, starting with early observations showing a close temporal association between UV and hard X-ray emission (Cheng et al. 1981). Welsh et al. (2006) reported on high time-resolution NUV+FUV flares seen with GALEX on nearby M dwarfs, and Hawley et al. (2007) presented high spectral-resolution NUV flare measurements of an M-dwarf flare. From these two studies, the contribution of emission lines relative to continuum emission could be determined; the main emission lines in the flare NUV spectrum were Mg ii, Fe ii, Al iii, and C iii. However, the main emission component overall was a continuum component. Recent results from solar flares observed from space (Heinzel & Kleint 2014; Kleint et al. 2016) demonstrate an NUV spectrum originating largely from Hydrogen Balmer continuum emission. The formation of the NUV emission appears to originate from an impulsive thermal and nonthermal ionization caused by the precipitation of electron beams through the chromosphere. This explains the temporal correlation with solar flare hard X-ray emission observed previously. More recently, Kowalski et al. (2019) presented accurately calibrated NUV flare spectra at high time cadence on an M dwarf, again finding a large flux enhancement due to continuum radiation. They commented that the oft-used 9000 K blackbody used to describe blue-optical stellar flare emission (Hawley et al. 2003) under-predicts the NUV continuum flare flux by a factor of two. Based on general similarities in radiative properties between solar and stellar flares studied thus far (Osten 2016), it is likely that a combination of line and continuum emission enhancements are present in the NUV flare flux from the flares being considered, but we cannot speculate about the relative contribution of one versus the other. These sources originate from different layers of the stellar atmosphere: singly and doubly ionized emission lines likely originate in the chromosphere, and the Balmer continuum emission also originates from the chromosphere. Some lines, such as Mg ii, exhibit absorption components and self-reversals, indicating optical depth effects in the atmosphere, while other lines such as Fe ii appear to be optically thin. Any hot blackbody emission might originate further in the photosphere.

2020ApJ...892...53A__Dudas_et_al._2018_Instance_1

These new limits, in conjunction with the inconsistency of isotropic flux interpretations, leave no room for an astrophysical interpretation of AAEs in the context of the standard model for time windows as short as 103 s. However, it has been shown that these events can be explained using physics beyond the standard model, as many models suggest that AAEs lend support for axionic dark matter, sterile neutrinos, supersymmetry, or heavy dark matter (Anchordoqui et al. 2018; Connolly et al. 2018; Dudas et al. 2018; Fox et al. 2018; Huang 2018; Abdullah et al. 2019; Anchordoqui & Antoniadis 2019; Borah et al. 2019; Chauhan & Mohanty 2019; Cherry & Shoemaker 2019; Chipman et al. 2019; Cline et al. 2019; Collins et al. 2019; Esmaili & Farzan 2019; Esteban et al. 2019; Heurtier et al. 2019a, 2019b; Hooper et al. 2019). Many of these models, excluding the axionic dark matter explanation (Esteban et al. 2019) or those heavy dark matter scenarios that are tuned to prevent signatures in IceCube (Hooper et al. 2019), can be constrained by this nonobservation at IceCube. Dedicated tests to quantify these constraints are beyond the scope of this work and may be the focus of a future study. In addition to explanations that incite new physics, it has recently been suggested that AAEs could be explained by downward-going CR-induced EASs that reflected off of subsurface features in the Antarctic ice (Shoemaker et al. 2019). Another possible explanation could be coherent transition radiation from the geomagnetically induced air shower current, which could mimic an upgoing air shower (Motloch et al. 2017; de Vries & Prohira 2019). Explaining these anomalous events with systematic effects or confirming the need for new physics requires a deeper understanding of ANITA’s detection volume. Efforts such as the HiCal radio frequency pulser, which has flown alongside ANITA on the last two flights (Prohira et al. 2018), are already underway to try to characterize the various properties of the Antarctic ice surface.

2020ApJ...899..143C___2016_Instance_1

Having constrained the estimated CME propagation by matching the observed and modeled fronts in this way, it was then possible to estimate the arrival time and velocity of each CME as it passed Venus and Earth. At Venus, the shock driven by CME-2 caught up with CME-1, showing a typical shock-ICME structure. According to observations from the Wind spacecraft, the shock driven by CME-2 had passed through CME-1 before arrival at Earth. By comparing the estimated and observed arrival times of each CME, our approach resulted in arrival time estimates within 2.5 hr of those observed at VEX and Wind. The longitudinal structure of the CMEs obtained by this approach has the potential to improve space weather forecasting. The accuracy of arrival times can be affected by uncertainties in the CME initial conditions, interaction between CMEs, distortion of the CME shape by solar wind structure, the presence of shocks, and the efficiency of solar wind drag on each CME. The relative importance of each such factor could be investigated through an ensemble approach. The drag-based ensemble model developed by Dumbović et al. (2018) is a possible option, but the method is not valid for CME–CME interaction events. In order to test the sensitivity of CME propagation direction and width to the predicted arrival time and velocity, we summarize the CME initial parameters from Srivastava et al. (2018), Kilpua et al. (2019), and Scolini et al. (2019) and two conditions from ours (the time at which the fastest velocity was estimated and the last observation by the coronagraphs). Table 2 shows the initial parameters from those studies and the errors in predicted velocity and arrival time. Note that the minimum value of the drag parameters we used here is 1 × 10−9 km s−1, which was revealed as the lowest possible value (Rollett et al. 2014, 2016; Temmer & Nitta 2015; Kubicka et al. 2016). Based on that, there may exist a residual between the predicted profile and the observations. The residual values of nose and ghost fronts are shown in the last two columns of Table 2. For an Earth-directed CME, the residual between model and observed elongations of the CME nose will reflect the accuracy of the predicted arrival time and in situ velocity of the CME at Earth. The residual value between model and observed elongations of the CME flank provides information about how well the model reproduces the shape of the CME front, which is important for predicting the time and in situ speed at Earth for those CMEs whose flank encounters the Earth. The model with the smallest residual values between model and observed elongations for both CME nose and flank provides the most creditable estimate of the in situ CME parameters at Earth, suggesting that the use of this technique can add to our forecast skill. In our analysis, the CME initial parameters estimated using the time and location of the fastest CME velocity observed in the coronagraph data produced the smallest residual between subsequent observed and model CME elongations. This should therefore be considered to be the best estimate of CME kinematics, and, if we were doing a weighted ensemble, this would be given a much higher weighting than the other runs. It should be noted that many of the other runs did not produce a minimum residual within the likely range of drag parameters, indicating that these runs are somehow not capturing the physics. Table 2 shows that different initial parameters change the predicted arrival time of CME-1 at Venus by 8.5 hr and at Earth by 14.6 hr in total. For CME-2, the prediction errors are found as 6 hr at Venus and 8 hr at Earth. The prediction errors are comparable to the mean absolute error obtained from the drag-based ensemble model (Dumbović et al. 2018). In future work, we will try to use the heliospheric upwind extrapolation model in large ensembles to efficiently investigate the effect on the CME transit time of the uncertainty in the initial CME parameters and ambient solar wind (Owens et al. 2020a). Our results indicate that CME-1 and CME-2 interacted with each other before they arrived at Venus, according to the propagation distances derived from our modeling. The in situ observation from Venus shows a typical shock-ICME complex structure, which provides confirmation of this interpretation. Our results also suggest that a CME–CME interaction is possibly involved in disrupting the propagation direction or geometry of each CME. Such a deflection and interaction is consistent with the fact that CME-1 (CME-2) reached VEX earlier (later) than expected (Shen et al. 2012, 2014; Wang et al. 2014, 2016). The difference between expected arrival time and observational arrival time is larger at Earth than at Venus. This implies that the interaction persisted between Venus and Earth.

2017ApJ...837..130V__Pasquini_et_al._2008_Instance_1

From the X-ray point of view, old open clusters are interesting for a number of reasons. First, X-ray observations efficiently detect different classes of close, interacting binaries, enabling the study of processes such as tidal coupling and the link between X-rays and rotation. The X-ray luminosity of late-type stars strongly depends on stellar rotation. As single stars age, they spin down due to magnetic braking (Pallavicini 1989). As a result, their X-ray emission decreases accordingly. An old star like our Sun (∼4.5 Gyr) has an X-ray luminosity of about 1026 to 1027 erg s−1 (0.1–2.4 keV; Peres et al. 2000). Even with the deepest exposures of a sensitive X-ray telescope like the Chandra X-Ray Observatory, this is nearly impossible to detect except for the nearest stars. Nevertheless, an early ROSAT observation of the old open cluster M 67, which lies at ∼840 pc (Pasquini et al. 2008) and is about as old as the Sun ( ; Dinescu et al. 1995), revealed a large number of X-ray sources among the cluster members (Belloni et al. 1993). Many of these turned out to be close, tidally interacting binaries, where the stellar rotation is locked to the orbital period and therefore kept at a level that can sustain magnetically active coronae. Subsequent XMM-Newton (Gondoin 2005; Giardino et al. 2008; Gosnell et al. 2012) and Chandra (van den Berg et al. 2004, 2013; Giardino et al. 2008) observations of old open clusters have detected many such active binaries (ABs). ABs can be binaries of two detached stars, or they can have a contact or semi-detached configuration such as in W UMa and Algol binaries, respectively. In terms of the number of sources, ABs are the most prominent X-ray source class in old open clusters, but other classes of interacting binaries are represented as well. In cataclysmic variables (CVs), the X-rays are the result of accretion from a late-type main-sequence donor onto a white dwarf. In fact, the first ROSAT observation of M 67 was aimed at studying the X-rays from a CV that was discovered in the optical (Gilliland et al. 1991). The origin of the X-ray emission from more exotic open-cluster binaries, like blue stragglers, is less understood, but in X-rays they are more similar to the ABs than to the mass-transfer sources (van den Berg 2013).

2017MNRAS.464..968S__Tacconi_et_al._2006_Instance_1

Comparison of apparent effective diameters of these sources to direct size measurements supports a similar conclusion. Simpson et al. (2015) present ALMA observations of 23 SCUBA-2-selected SMGs with a median physical half-light diameter of 2.4 ± 0.2 kpc, while Ikarashi et al. (2015) show ALMA observations of 13 AzTEC-selected SMGs with a median physical half-light diameter of $1.34^{+0.26}_{-0.28}$ kpc. ALMA observations of four SPT-selected lensed SMGs give a mean physical half-light diameter of 2.14 kpc (Hezaveh et al. 2013b). This measurement is consistent with a recent lensing analysis of a significantly expanded SPT-selected DSFG sample (Spilker et al. 2016). These high-resolution ALMA observations constrain the FIR sizes of the sources to be 1.0–2.5 kpc. Earlier observations of the physical sizes of SMGs by CO detection and 1.4 GHz imaging suggest larger sizes (e.g. Tacconi et al. 2006; Biggs & Ivison 2008; Younger et al. 2008). However, Simpson et al. (2015) point out that the submillimetre sizes are consistent with resolved 12CO detections, while the sizes derived from 1.4 GHz imaging are about two times larger because of the cosmic ray diffusion, which can explain the results before higher frequency observations at ALMA were possible (Chapman et al. 2004; Tacconi et al. 2006; Biggs & Ivison 2008; Younger et al. 2008). Similarly, Ikarashi et al. (2015) reveal that the 12CO detected sizes and the 1.4 GHz imaging sizes of similar sources are greater than their submillimetre sizes as well. Furthermore, observations of local galaxies also show the submillimetre sizes are smaller than the CO detected sizes (e.g. Sakamoto et al. 2006, 2008; Wilson et al. 2008) and the 1.4 GHz continuum sizes (e.g. Elbaz et al. 2011). Our photometrically derived $\sqrt{\mu }d$ is best compared to the submillimetre continuum sizes. With a median apparent effective diameter of $4.2^{+1.7}_{-1.0}$ kpc, the $\sqrt{\mu }d$ of our sample is one to six times the observed intrinsic diameters (1.0–2.5 kpc). Lensing (or multiplicity) increases the apparent effective size of a source, so this comparison favours a lensing (or multiplicity) interpretation for the ACT-selected sources.

2019MNRAS.482..988C__Zhang_et_al._2016_Instance_1

The EW(λ8542)/EW(λ8498) values are obtained around ∼1.4 in the first observing night, which are somewhat smaller than the values around ∼1.6 derived in the second night, when a strong optical flare decay was detected (also see the Section 4.2). These low ratios are consistent with the values found for several other stars with strong chromospheric activity (e.g. Montes et al. 2000; Gu et al. 2002; López-Santiago et al. 2003; Zhang & Gu 2008; Gálvez et al. 2009; Cao & Gu 2014, 2015, 2017; Zhang et al. 2016), which suggests that the $\rm{Ca\,{\small II}}$ IRT line emission arises from plage-like regions. The EHα/EHβ ratios have also been usually used as a diagnostic for discriminating the presence of different structures on the stellar surface. As Huenemoerder & Ramsey (1987) discussed, the low ratios in RS CVn-type stars are caused by plage-like regions, while prominence-like structures have high values. Similar results have also been reported by Hall & Ramsey (1992) who found that low ratios (∼ 1–2) can be achieved both in plages and prominences viewed against the disc, but high values (∼ 3–15) can only be obtained in extended prominence-like structures viewed off the stellar limb. We obtain the ratios on SZ Psc during the flare decay phase change from 2.40 to 3.40, which are not especially high and anticorrelated with the variation of activity emission shown in Fig. 3, in which the EWs of Hα and Hβ line subtraction and the EHα/EHβ values are plotted as a function of orbital phase. Especially when the EWs have an increasing oscillation during the gradually decrease, which is resulted from flare ejection (see the discussion in Section 4.2), the ratios have a similar anticorrelation feature. These facts suggest that the low EHα/EHβ ratios and its variation might be dominantly associated with the flare decrease, and accompanied cool post-flare loops (see the Section 4.3) might play a part of role for the low ratios, which may have a state like absorbing prominence projected against the disc.

2018MNRAS.473.4566P__Papaderos_et_al._2006_Instance_1

The young starburst inferred by the detections of high ionization emission line of He II λ4686 and the blue WR bump in this and previous works (Guseva et al. 2000; Brinchmann, Kunth & Durret 2008) is confirmed by the age estimates made here for the bright and faint regions in Mrk 22 as ∼4 and ∼10 Myr, respectively. Unlike previous works, we carried out abundance analysis for both the regions separately. We found an appreciable metallicity difference of ∼0.5 dex between the bright region [12 + log (O/H) ∼ 8] and the faint region [12 + log(O/H) ∼ 7.5]. The separation between two regions is ∼0.6 kpc. Typical metallicity gradients in normal spiral galaxies have been found between −0.009 and −0.231 dex kpc−1, with an average gradient of −0.06 dex kpc−1 (Zaritsky, Kennicutt & Huchra 1994). The observed metallicity difference between the two regions in Mrk 22 is too large to be explained as a normal galactic metallicity gradient. The chemical composition as measured from the gas-phase metallicity [12 + log(O/H)] shows various degree of spatial variations in different types of dwarf galaxies. For instance, shallow gradient in metallicity is seen in SBS 0335−052 (Papaderos et al. 2006) while no significant variations were seen in Mrk 35 (Cairós et al. 2007). A study on a large sample indicates that normal BCD galaxies are chemically homogeneous (Kobulnicky & Skillman 1996; Papaderos et al. 2006; Kehrig et al. 2008; Cairós et al. 2009; Pérez-Montero & Contini 2009; Pérez-Montero et al. 2011; Hägele et al. 2011; García-Benito & Pérez-Montero 2012; Lagos & Papaderos 2013). On the other hand, the metallicity of extremely metal-poor galaxies is usually not homogeneous within the galaxy, with the low metallicity seen in regions of intense star formation (Papaderos et al. 2006; Izotov & Thuan 2009; Levesque et al. 2011; Sánchez Almeida et al. 2013, 2014, 2015). However, large metallicity gradients are not common in dwarf galaxies. The simplest explanation for large metallicity difference in a single system is a recent merger of two galaxies with different metallicity. In a few cases, significantly large metallicity differences between star-forming regions in dwarf galaxies were seen and understood in terms of recent tidal interactions or mergers (López-Sánchez, Esteban & Rodríguez 2004a,b; López-Sánchez, Esteban & García-Rojas 2006; López-Sánchez & Esteban 2009, 2010). The evolution in terms of metallicities in interacting dwarf galaxies is fairly complex as it can depend on various factors such as mixing of metals with the interstellar medium (ISM), possible outflows of metals, and inflow of metal-poor gas in tidally interacting systems.

2018ApJ...863..162M__Liu_et_al._2013_Instance_2

NLFFF extrapolation provides the reconstructed coronal magnetic field for AR 11158 from 2011 February 13 − 2011 February 15 (Figures 1(d)–(f)). The field lines (yellow lines) within the core of the AR have arcade-like structure with a relatively strong twist mainly near the PIL. These figures show that the magnetic field evolved during this period. Although we did not quantitatively compare the field lines with the observation, in general, the reconstructed coronal field morphologies match with the observations in Figures 1(a)–(c). The general morphologies and the locations of the high-twist fields are also in agreement with many previous studies (Jing et al. 2012; Sun et al. 2012; Dalmasse et al. 2013; Inoue et al. 2013, 2014a; Liu et al. 2013; Wang et al. 2013; Aschwanden et al. 2014; Malanushenko et al. 2014; Zhao et al. 2014). Unlike Zhao et al. (2014) who could identify the twisted flux rope from the topology of the reconstructed coronal field, we could not find an obvious topological signature of a flux rope existing in our NLFFF during our analysis time window. It might be due to the fact that there was little magnetic flux with twist higher than one turn in our NLFFF and it is difficult to topologically define it as a flux rope. However, our result is consistent with other NLFFF results (Jing et al. 2012; Sun et al. 2012; Liu et al. 2013; Wang et al. 2013; Inoue et al. 2014a; Malanushenko et al. 2014). The high-twist region in our result is also in agreement with the region with high helicity flux (Dalmasse et al. 2013) and the location of the flare ribbons (Bamba et al. 2013; Liu et al. 2013), as well as the high current density region (Janvier et al. 2014). Figures 1(g)–(i) show the evolution of the twist distribution map, with the magnetic twist of the field lines plotted at the footpoints of field lines according to a color scale. This shows that the high-twist (strongly right-handed twist corresponding to Tw > 0.5) areas are concentrated in only a limited part of the AR. The high-twist area grew and became even more twisted just before the X2.2 flare (Figure 1(i)). Most parts of the AR have twist values less than 0.25, but near the PIL the twist can reach more than 0.5, even up to about a full turn. This is consistent with the results of Sun et al. (2012) and Inoue et al. (2014a). A high-twist (strong negative/left-handed twist) area also developed in the eastern part of the AR, which did not exist initially on February 13. Both of these high-twist areas produced several flares. However, here we focus on the flares that resulted from the high-twist core region near the center of the AR, where the M6.6 and X2.2 flares occurred.

2021AandA...655A..25Z__Shimizu_et_al._2019_Instance_1

Outflows are ubiquitous in both luminous AGN and in local Seyfert galaxies, and occur on a wide range of physical scales, from highly ionised semi-relativistic winds and jets in the nuclear region at subparsec scales to galactic scale outflows seen in mildly ionised, molecular, and neutral gas (Morganti et al. 2016; Fiore et al. 2017; Fluetsch et al. 2019; Lutz et al. 2020; Veilleux et al. 2020, and references therein). In some cases molecular and ionised winds have similar velocities and are nearly co-spatial, suggesting a cooling sequence scenario where molecular gas forms from the cooling of the gas in the ionised wind (Richings & Faucher-Giguere 2017; Menci et al. 2019). Other AGN show ionised winds that are faster than the molecular winds, suggesting a different origin of the two phases (Veilleux et al. 2020, and references therein). The molecular phase is a crucial element of the feeding and feedback cycle of AGN because it constitutes the bulk of the total gas mass and it is the site of star formation activity. On galactic scales massive molecular winds are common in local Seyfert galaxies (e.g. Feruglio et al. 2010; Cicone et al. 2014; Dasyra et al. 2014; Morganti et al. 2015; García-Burillo et al. 2014, 2017, 2019); these winds likely suppress star formation (i.e. negative feedback) as they reduce the molecular gas reservoir by heating or expelling gas from the host-galaxy ISM. In late-type AGN-host galaxies, the gas kinematics appears complex at all scales, showing several components such as bars, rings, and (warped) discs, with high velocity dispersion regions (e.g. Shimizu et al. 2019; Feruglio et al. 2020; Fernández-Ontiveros et al. 2020; Alonso-Herrero et al. 2020; Aalto et al. 2020; Audibert et al. 2020). Accurate dynamical modelling of the molecular gas kinematics reveals kinematically decoupled nuclear structures, high velocity dispersion at nuclei, trailing spirals, and evidence of inflows and AGN-driven outflows. (e.g. Combes et al. 2019; Combes 2019, 2021). The outflow driving mechanism (wind shock, radiation pressure, or jet), their multiphase nature, and their relative weights and impact on the galaxy ISM are still open problems (Faucher-Giguère & Quataert 2012; Zubovas & King 2012; Richings & Faucher-Giguere 2017; Menci et al. 2019; Ishibashi et al. 2021). To date, far different outflow phases have been observed only for a handful of sources. Atomic, cold, and warm molecular outflows have been observed in radio galaxies (e.g. Morganti et al. 2007; Dasyra & Combes 2012; Dasyra et al. 2014; Tadhunter et al. 2014; Oosterloo et al. 2017). The nuclear semi-relativistic phase and the galaxy-scale molecular phase have been observed simultaneously in less than a dozen sources, with varied results: in some cases data suggest energy driven flows (Feruglio et al. 2015; Tombesi et al. 2015; Longinotti et al. 2018; Smith et al. 2019), in other cases data suggest momentum driven flows (e.g. García-Burillo et al. 2014; Feruglio et al. 2017; Fluetsch et al. 2019; Bischetti et al. 2019; Marasco et al. 2020). Fiore et al. (2017), using a compilation of local and high redshift winds, showed that there is a broad distribution of the momentum boost, suggesting that both energy- and momentum-conserving expansion may occur. Enlarging the sample of local AGN-host galaxies with outflows detected in different gas phases is important to understand the nature and driving mechanisms of galaxy-scale outflows.

2019ApJ...885...79S__Burrows_et_al._1997_Instance_1

However, this outward migration of the inner edge should stop at the corotation radius rco, where the Keplerian frequency of the disk equals the spin frequency of Jupiter. When rcav > rco, there will be two possibilities: the angular momentum will be transferred from Jupiter to the disk and then the gas accretion will stop, or otherwise the corotation radius and the disk edge will move outward together and then the accretion will continue (Takata & Stevenson 1996; Liu et al. 2017). Although the current corotation radius is rco ≈ 2.25 RJ, Jupiter at its time of formation was much larger than it is today (Burrows et al. 1997; Fortney et al. 2011), and this means that the corotation radius was also larger than the current one if the conservation of the angular momentum of Jupiter is assumed. Considering the transport of the angular momentum from Jupiter to the disk, the angular momentum should have been conserved since the disk disappeared. According to a formation model of Jupiter, the radius of the planet was ≈1.75 RJ after its rapid gas accretion and it decreased little by little (Lissauer et al. 2009). When the radius of Jupiter is 1.75 RJ, the corotation radius should be rco ≈ 4.7 RJ. We can then consider two scenarios of Io formation. In the first one, Io formed around r ≈ 4.7 RJ, slightly interior to the r = 5.89 RJ of our fiducial model, and then moved outward after the disk dissipated. The satellites, especially the inner ones, could move outward by the tidal force from Jupiter (Yoder & Peale 1981). The outer ones would be pushed by the inner ones and move outward with them because of the resonance. In this case, the position of the snow line should have been more inside than the fiducial case in this work, but this thermal condition could easily be reproduced by another parameter set. The second possibility is that Io was not the innermost satellite. If a body was present at r = 3.7 RJ, Io would have been situated at r = 5.7 RJ if they were trapped in a 2:1 resonance. This orbit is consistent with the corotation radius when the radius of Jupiter is ≈1.5 RJ, and this radius can be achieved during the contraction of Jupiter. The innermost body may have been broken by the tidal force of Jupiter when it has entered inside the Roche limit. Current Io, trapped in the Laplace resonance, actually moves inward little by little because of the tidal dissipation, and the innermost body may have also experienced such inward migration (Lainey et al. 2009).

2021ApJ...911...89M__Mozer_et_al._2020a_Instance_1

Time domain structures (TDSs; electrostatic or electromagnetic electron holes, ion holes, solitary waves, double layers, nonlinear whistlers, etc.) are ∼1 ms pulses having significant electric fields parallel to the background magnetic field (Mozer et al. 2015). They are abundant through space, occurring along auroral zone magnetic field lines (Temerin et al. 1982; Mozer et al. 1997; Ergun et al. 1998), in the magnetospheric tail and plasma sheet (Cattell et al. 2005; Tong et al. 2018; Lotekar et al. 2020), at reconnection sites (Cattell et al. 2005; Steinvall et al. 2019; Lotekar et al. 2020), in the solar wind (Mangeney et al. 1999; Malaspina et al. 2013), in collisionless shocks (Wilson et al. 2010; Vasko et al. 2020; Wang et al. 2020), and in the magnetospheres of other planets (Pickett et al. 2015). TDSs are also expected along the Parker Solar Probe orbit (Mozer et al. 2020a). According to theoretical estimates and simulations (Cranmer & van Ballegooijen 2003; Valentini et al. 2011, 2014), these nonlinear structures can provide thermalization of electron and ion beams produced in the course of the turbulence cascade development at scales the order of the electron inertial length and down to the Debye length. This paper discusses such observations at a heliocentric distance of 35 solar radii. The electric field experiment on the Parker Solar Probe measures electric fields from DC to 20 MHz. A general description of the instrument and its electronics appears elsewhere (Bale et al. 2016). In this paper, the data from DC to 2 MHz are discussed. These measurements are obtained from the potentials of the four antennas, V1 through V4, that are located in the plane perpendicular to the Sun–satellite line. They produce E12 = (V1−V2)/3.5 and E34 = (V3−V4)/3.5, which are then rotated into the spacecraft coordinate system to produce EX and EY. The direction, X, is perpendicular to the Sun–spacecraft line, in the ecliptic plane, and pointing in the direction of solar rotation (against the ram direction), Y is perpendicular to the ecliptic plane, pointing southward, and Z points toward the Sun. The numerical factor, 3.5, is the effective antenna length (Mozer et al. 2020a). The uncertainties of the amplitudes of the waves and time domain structures reported in this paper are estimated to be about a factor of 2. These amplitudes are underestimated because the capacitive divider that couples the antennas to the electronics decreases the measured electric field relative to that on the antennas. They are often overestimated because short antennas produce overestimates of the electric field by factors of 2–4, as was observed during antenna deployment on the Cluster satellite and as is observed on the Parker Solar Probe from the ratio of the electric field to the magnetic field in whistlers.

2022AandA...662A..42M__Nóbrega-Siverio_et_al._2020a_Instance_1

In this paper, we are interested not only in the mathematical properties of the ambipolar diffusion as a nonlinear diffusion process, but also in the inclusion of ambipolar diffusion terms in MHD codes. In astrophysics, over the past few decades, multidimensional MHD computer codes have been developed that model a variety of physical processes including ambipolar diffusion. Representative examples of such codes and simulations outside solar physics can be found in Basu & Mouschovias (1994), Mac Low et al. (1995), Padoan et al. (2000), Basu & Ciolek (2004), Kudoh & Basu (2008), Choi et al. (2009), Gressel et al. (2015), Tomida et al. (2015), O’Sullivan & Downes (2007), Masson et al. (2012), Viganò et al. (2019), and Grassi et al. (2019). The consideration of ambipolar diffusion processes in solar physics started many decades ago (e.g. Parker 1963), but it has undergone a true explosion in terms of its use in large numerical models (e.g. Leake et al. 2005; Leake & Arber 2006; Arber et al. 2007; Cheung & Cameron 2012; Leake & Linton 2013; Martínez-Sykora et al. 2012, 2017a,b, 2020a,b; Ni et al. 2015, 2016, 2021; Khomenko et al. 2017, 2018, 2021; González-Morales et al. 2018, 2020; Nóbrega-Siverio et al. 2020a,b; Popescu Braileanu & Keppens 2021). Such numerical calculations often encounter a problem: given the comparatively high values of χa in different cosmic environments, the advance in time may grind to a halt in magnetised regions when a standard Courant-Friedrichs-Lewy condition is adopted for the timestep based on ηa. The recent papers by González-Morales et al. (2018) and Nóbrega-Siverio et al. (2020b) describe the construction of so-called super-time-stepping (STS) modules for the Mancha code and for the Bifrost code, respectively, designed with the aim to overcome that stiffness problem. In at least three of the papers cited above (Masson et al. 2012; Viganò et al. 2019; Nóbrega-Siverio et al. 2020b), the basic ZKBP solution in cylindrical coordinates was used to test the ambipolar diffusion module, given its simplicity and the analytical expression available for it. However, as already mentioned, the ambipolar diffusion problem tends to give rise to sharp current sheets and singularities. The ZKBP solution is comparatively smooth in that sense and it would be good to have some other canonical solutions on hand that include current sheets having higher degrees of singularity, which naturally occur in the ambipolar diffusion problem.

2020AandA...643A..35P__Irwin_et_al._2007_Instance_2

In order to achieve high photometric accuracy and be sensitive to low amplitude undulations, we adopted techniques from the exoplanet community, with the purpose of eliminating the systematic errors. When performing differential photometry (Sect. 3), accurate bias-subtraction and flat-fielding are of major importance. According to Irwin et al. (2007), the Poisson noise is 200 e− for a typical detector with a gain of a few e− ADU−1 and the flat illumination level is of 20 000 ADU pixel−1 = 40 000 e− pixel−1. Thus, a typical photometric aperture with a radius of 3 pixels contributes ∼1 mmag photon noise. For this reason, we obtained a considerable amount of biases (150−300 frames) and twilight flat-fields (25−100 frames) each night to reduce the Poisson noise to less than 0.2 mmag (Irwin et al. 2007). The bias frames were averaged together using the minmax in the reject option of the zerocombine task in IRAF with a view of keeping radiation events out of the master bias frame. The master flat frame was the result of combining all the frames using a median mode. The median value is an excellent way of removing the effects of hot pixels and cosmic rays, so these extreme values do not affect the calculation, as they would, if they would averaged. The reject option was set to avsigclip, in which case the “typical” sigma would have been determined from the data itself rather than an a priori knowledge of the noise characteristics of the CCD. Other related issues that can limit the photometric precision are: (i) the positioning of the telescope, (ii) fringing issues, and (iii) the differential variations on the quantum efficiency of the pixels. With the aim of minimizing the contribution of these effects, we repositioned each star almost on the same pixel of the detector using the autoguiding system of each telescope. The read-out-noise of the detectors are insignificant, as it can be as low as a few e− ( 10 e− for RISE2 and Andor Zyla cameras).

2022MNRAS.509.3339K___2011_Instance_1

Semi-analytical disc models indicate that the frequency of giant planets must increase with the mass of the host star between 0.2–1.5 M⊙ (Ida & Lin 2005; Kennedy & Kenyon 2008). However, this trend is expected to decrease above 1.5 M⊙ due to a smaller growth rate, longer migration time-scale, and shorter lifetime of the protostellar disc (Reffert et al. 2015). Looking for planets around main sequence stars more massive than the sun can help shed some light on this aspect. These stars have few spectral lines for Doppler measurements and are often broadened by the rapid rotation of the star. This has been the reason for RV surveys to have traditionally targeted slow rotating FGK type stars. However, as rapidly rotating stars evolve off the main sequence they slow down considerably and become much cooler making it relatively easy to search for planets around them. This fact was exploited by dedicated planet searches around intermediate mass sub-giants leading to dozens of planet discoveries (Johnson et al. 2007, 2010a, 2011). An important result obtained from the survey of giant stars at the Lick observatory pointed out that the occurrence rate peaks at a stellar mass of $1.9^{+0.1}_{-0.5}$ M⊙. However, many of the discovered planets around evolved stars were found at large orbital separations (Hatzes et al. 2003; Fischer et al. 2007; Robinson et al. 2007; Johnson et al. 2008). This is not a surprise as star–planet interaction is largely governed by tidal forces. When the stellar rotational period is longer than the planet orbital period, the star experiences spinning up, leading to orbital decay. Synchronization and circularization of orbit occurs in systems where the total angular momentum exceeds a critical value. When this total angular momentum is small enough, the orbit of the planet can continue to shrink and be engulfed by the host star. This phenomenon entirely depends on the dissipation time-scales for the star (Mazeh 2008, and references therein). The role of tidal forces becomes increasingly important in the context of host stars being in an evolved state. There is a higher chance of the planet being destroyed by the evolved star (Kunitomo et al. 2011; Schlaufman & Winn 2013). However, there is no obvious way to estimate these tidal dissipation forces. The circularization time-scale for such planets can be used to quantify tidal dissipation inside planets (Hansen 2010; Socrates et al. 2012). Most of the discovered hot Jupiters with periods less than 3 d are found to be on circular orbits. We calculate the circularization time-scales for the orbit of TOI-1789 b, which is τcir = 0.08 Gyr (for QP = 106, equation 3 of Adams & Laughlin 2006). This is less than the age of the star as calculated from our work (Section 3.3.1).

2022MNRAS.515..185O__Gutcke_et_al._2022_Instance_1

While gas cooling and stellar feedback can transform dark matter cusps to cores, it is energetically challenging for this process to create large dark matter cores (typically >500 pc) in the very smallest ‘ultra-faint’ dwarfs, since they form so few stars (M* 105 M⊙; Peñarrubia et al. 2012; Garrison-Kimmel et al. 2013; Di Cintio et al. 2014; Maxwell, Wadsley & Couchman 2015; Oñorbe et al. 2015; Tollet et al. 2016). However, smaller dark matter cores may still form inside the half-light radius (R1/2 ∼ 20–200 pc for UFDs2), where the gravitational potential fluctuations are strongest (e.g. Oñorbe et al. 2015; Read et al. 2016). Whether this is expected to happen in a ΛCDM cosmology remains an active area of debate. Most studies to date find that cusp-core transformations are challenging at the likely mass-scale of UFDs (M200c ∼ 109 M⊙; M* ∼ 105 M⊙; e.g. Chan et al. 2015; Wheeler et al. 2019a; Gutcke et al. 2022). However, there are some notable exceptions. In recent work, Orkney et al. (2021) studied cusp-core transformations for UFDs drawn from the ‘Engineering Dwarfs at Galaxy formation’s Edge’ (EDGE) simulation project (with a mass, baryonic mass, and spatial resolution of 120 M⊙, 20 M⊙, and 3 pc, respectively, sufficient to resolve dark matter cores in UFDs larger than ∼20 pc). They found that UFDs can lower their inner dark matter density by up to a factor ∼2 through a combination of early heating due to star formation, followed by late-time heating from minor mergers. While none of their simulated dwarfs formed a completely flat central core, their small sample size left open the possibility that this combination of mechanisms could form flatter cores for some rarer assembly histories. It is also important to note that dark matter core formation on these mass scales is very sensitive to small changes in the star formation and feedback modelling. Pontzen et al. (2021) showed that a small increase in variability of the star formation rate on time-scales shorter than the local dynamical time is sufficient to form a full dark matter core in an ultra-faint, without significantly altering its stellar mass. As such, the question of whether complete dark matter core formation is expected in some or all UFDs in ΛCDM remains open.

2018ApJ...854..137S__Manuel_et_al._2014_Instance_1

Ulysses was launched on 1990 October 6 and orbited the Sun with a latitude varying from −80° to 80° and a solar distance ranging from ∼1 au to ∼5 au (Heber et al. 2009). The Kiel Electron Telescope (KET) on board Ulysses measured electrons in the energy range from ∼3 MeV to above 300 MeV, and protons and helium nuclei in the energy range from ∼5 MeV/nuc to above 2 GeV/nuc (Simpson et al. 1992). The data of the KET coincidence channel K12, which measures protons with energy in 0.25–2.0 GeV, has been used to study the GCR modulation outside the solar ecliptic plane in many works (see, e.g., Ndiitwani et al. 2005; Vos & Potgieter 2016; Boschini et al. 2017a). However, different works used different mono-energetic bins to represent this channel, e.g., 1.08 GeV (Rastoin et al. 1996), 2.5 GV (i.e., 1.73 GeV, Ndiitwani et al. 2005; Heber et al. 2009; Manuel et al. 2014), and 2.2 GeV (Boschini et al. 2017a). As the KET observations are integrated over a large energy interval (de Simone et al. 2011), it is perhaps better to weight the model results of several energy bins with the Ulysses response function and then combine them (Boschini et al. 2017a). Heber et al. (2009) obtained the 1 au equivalent count rates for this channel by correcting the proton intensity for the global spatial gradients of GCR protons, and the 1 au equivalent GCR proton flux can be obtained with the corresponding response factor. In addition, the precise cosmic-ray spectra measured by the PAMELA instrument can help us to roughly estimate the effective energy of the KET coincidence channel K12. In Figure 8, the black solid line shows the monthly averaged 0.25–2.0 GeV proton flux observed by Ulysses. Note that the original daily count rates from the Ulysses Final Archive (ufa.esac.esa.int/ufa) are divided by the corresponding response factor to obtain the proton flux. The dash-dotted line represents the 1 au equivalent 0.25–2.0 GeV proton flux, and the relevant count rates are digitized from Figure 5 in Heber et al. (2009). The 1.2 GeV proton flux observed by SOHO/EPHIN (Kühl et al. 2016) and PAMELA (Adriani et al. 2013) is shown as magenta triangles and green circles, respectively. The 1 au equivalent GCR proton flux roughly matches the 1.2 GeV GCR proton observations, and 1.2 GeV can be used to represent the Ulysses/KET channel K12. Therefore, we compute the 1.2 GeV proton flux along the trajectory of Ulysses to compare it with the Ulysses K12 measurements.

2015MNRAS.452.2731S__Stroe_et_al._2013_Instance_4

The H α studies of Umeda et al. (2004) and Stroe et al. (2014a, 2015) are tracing instantaneous (averaged over 10 Myr) SF and little is known about SF on longer time-scales and the reservoir of gas that would enable future SF. An excellent test case for studying the gas content of galaxies within merging clusters with shocks is CIZA J2242.8+5301 (Kocevski et al. 2007). For this particular cluster unfortunately, its location in the Galactic plane, prohibits studies of the rest-frame UV or FIR tracing SF on longer time-scales, as the emission is dominated by Milky Way dust. However, the rich multiwavelength data available for the cluster give us an unprecedented detailed view on the interaction of their shock systems with the member galaxies. CIZA J2242.8+5301 is an extremely massive (M200 ∼ 2 × 1015 M⊙; Dawson et al. 2015; Jee et al. 2015) and X-ray disturbed cluster (Akamatsu & Kawahara 2013; Ogrean et al. 2013, 2014) which most likely resulted from a head-on collision of two, equal-mass systems (van Weeren et al. 2011; Dawson et al. 2015). The cluster merger induced relatively strong shocks, which travelled through the ICM, accelerated particles to produce relics towards the north and south of the cluster (van Weeren et al. 2010; Stroe et al. 2013). There is evidence for a few additional smaller shock fronts throughout the cluster volume (Stroe et al. 2013; Ogrean et al. 2014). Of particular interest is the northern relic, which earned the cluster the nickname ‘Sausage’. The relic, tracing a shock of Mach number M ∼ 3 (Stroe et al. 2014c), is detected over a spatial extent of ∼1.5 Mpc in length and up to ∼150 kpc in width and over a wide radio frequency range (150 MHz–16 GHz; Stroe et al. 2013, 2014b). There is evidence that the merger and the shocks shape the evolution of cluster galaxies. The radio jets are bent into a head–tail morphology aligned with the merger axis of the cluster. This is probably ram pressure caused by the relative motion of galaxies with respect to the ICM (Stroe et al. 2013). The cluster was also found to host a high fraction of H α emitting galaxies (Stroe et al. 2014a, 2015). The cluster galaxies not only exhibit increased SF and AGN activity compared to their field counterparts, but are also more massive, more metal rich and show evidence for outflows likely driven by SNe (Sobral et al. 2015). Stroe et al. (2015) and Sobral et al. (2015) suggest that these relative massive galaxies (stellar masses of up to ∼1010.0–10.7 M⊙) retained the metal-rich gas, which was triggered to collapse into dense star-forming clouds by the passage of the shocks, travelling at speeds up to ∼2500 km s−1 (Stroe et al. 2014c), in line with simulations by Roediger et al. (2014).

2019MNRAS.490.5478W__Kurtovic_et_al._2018_Instance_1

A growing body of work suggests that planet formation is strongly dependent on the birth environment of the host star. Stars preferentially form in groups (Lada & Lada 2003), and in sufficiently dense environments the evolution of a PPD can be significantly influenced by neighbours (de Juan Ovelar et al. 2012). Close star–disc encounters are one such environmental influence on PPDs that can result in enhanced accretion and hasten disc depletion (Clarke & Pringle 1993; Ostriker 1994; Pfalzner et al. 2005; Olczak, Pfalzner & Spurzem 2006; Bate 2018; Winter et al. 2018a; Cuello et al. 2019). However, the stellar number densities required for tidal truncation are high, and in practice few observed regions satisfy this condition (Winter et al. 2018b, 2019a). The influence of tidal truncation is therefore limited to stellar multiples, either in bound systems (Dai et al. 2015; Kurtovic et al. 2018) or during the decay of higher order multiplicity (Winter, Booth & Clarke 2018c). Since stellar multiplicity does not appear to be strongly dependent on environment (see Duchêne & Kraus 2013, for a review), this suggests that encounters are not an environmental influence, but may set disc initial conditions during the early phases of cluster evolution (Bate 2018). Discs can also be externally depleted via thermal winds driven by far-ultraviolet (FUV) and extreme ultraviolet (EUV) photons from neighbouring massive stars (Johnstone, Fabian & Taylor 1998; Störzer & Hollenbach 1999; Adams et al. 2004; Facchini, Clarke & Bisbas 2016; Haworth et al. 2018; Haworth & Clarke 2019). This process of external photoevaporation dominates over dynamical encounters in observed environments, and can deplete PPDs rapidly for many stars that are born in massive and dense clustered environments (Scally & Clarke 2001; Winter et al. 2018b). Many stars in the solar neighbourhood are born in regions where UV fields are sufficient to significantly shorten disc lifetimes (Fatuzzo & Adams 2008; Winter et al. 2018b), and the fraction of stars born in such environments may be much greater outside of this region, dependent on galactic environment (Winter et al. 2019a). From an observational perspective, Guarcello et al. (2016) report disc survival fractions that decrease with increasing FUV flux in Cygnus OB2 (see also Winter, Clarke & Rosotti 2019b), and Ansdell et al. 2017 find a correlation between the dust mass in PPDs and separation from σ Ori. However, Richert et al. (2015) find no correlation of disc fraction with distance from OB stars. Reconciling these contradictory findings may require appealing to the inefficiency of external photoevaporation at small radii within the disc, dynamical and projection effects, or the stellar age gradient apparent in many star forming regions (Getman et al. 2018).

2020MNRAS.494.5110B__Troja_et_al._2018_Instance_2

Following the short gamma-ray burst (sGRB) associated with this event, GRB 170817A (Abbott et al. 2017a,b; Goldstein et al. 2017), radio emission was anticipated as the associated merger outflow interacted with the circum-merger medium. Monitoring the radio emission could therefore provide crucial information on the energetics and geometry of the outflow, as well as the ambient environment. At radio frequencies, telescopes were observing the Advanced LIGO–Virgo probability region for GW170817 within 29 min post-merger (Callister et al. 2017a), and subsequent monitoring of AT 2017gfo resulted in an initial radio detection 16 d after the event (Abbott et al. 2017a; Hallinan et al. 2017). Further monitoring, predominantly at frequencies between 0.6 and 15 GHz, has since taken place (e.g. Alexander et al. 2017, 2018; Corsi et al. 2018; Dobie et al. 2018; Margutti et al. 2018; Mooley et al. 2018a,b,c;Resmi et al. 2018; Troja et al. 2018, 2019). At these frequencies, a general picture emerged in which the radio light curve was first observed to steadily rise, before it turned over and began a more rapid decay. Using a compilation of 0.6–10 GHz radio data from 17 to 298 d post-merger, Mooley et al. (2018c) derived both a fitted time for the radio peak of 174$^{+9}_{-6}$ d and a fitted 3-GHz peak flux density of 98$^{+8}_{-9}\, \mu$Jy (also see similar analyses in Dobie et al. 2018 and Alexander et al. 2018). The fitted radio spectral index α1 from this study is −0.53 ± 0.04, consistent with broad-band spectral indices determined using radio, optical, and X-ray data at various epochs, where the typical value is approximately −0.58 (e.g. Alexander et al. 2018; Margutti et al. 2018; Troja et al. 2018, 2019; Hajela et al. 2019). Mooley et al. (2018c) also found power-law dependencies for the rise and decay phases of approximately t0.8 and t−2.4, respectively, where t is the time since the merger. Within the associated uncertainties, these results are consistent with the broad-band evolution of AT 2017gfo (e.g. Alexander et al. 2018; Hajela et al. 2019; Lamb et al. 2019; Troja et al. 2019).

2021ApJ...922..224L__Jain_et_al._2015_Instance_1

Theoretically, coronal loops are made up of magnetically confined denser and hotter plasma, and can thus support modes of oscillations (Aschwanden 1987; Roberts 2008). These oscillations are closely related to the physical properties of their host coronal loops and may open a new window for detecting the inhomogeneous corona (Nakariakov & Verwichte 2005; Yuan & Van Doorsselaere 2016a; Li & Liu 2018). Theories of magneto-acoustic oscillations in coronal loops have indicated that the fast kink mode possesses both a unique dispersion relation and zero asymptotic dispersion for a long-wave approximation, and could provide a potential diagnostic tool for determining physical properties of the inhomogeneous corona (Edwin & Roberts 1983; Roberts et al. 1984). Due to the specific role of coronal loops’ fast kink oscillations in coronal seismology, the proposal of using the fast kink oscillations to diagnose the physical properties of their host coronal loops has attracted a great deal of attention since it was first introduced (Roberts et al. 1984). Along with an increasing number of observational cases, the study of fast kink oscillations in coronal loops has been significantly advanced (Aschwanden et al. 1999; Nakariakov et al. 1999; Verwichte et al. 2004; Aschwanden & Schrijver 2011; Wang et al. 2012; Jain et al. 2015; Yuan & Van Doorsselaere 2016b; Shen et al. 2017; Pascoe et al. 2018; Shen et al. 2018; Nechaeva et al. 2019; Anfinogentov & Nakariakov 2019). Numerous detailed observations, on the other hand, have also brought new challenges to the current theory (De Moortel & Brady 2007; Guo et al. 2015; Li et al. 2017; Su et al. 2018; Li et al. 2019). Among these challenges, a prominent one is the increasingly presented oscillating coronal loops whose frequencies show significant changes, which is also known as the frequency drift (Su et al. 2018). It indicates that the frequency drift for the oscillation in fast kink mode can occur in a quiet loop (Su et al. 2018) or a quiet fiber (Li et al. 2018). It also indicates that the brightness of the oscillating structures undergoes a significant change during the occurrence of the frequency drift, which may imply changes in the loop’s thermal properties. A detailed investigation present in Table 1 and Figure 5 of Su et al. (2018) indicates that the period of fast kink oscillation has an ≈25% increase corresponding to an ≈40% increase in the loop’s density. A decrease in the period of fast kink oscillation is also observed by Li et al. (2018). It is also worth noting that the oscillation reported by Li et al. (2018) shows a significant increase of the amplitude as the period decreases, which is very unusual.

2017ApJ...837...97L__Grillo_et_al._2015_Instance_1

The newly discovered arcs and new spectroscopic redshifts have been incorporated into updated HFF+ versions of the Abell 2744 and MACSJ0416.1-2403 lensing models (Table 5); many of these have (see Figure 8 for a comparison of the arc redshift distributions adopted by the pre-HFF and new HFF+ lensing models; Cypriano et al. 2004; Okabe & Umetsu 2008; Zitrin et al. 2009; 2013; Okabe et al. 2010a, 2010b; Merten et al. 2011; Christensen et al. 2012; Mann & Ebeling 2012; Jauzac et al. 2014; Lam et al. 2014; Richard et al. 2014; Balestra et al. 2015; Diego et al. 2015; Grillo et al. 2015; Jauzac et al. 2015; Rodney et al. 2015; Wang et al. 2015; Kawamata et al. 2016). The incorporation of these new multiple image systems often results in a reduction in the statistical uncertainty in the galaxy magnifications for a given model. All of the public HFF lensing models provide a range of possible realizations from which the statistical uncertainty of a given model set may be calculated (typically 100 but no fewer than 30). We plot the cumulative distribution of the galaxy magnification uncertainties σ(model)/ , for the galaxies and photometric redshifts provided by the ASTRODEEP catalogs (Merlin et al. 2016; Castellano et al. 2016a) for Abell 2744 (Figure 9) and MACSJ0416.1-2403 (Figure 10). Generally, the statistical uncertainties are reduced for the models computed with the new HFF data sets, with more dramatic reductions for the methods that rely strongly upon the strong-lensing constraints. The parametric methods (CATS, Sharon, Zitrin, GLAFIC) report median statistical magnification errors of 0.2%–5%, while the non-parametric methods (Bradac Williams, Diego) report median statistical magnification errors of 2%–11% for the post-HFF calculations (green curves), versus 2%–22% and 2%–17% respectively for pre-HFF models (blue curves). (We note that the statistical errors for the MACSJ0416.1-2403 Bradac post-HFF models (Hoag et al. 2016) included additional uncertainties due to the photometric redshift uncertainties of the multiple images. These were not included in the pre-HFF Bradac model, and thus may explain why the post-HFF statistical errors are larger for this model.)

2022AandA...666A.190S__Velichko_et_al._1995_Instance_1

For our dataset of absolute magnitudes, we used data collected at the Institute of Astronomy of V. N. Karazin Kharkiv National University within the long-term observational programme to study asteroid magnitude-phase curves (Shevchenko et al. 2010, 2012, 2014a, 2016; Slyusarev et al. 2012). We also used some observational data obtained within several other programmes (Belskaya et al. 2010; Chiorny et al. 2007, 2011; Dotto et al. 2009; Hahn et al. 1989; Kaasalainen et al. 2004; Lagerkvist et al. 1998; Michalowski et al. 1995; Mohamed et al. 1994, 1995; Oszkiewicz et al. 2021; Shevchenko et al. 1992, 2003, 2009, 2014b, 2021; Velichko et al. 1995; Wilawer et al. 2022). All magnitudes were measured in the Johnson V band and extrapolated to zero phase angle using the HG1G2 system proposed by Muinonen et al. (2010), with some modifications presented by Penttilä et al. (2016). For computations, the online calculator1 for the HG1G2 photometric system was used. Since we derived absolute magnitudes in our data from the light curve maxima, and the definition of H is based on the rotationally averaged brightness, we added a half of the light curve amplitude corrected to zero phase angle to our results. We used the average correction coefficients from Zappala et al. (1990) for low- and moderate-albedo asteroids. This correction is typically very small because our light curve observations covered small phase angles. Absolute magnitudes obtained at different aspects were averaged. In such a manner, we obtained a homogeneous dataset of absolute magnitudes of about 400 asteroids up to H = 16.5 mag. Our database includes the absolute magnitude data, the G1 and G2 parameters, and the albedo and diameter values from different databases (such as Tedesco et al. 2002; Masiero et al. 2011, 2012; Nugent et al. 2015; Usui et al. 2011). The database is available at the CDS. Figure 1 shows the correlations of the absolute magnitudes from the largest datasets (MPC (HMPC), Pan-STARRS (HPS), and ATLAS (HATLAS)) with those of the Kharkiv dataset (HKH). For the ATLAS dataset, we used the absolute magnitudes in a cyan filter, since this filter overlaps the Johnson V band (Mahlke et al. 2021).

2015MNRAS.451.4290S__Governato_et_al._2004_Instance_1

Hydrodynamical simulations of evolving galaxies allow us to calibrate these diagnostics by measuring their observability given a set of formation scenarios and physical processes (e.g. Jonsson et al. 2006; Rocha et al. 2007; Lotz et al. 2008a; Bush et al. 2010; Narayanan et al. 2010; Hayward et al. 2013; Snyder et al. 2013; Lanz et al. 2014). The quality and breadth of these experiments are limited by the availability of computational resources and the fidelity of models for galaxy physics such as star formation, supernovae, and the interstellar medium (ISM). It has only recently become widespread to model the formation of galaxies ab initio (e.g. Governato et al. 2004; Agertz, Teyssier & Moore 2011; Guedes et al. 2011; Marinacci, Pakmor & Springel 2013; Ceverino et al. 2014), and the realism continues to improve (Stinson et al. 2012; Hopkins et al. 2014; Torrey et al. 2014), albeit with still widely varying physics models (e.g. Scannapieco et al. 2012; Kim et al. 2014). Prior to these advances, studies were limited to small numbers of isolated galaxies or mergers to inform common diagnostics of galaxy evolution, an approach with a significant limitation: they do not fully account for cosmological context, such as gas accretion and the breadth of assembly histories. In addition to mergers, models of high-redshift galaxy formation (e.g. Dekel, Sari & Ceverino 2009; Dekel et al. 2013) have recently appreciated the tight coupling between gas accretion and disc evolution (e.g. Cacciato, Dekel & Genel 2012; Danovich et al. 2012; Dekel & Krumholz 2013), as well as bulge and super-massive black hole (SMBH) growth mediated by turbulent motions or violent disc instability (e.g. Bournaud et al. 2011; Porter et al. 2014) and the evolution of giant clumps (Dekel & Burkert 2013). These important processes likely complicate interpretation of a given observation, and recent studies of galaxy morphology have begun to exploit simulations including them (e.g. Scannapieco et al. 2010; Pedrosa, Tissera & De Rossi 2014).

2022MNRAS.515.5495M__Genel_2016_Instance_1

The stellar metallicity in the Universe evolves with redshift (Mannucci et al. 2010; Sommariva et al. 2012; Krumholz & Dekel 2012; Dayal, Ferrara & Dunlop 2013; Madau & Dickinson 2014). The metallicity at a high redshift (z > 2) is much smaller in comparison to the low redshift Universe z 2. The first-generation stars contaminate the interstellar medium and cause a chemical evolution of the Universe. We can treat the metallicity evolution with redshift by a relation (2)$$\begin{eqnarray*} \log _{10}(Z(z))= \gamma z +\zeta , \end{eqnarray*}$$where γ captures the redshift dependence and ζ captures the metallicity value at z = 0 (Mannucci et al. 2010; Madau & Dickinson 2014). This relation captures the metallicity of the parent star or the gas cloud from which a star has formed. It is written to express only a mean evolution of the metallicity. Along with the mean metallicity evolution of the Universe, there is going to be a scatter in the metallicity depending on the galaxy properties. Such a source of uncertainty brings additional stochasticity to the metallicity relation. Currently, a limited number of observations (Gallazzi et al. 2008; Mannucci et al. 2010; Krumholz & Dekel 2012) are available to explore the environment dependence of the metallicity, and most of our current understandings are based on simulations(Genel 2016; Torrey et al. 2019). These studies show that the overall median metallicity dependence of the galaxies at different redshifts can be explained by power form (Pei, Fall & Hauser 1999; Young & Fryer 2007; Torrey et al. 2019). Several studies of GW merger rates and mass distribution are performed (Belczynski et al. 2002; Dominik et al. 2012, 2015; Mapelli et al. 2017; Giacobbo, Mapelli & Spera 2018; Toffano et al. 2019; van Son et al. 2022) which are motivated by these studies and show that the black hole mass distribution can exhibit a redshift dependence. The existence of any stochasticity in the galaxy metallicity distribution will also influence the mass distribution but is currently not well known. However, as the relation given in equation (1) is in terms of the logarithm of metallicity, so the impact of fluctuation around the median value depending on the individual galaxy properties is going to be a small (logarithmic) change. As we are unable to measure the host of the BBH due to a large sky localization error of the BBH, we cannot directly associate the properties of galaxies with BBH source properties. So, we can only infer an ensemble average mass distribution from the GW data and the additional stochasticity (which will depend on the host properties) will appear as an additional uncertainty in the measurement of MPISN. As a result, we consider a median distribution of galaxy metallicity and the dependence of MPISN on it.

2022AandA...664A.127S__Lefebvre_et_al._2008_Instance_1

Efforts have been made to model the signals caused by different sources of stellar variability within the RV time series (e.g., Tuomi et al. 2013; Rajpaul et al. 2015; Davis et al. 2017; Simola et al. 2019). Several solutions have been successfully proposed in order to deal with stellar oscillations and granulation phenomena, such as: calculating stellar evolution sequences (e.g., Christensen-Dalsgaard et al. 1995); fitting a two-level structure tracking (TST) algorithm based on a two-level representation of granulation (Del Moro 2004); using daytime spectra of the Sun in order to measure the solar oscillations (e.g., Kjeldsen et al. 2008; Lefebvre et al. 2008); and characterizing the statistical properties of magnetic activity cycles focusing on HARPS observations (e.g., Pepe et al. 2011; Dumusque et al. 2011b). However, properly modeling the other sources of stellar activity remains extremely challenging (e.g., Nava et al. 2019). In the present work, we deal with the cross correlation function (CCF) that is derived from the stellar spectrum (e.g., Hatzes 1996; Hatzes & Cochran 2000; Fiorenzano et al. 2005). As it is well known, the CCF barycenter estimates the RV of the star. The asymmetry and the full width at half maximum (FWHM) of the CCF give a strong indication for stellar activity, meaning that variations in RV are caused by active regions rather than by an exoplanet (e.g., Hatzes 1996; Queloz et al. 2001; Boisse et al. 2011; Figueira et al. 2013; Simola et al. 2019). Several solutions have been successfully proposed for mitigating stellar activity perturbations when working with RV measurements, including: decorrelating RV data against activity indicators such as log ${{R'}_{{\rm{HK}}}}$R′HK (e.g., Wilson 1968; Noyes et al. 1984) or Hα (e.g., Robertson et al. 2014); modeling stellar activity by fitting Gaussian processes (GPs, Rasmussen & Williams 2005; Haywood et al. 2014; Rajpaul et al. 2015); or moving averages (e.g., Tuomi et al. 2013) to the RV data. A common statistic employed for identifying changes in the shape of the CCF is the bisector span (e.g., Hatzes 1996; Queloz et al. 2001).

2021MNRAS.507.5567D__Guo_et_al._2019_Instance_1

The source shows some preferred tracks in its movement along the HID. Dips occur as the source is in the SUL regime, either from SUL2 or from SUL3 regions; ingress and egress times populate the D3 and D2 spectra, whereas the D1 spectrum comes from the time segments characterized by the lowest count rate (deep dip). We noted that during the longest dips, the source occasionally switched to harder flaring episodes in D2 state. Most dips show the following pattern: SUL2/SUL3 → D3 → D2 → D1 → D2 → D3 → SUL2/SUL3. The passage from the SSUL to SUL state is only occasionally observed. We selected seven regions on the HID and extracted the corresponding spectra. For the normal branch spectra, the continuum emission is well fitted with two thermal components: a soft blackbody and a disc multicolour blackbody. The blackbody emission represents the bulk of the emitted power in each spectrum. It is likely due to strong reprocessing in an optically thick environment formed at Rsph, where disc inflow is mainly inflated by internal radiation pressure. This picture is consistent with the observed low temperature, large radius, and super-Eddington luminosity (see Shen et al. 2015; Soria & Kong 2016; Urquhart & Soria 2016; Guo et al. 2019). The hotter component, which we fitted using a diskbb, dominates the emission above 2 keV. Although its origin is still debated, it might come from internal hard X-ray emission, which has been inefficiently reprocessed, or simply scattered along our line of sight. Alternatively, it can be continuum emission (bremsshtrahlung and/or Comptonization) from an extended optically thin plasma where the emission lines are produced, or a tail of the blackbody emission which has been Compton upscattered in a coronal environment around the photosphere. In addition to the continuum, we added multiple emission and absorption lines derived by the combined averaged PN / RGS analysis to mimic the emitting and absorbing plasmas found in Paper I. Their shifts suggest different Doppler motions in several states as a consequence of a velocity field which changes depending on the launching site and on the geometry of the system. This seems supported by correlations among the parameters of the emission lines and the underneath hard X-ray flux (see Table 3). However, given the limited energy resolution of the EPIC we are not able to distinguish between a varying ionization state of the plasma, the effect of a different line broadening for the different ionized species, a complex absorption/emission pattern. A thorough study of the lines is left to a dedicated forthcoming study. For consistency the same spectral model has been applied also to the dipping branch spectra, although the physical conditions in and out of the dips might be different.

2021MNRAS.500..786D__Delgado_&_Perez_1997_Instance_1

xstar predicts O to be progressively ionized through all its possible ionization stages as the central UV-X-ray source is approached providing a natural explanation for the absence of broad forbidden ${[}{\rm O\, \small{III}}{]}$ emission lines. No $[{\rm O\, \small{II}}]\, {\lambda }\, {\lambda }$3727, 3729 emission lines appear in the HST spectra, consistent with the xstar model. Furthermore, both the observed reddening-insensitive ratio $[{\rm O\, \small{III}}]\, {\lambda }\, 5008/[{\rm O\, \small{III}}]\, {\lambda }$4960 and the value predicted by xstar are consistent with the theoretical value (Dimitrijevic et al. 2007). However, there are two notable discrepancies between the HST observations and the xstar model predictions. The first is that the $[{\rm O\, \small{III}}]\, {\lambda }$5008 emission line is observed to be 230 per cent brighter than predicted by xstar. This excess emission could be attributed to the extended source of $[{\rm O\, \small{III}}]\, {\lambda}$5008 seen in the nucleus of NGC 3327 (Arribas & Mediavilla 1994; Mundell et al. 1995; Delgado & Perez 1997; García-Lorenzo, Mediavilla & Arribas 2001; Walsh et al. 2008). However, the second discrepancy that xstar predicts the $[{\rm O\, \small{III}}]\, {\lambda }$4364 emission line to be ∼25 times brighter than the upper limit estimated in D13 is not as easily addressed as the first. On the one hand, the $[{\rm O\, \small{III}}]\, {\lambda }$5008/λ4364 ratio produced by xstar is entirely consistent with that expected (Osterbrock 1989), given the temperature ∼104 K and electron density ∼108 cm−3 in the O++ region. On the other hand, the observed $[{\rm O\, \small{III}}]\, {\lambda }$4364 emission line would have to be as bright as H γ in order to match the xstar prediction. Observationally, this seems unlikely, but the resolution of the ${\it {\it HST}}$ observations is insufficient to adequately resolve the two lines in question (D13). Equally disconcerting, however, is that contrary to commonly accepted wisdom, xstar predicts the $[{\rm O\, \small{III}}]\, {\lambda }$4364 emission line to be bright even though the electron density in the O++ region exceeds the critical density of the 1S0 level responsible for the transition by about one order of magnitude.

2019AandA...621A..27F__DeGraf_et_al._2017_Instance_1

It is difficult to isolate the impact of mass and environment on the rate and timing of quenching. Mass quenching is more important at earlier times in the evolution of galaxies and may be more important in denser regions (e.g., Peng et al. 2010; Muzzin et al. 2012; Lee et al. 2015; Darvish et al. 2016, 2018; Kawinwanichakij et al. 2017). And in the case of powerful radio galaxies, which lie in over-dense environments, both gas-rich and gas-poor mergers likely play an important role in both the growth of the stellar mass and the black holes. Volonteri et al. (2015b) suggest that in the merger phase, the AGN dominates the bolometric luminosity but the accretion can be very stochastic (see also Gabor & Bournaud 2013; DeGraf et al. 2017). It appears that the galaxies in our sample with the highest star-formation rates all host very powerful AGN, and are potentially all advanced mergers, consistent with this picture. In fact, PKS 0529−549, which has one of the highest SFRs of all the galaxies in our sample, has a modest gas fraction of about 15%, a high star-formation efficiency (SFR/molecular gas mass), and has been transforming its gas into stars rapidly (Man et al., in prep.). The star formation efficiencies in the other radio galaxies with high SFRs also appear extreme (10–100 Gyr−1; Man et al., in prep.). But of course, that does not explain our results in themselves. Dubois et al. (2015), in a study using numerical simulations of the relative growth of SMBHs and their host galaxies, found that star formation may regulate the black hole accretion rate. During the most rapid, gas-rich phase of the growth of massive galaxies, it may be that a larger fraction of the gas in the ISM is not available to fuel the SMBHs, but is consumed via star formation (see DeGraf et al. 2017). As the gas fractions decline, the relative power of the AGN compared to that of the star formation increases, resulting in an increased star formation efficiency. Concomitantly, the increased star formation rate can then disperse the dense gas making it easier for the jets to drive vigorous and efficient outflows (Nesvadba et al. 2006, 2017).

2021ApJ...919..133P__Ravishankar_et_al._2019_Instance_1

The inversion of the Fourier transform from limited data is a well-known problem in several imaging domains like, for instance, medical imaging (McGibney et al. 1993; Bronstein et al. 2002; Sutton et al. 2003; Fessler 2007; Gallagher et al. 2008; Lustig et al. 2008), crystallography (Eisebitt et al. 2004; Marchesini et al. 2008; Brady et al. 2009), and geophysics (Brossier et al. 2009; Jin 2010). This image reconstruction problem inspired several computational approaches like nonuniform Fast Fourier Transform (FFT) (Bronstein et al. 2002; Fessler & Sutton 2003; Greengard & Lee 2004; Lee & Greengard 2005), compressed sensing (Donoho 2006; Lustig et al. 2008; Bigot et al. 2016), and machine learning (Wang et al. 2018; Ravishankar et al. 2019). In the case of astronomical imaging, the use of Fourier methods is mainly related to radio and optical interferometry (Le Besnerais et al. 2008; Thiébaut & Giovannelli 2009; Wiaux et al. 2009; Felli & Spencer 2012; Thompson et al. 2017; Ye et al. 2020), although a similar methodology also involves snapshot imaging spectroscopy (Culhane et al. 2007; Harra et al. 2017; Courrier & Kankelborg 2018; Winebarger et al. 2019). However, in the last three decades, this approach has been utilized also in the case of solar hard X-ray telescopes that have been conceived in order to provide spatial Fourier components of the photon flux emitted via either the bremsstrahlung or thermal processes during solar flares (Lin et al. 2002; Krucker et al. 2020). These Fourier components, named visibilities, are sampled by the hard X-ray instrument in the two-dimensional Fourier space, named the (u, v) plane, in a sparse way, according to a geometry dependent on the instrument design. For instance, NASA’s Reuven Ramaty High Energy Spectroscopic Imager (RHESSI) relies on the use of a set of nine Rotating Modulation Collimators (RMCs) whose FWHM is logarithmically spaced between 23 and 183″ (Hurford et al. 2002). Each RMC measures visibilities on a circle of points in the (u, v) space with a spatial frequency that corresponds to its angular resolution and a position angle that varies according to the spacecraft rotation (see Figure 1, left panel). On the other hand, the Spectrometer/Telescope for Imaging X-rays (STIX) on board ESA’s Solar Orbiter is based on the Moiré pattern technology (Giordano et al. 2015; Massa et al. 2019), and its 30 collimators sample the (u, v) plane over a set of six spirals for an FWHM resolution coarser than 7″ (see Figure 1, right panel).

2019MNRAS.485.3600A__Bignall_et_al._2015_Instance_1

Having eliminated calibration or observational effects as the cause of the spectral changes, it must be that either (1) the configuration of the sources themselves has changed or (2) the interstellar scintillation has caused time-variable focusing and defocusing of the polarized substructure in the radio jets (e.g. Rickett 2001; de Bruyn & Macquart 2015), which can generate spectropolarimetric variability (e.g. Kedziora-Chudczer 2006). Interstellar scintillation (ISS) is difficult to distinguish from intrinsic variability without fully time-resolved data (see e.g. Bignall et al. 2015 and references therein), and can coexist with it (Koay et al. 2018). Never the less, ISS does not easily explain our results: Sources located more than ∼25 degrees from the Galactic plane (3/4 of our polarized sources) and with line-of-sight H α intensities less than ∼ a few Rayleighs (7/8 of our polarized sources; see column 9 of Table 1) are typically not strongly scattered (Pushkarev & Kovalev 2015), and experience associated flux density modulations of typically less than a few per cent (Heeschen 1984; Quirrenbach et al. 1992; Rickett, Lazio & Ghigo 2006; Lovell et al. 2008). This is smaller than the changes observed in our integrated fractional polarization spectra, which should undergo less modulation than the Stokes I in any case. ISS modulation also has a distinct frequency and time dependence. At the mid-galactic latitudes inhabited by most of our sources (see column 8 of Table 1), its magnitude peaks between 4 and 6 GHz (λ2 between 0.0024 and 0.0056 m2), and drops to less than 50 per cent of this value at 1.4 and 10 GHz (i.e. λ2 of 0.0008 and 0.046 m2). Thus, changes due to ISS should ‘spike’ in this narrow λ2 window, which we see little evidence of (though see perhaps PKS B0517–726 and PKS B1903–802; see Figs 3 and 9). The characteristic time-scale of ISS is ∼hours to days (Rickett et al. 2006; Gabányi et al. 2007, and references therein), and over multiyear time-scales, intrinsic effects tend to dominate the observed variability (e.g. Lazio et al. 2001; Rickett et al. 2006; Mooley et al. 2016). We therefore claim that the spectral variability most likely reflects intrinsic changes in the sources themselves.

2018AandA...610A..15J__Tregloan-Reed_et_al._2015_Instance_1

Figure 2 shows the geometry used within our model. The center of the stellar sphere is located at the origin of the three-dimensional spherical coordinate system. PyTranSpot does not take into account a fractional area correction, as used within the WD code (Wilson & Devinney 1971; Wilson 1979, 1990, 2008, 2012). We argue that this effect is negligible, as the resulting loss of accuracy is much smaller than the noise currently present in observations. However, to obtain precise photometric transit and spot parameters, we recommend the use of a planetary pixel radius between 15 and 50 pixels (Tregloan-Reed et al. 2015). On the stellar sphere, every point is described by the two angles (longitude θ, co-latitude φ) and the distance to the stellar center (stellar radius rs in pixels). The longitude θ varies between − 90° and 90°, with the center of the stellar disk corresponding to a value of 0°. The co-latitude φ ranges from 0° to 180°, with the stellar equator set at 90°. Figure 3 illustrates the projection of a spot on to the stellar sphere, as seen from a two-dimensional perspective. The observer is assumed to lie far along the z-axis. To determine the pixels on the stellar sphere, which correspond to the starspot, we implement the following boundary condition: If the angle Δσ between the pixel on the sphere and the spotcenter is greater than the angular radius of the spot α, then this pixel does not belong to the spot. The values for Δσ are derived by using the spherical law of cosines: (2)\begin{equation} \cos(\Delta\sigma) = \cos(\phi_{\rm{spot}}) \cdot \cos(\phi) + \sin(\phi_{\rm{spot}}) \cdot \sin(\phi) \cdot \cos(\Delta\theta), \label{eq:slaw} \end{equation}cos(Δσ)=cos(φspot)·cos(φ)+sin(φspot)·sin(φ)·cos(Δθ),where φspot and φ are the co-latitudes of the spotcenter and the surrounding pixels, respectively. The value Δθ represents the absolute difference in longitude between the spotcenter and the pixel center.

2022MNRAS.509.5340B__Masci_et_al._2019_Instance_1

We run forced photometry at these SN locations using a pipeline, hereafter known as the zuds pipeline1 (Dhawan et al. 2021), which performs aperture photometry using the astropy affiliated package PhotUtils (Bradley et al. 2019), using a 6-pixel diameter aperture on the difference images. The reference images for the difference images are constructed by co-adding exposures from epochs at least 30 d or more before the initial estimate of the time of maximum from the alert photometry, using the software swarp (Bertin 2010). In order to build the co-add, we only take epochs with seeing between 1.7 and 3 arcsec and a magnitude limit deeper than 19.2 mag. For consistency, we use the same reference image for both SNe. In the zuds pipeline, difference images are obtained using hotpants (Becker 2015), an implementation of the image subtraction algorithm (Alard & Lupton 1998). The zero points for each epoch are computed by the Infrared Processing and Analysis Center (IPAC), corrected for a 6-pixel diameter aperture. For the i band, we use the images corrected for an observed fringing pattern, using the fringez software (Medford et al. 2021). From the IPAC forced-photometry service (Masci et al. 2019) at the same locations, we obtain the metadata for each observation, including the magnitude limit mlim of the observation, the seeing of the observation, and the standard deviation σpix on the background at the pixel on which the SN is located. We combine this information with the zuds pipeline results for data quality assessment. Specifically, we only use those observations that satisfy the following conditions: $1.0 \lt \mathrm{seeing} \lt 4.0\,\mathrm{ arcsec}, \ m_{\mathrm{lim}} \lt 19.2 \mathrm{\, mag}, \ \mathrm{ and} \ \sigma _{\mathrm{pix}} \lt 14.0$, where σpix is the robust σ per pixel in the science image and is used as a metric to remove non-photometric data. We then use a maximum likelihood method to fit the salt2 model to each of these two SN light curves. The low seeing values are removed to protect against undersampling during image subtraction. We then remove the epochs that have 5σ flux outliers relative to the best-fitting salt2 model (discussed later and summarized in Table 2) and use the remaining selected points as the light curves of the individual SNe. The final photometry data sets used are included as Tables C1 and C2. The resulting light curves are shown in Fig. 3. As stated earlier, this light curve clearly shows the presence of two transients with no detections for a period of over 100 d in between. For the unclassified transient AT 2019lcj, we notice that AT 2019lcj has a shoulder in the redder bands (r- and i band) as seen in Fig. 3 strongly indicating that the SN is of Type Ia. We will verify this shortly after discussing its redshifts and host properties.

2020AandA...634L...8K__Ramos_et_al._2018_Instance_1

The second data release (DR2) of the ESA Gaia mission has provided the largest available 6D phase-space (positions and velocities) dataset for 7.2 million stars brighter than GRVS = 12 mag (Gaia Collaboration et al. 2018b), making possible precise studies of the Milky Way structure and kinematics on large scales. Gaia data have already revealed signatures of non-equilibrium and ongoing vertical phase mixing in the Milky Way disc (Antoja et al. 2018), likely induced by a previous pericentric passage of the Sagittarius dwarf galaxy (Laporte et al. 2019; Bland-Hawthorn et al. 2019; Haines et al. 2019) or by internally driven bending waves (Khoperskov et al. 2019; Darling & Widrow 2019). A large number of kinematic arches with various morphologies not known prior to Gaia were found (Ramos et al. 2018; Kushniruk & Bensby 2019; Monari et al. 2019) and large-scale wiggles were discovered in the Vϕ − R plane (Kawata et al. 2018; Antoja et al. 2018). These features can be interpreted as the signature of the impact of an external perturbation (D’Onghia et al. 2016; Laporte et al. 2019) or as evidence of the spiral structure and the bar of the Milky Way (Hunt et al. 2018; Fragkoudi et al. 2019). At the same time, in the solar vicinity the Galactic spiral structure is believed to generate various patterns in the phase-space (Siebert et al. 2012; Williams et al. 2013), but uncertainties on the location and strength of the spiral arms have so far prevented us from explaining the observed patterns. In this work, we use the high-quality Gaia DR2 sample of stars with radial velocities (hereafter GRV2), together with a new method to highlight the stellar density structures, to explore the Milky Way spiral arms without relying on any specific stellar tracer. The outline of the Letter is as follows: in Sect. 2 we describe the data and the method we used; in Sect. 3 we examine the phase-space structure of the Milky Way and compare its various features with Milky Way-type galaxy simulations; and in Sect. 4 we summarize the conclusions that can be drawn from our study.

2016AandA...588A.132T__Padilla_et_al._2014_Instance_1

Most of the recent observational efforts to understand galaxy evolution have been focused on determining the history of cosmic star formation, gas density evolution, metallicity evolution, and mass growth of the Universe (Daddi et al. 2004; Mannucci et al. 2010; Madau & Dickinson 2014; Tomczak et al. 2014; Bouwens et al. 2015). These multiwavelength observational constraints have usually been summarized as galaxy scaling relations that might or might not change with redshift (Mannucci et al. 2010; Elbaz et al. 2011; Bouwens et al. 2014; Troncoso et al. 2014), in high- or low-density environments, in extreme physical conditions (starburst, AGN galaxies), and in spatially resolved data due to internal variations of the galaxy properties (Sanchez et al. 2013). In parallel, theoretical works and simulations have tried to explain the physical mechanisms that reproduce the measured global properties (Daddi et al. 2010; Davé et al. 2011; Lilly et al. 2013; Lagos et al. 2014; Padilla et al. 2014). Despite these efforts, the completeness and cleanness of the sample are still challenging problems that depend on the sample selection-method, instrument limits, and telescope time. These problems make the comparison between observational and theoretical works even more difficult. For example, Campbell et al. (2014) compared the stellar mass of GALFORM galaxies predicted by the model with those obtained through the fit of their predicted broad-band colors. They found that both quantities differ for an individual galaxy, hence the clustering of mass-selected samples can be affected by systematic biases. Therefore, mass-selected samples might provide erroneous conclusions regarding their progenitors and descendants. In addition, the evolution of scaling relations is constrained with observations of galaxy samples that are selected with luminosity or stellar-mass thresholds and are located at different redshifts, which does not necessarily constitute causally connected populations (i.e., they do not follow a progenitor-to-descendant relation). Clustering-selected samples overcome this problem because in a hierarchical clustering scenario, a correlation analysis allows us to estimate the bias and hence statistically determine the progenitors and descendants of galaxy samples. The bias parameter measures the clustering difference between the galaxy spatial distribution and underlying dark-matter distribution. Thus, it relates the typical mass of halos hosting the galaxies (Sheth et al. 2001). Hence, measuring it in galaxy samples at different redshifts determines whether we are following the evolution of baryonic processes occurring in halos of similar masses or not. This fact is of extreme importance because once it is determined, we can use the multiwavelength data to study the evolution of the baryonic processes at certain halo mass, establishing a direct link between observations and galaxy formation models. Padilla et al. (2011) selected early-type galaxies according to their clustering and luminosity function in the MUSYC survey. So far, no study that selects star-forming galaxies according their clustering and luminosity function has been reported.

2016ApJ...821..107G__Gloeckler_&_Fisk_2015_Instance_1

We repeated the plasma pressure calculation presented by Schwadron et al. (2011) and Fuselier et al. (2012) for the new ENA energy spectrum. The results for the downwind hemisphere and for the Voyager 1 region are summarized in Table 3. The measured intensity j ENA of neutralized hydrogen at a given energy translates into a pressure of the parent ion population in the heliosheath times the integration length along the line of sight, ΔP × l, in the following way: 3 Δ P × l = 4 π 3 n H m H v j ENA ( E ) σ ( E ) Δ E c f 4 c f = ( v + u R ) 2 v 4 ( v 2 + 4 u R 2 + 2 u R v ) . In Equation (3), ΔE denotes the width of the respective energy bin; for the typical radial velocity of solar wind in the flanks and the downwind hemisphere of the inner heliosheath, we assumed uR = 140 km s−1 as measured by Voyager 2, whereas uR = 40 km s−1 for the heliosheath in the Voyager 1 direction (Schwadron et al. 2011; Gloeckler & Fisk 2015). For the density of neutral hydrogen in the inner heliosheath a constant nH = 0.1 cm−3 is assumed (Schwadron et al. 2011; Gloeckler & Fisk 2015). The charge-exchange cross section between protons and neutral hydrogen decreases from (4 to 2) × 10−15 cm−2 for 0.015 to 1.821 keV (Lindsay & Stebbings 2005). The integration length l for ENA production in the plasma is approximately the thickness of the inner heliosheath. The part of Equation (3) without the velocity factor cf can be interpreted as stationary pressure. The total pressure or dynamic pressure is the stationary pressure times this factor. Integrating over all energy bins in Table 3, we obtain the total plasma pressure times heliosheath thickness as P × l = 304 pdyn cm−2 au for the downwind hemisphere and 66 pdyn cm−2 au for the Voyager 1 region (1 pdyn cm−2 au = 0.015 N m−1). If we want to put these numbers into the context of other studies, we face two problems. First, the uncertainty of the total pressure is large given the upper limits in the two lowest energy bins. Second, heliosheath plasma more energetic than 2 keV will produce ENAs that cannot be detected with IBEX-Lo. We therefore used the observed median j = 0 cm−2 sr−1 s−1 keV−1 for heliospheric ENAs in the two lowest energy bins of IBEX-Lo and relied on the study by Livadiotis et al. (2013). They compared the expected plasma pressure from a kappa distribution of protons with the plasma pressure derived from IBEX-Hi energy spectra: the energy range between 0.03 and 2 keV, roughly corresponding to the IBEX-Lo range, covered more than half of the total plasma pressure predicted from a kappa distribution. The authors found a total plasma pressure of P = 2.1 pdyn cm−2 for all sky directions except for the ENA Ribbon. Gloeckler & Fisk (2015) presented a multi-component plasma model for the heliosheath to explain Voyager and IBEX observations. At low energies they assumed the ENA energy spectra provided by Fuselier et al. (2012). They derived a total pressure of 2.5 pdyn cm−2 in all three plasma regions in the nose of the heliotail (Gloeckler & Fisk 2015). Pressure contributions from the slowed solar wind, magnetic pressure, and the pressure exerted from pickup ions and anomalous cosmic rays all had to be taken into account to obtain this total pressure.

2019AandA...630A..37S__Behar_et_al._2017_Instance_1

Solar wind velocity distribution moments are described in Behar et al. (2017). The ion density nsw is the moment of order 0, and the ion bulk velocity usw (a vector) appears in the moment of order 1, the flux density $n_{\mathrm{sw}} \ \underline{\mathbf{u}_{\mathrm{sw}}}$ n sw   u sw _ . The bulk speed can be defined as the norm of the bulk velocity, that is, $u_{\mathrm{sw}} = |\underline{\vec{u}_{\mathrm{sw}}}$ u sw =| u sw _ |. However, this bulk speed is representative of single-particle speeds as long as the velocity distribution function is compact (e.g., a Maxwellian distribution). Complex velocity distribution functions were observed by RPC-ICA within the atmosphere of 67P. For instance, partial ring distributions were frequently observed for solar wind protons at intermediate heliocentric distances, when the spacecraft approached the SWIC (Behar et al. 2017). To illustrate the effect of such distorted distributions, a perfect ring (or shell) distribution centered on the origin of the plasma reference frame can be imagined, in which all particles have the same speed of 400 km s−1. The norm of the bulk velocity in this case would be 0 km s−1, whereas the mean speed of the particles is 400 km s−1, which is the relevant speed for SWCX processes. This mean speed, noted Usw, of the particles is calculated by first summing the differential number flux over all angles, and then taking the statistical average (Behar 2018). Over the entire mission, the deceleration of the solar wind using the mean speed of the particles is much more limited than the deceleration shown by the norm of the bulk velocity (Behar et al. 2017): there is more kinetic energy in the solar wind than the bulk velocity vector would let us think. This is the main difference with the paradigm used at previously studied (and more active) comets (Behar et al. 2018b). These complex, nonthermal velocity distribution functions also prevent us from reducing the second-order moment (the stress tensor) to a single scalar value, which, for a Maxwellian distribution, could be identified with a plasma temperature. In the context of 67P and for an important part of the cometary orbit around the Sun, the temperature of the solar wind proton has no formal definition.

2016MNRAS.459.3998L__Blanton_&_Roweis_2007_Instance_1

In this subsection, we first use our measured CLFs to infer the conditional stellar mass functions and then use the results to study the stellar mass contents of dark matter haloes. To convert luminosity into stellar mass, one typically uses a mass-to-light relation based on galaxy colour (e.g. Bell et al. 2003). This requires robust colour estimates. In our case, galaxies with r ∼ 21 in the SDSS photometric sample have typical error in the (u − r) colour of about 1 mag, mostly due to uncertainty in the u-band photometry. This error will propagate into the stellar mass estimates and can bias the stellar mass function, leading to an overestimate at the high-mass end.7 To reduce such bias, we estimate stellar masses using the observed mean colour–magnitude relations for blue and red galaxies separately. The details of this procedure are described in Appendix C. As our final goal is to estimate the global baryon fractions in stars, the use of average values as opposed to full colour distributions is not a severe limitation. Following Bell et al. (2003), we convert the observed luminosity and colour into stellar mass using (11) \begin{eqnarray} \log \bigg [\frac{M_{{\ast }}}{\mathrm{M}_{\odot }}\bigg ] = -0.223+0.299\,(u-r) -0.4\,(M_{\rm r}-4.64) -0.1,\nonumber\\ \end{eqnarray} where (u − r) is the mean colour of a blue or red galaxy at a given absolute magnitude Mr. The constant, 4.64, is the r-band magnitude of the Sun in the AB system (Blanton & Roweis 2007) and the −0.1 offset corresponds to the choice of the Kroupa initial mass function (Kroupa 2001). Using this light-to-mass relation, we convert the global best-fitting luminosity functions into the corresponding stellar mass functions. The two left-hand panels in Fig. 10 show the estimated conditional stellar mass functions for blue and red satellites as a function of halo mass, respectively. Since a fixed $M^{{\ast }}_{{\rm b}}$ is applied to satellite galaxies for all halo masses, a slight overestimate of the stellar mass occurs at the massive ends for small haloes. As the stellar masses of central galaxies are obtained using individual observed (u − r) colours, the overestimate of the stellar mass of satellites can sometimes cause the stellar mass of a satellite galaxy to exceed that of the central. The dashed lines in the left-hand panels indicate the ranges where such situation is present. In order to estimate the total stellar mass in haloes of a given halo mass, we integrate the inferred conditional stellar mass functions down to low masses. We find that using 107 M⊙, which is about the minimum stellar mass reachable by the sample used here or zero lead to similar results. The results are shown in the right-hand panel of Fig. 10. The coloured dashed lines show the stellar mass to halo mass ratios for blue and red satellites, respectively. The grey dashed line is the total stellar mass of satellite galaxies to halo mass ratio, while the grey solid line is the stellar to dark matter mass ratio of central galaxies. The total ratio is shown as the black line. For haloes with M200 1013 M⊙, the total stellar mass is dominated by the central galaxies; in contrast, for more massive haloes, it is dominated by red satellites. The contributions from red and blue satellites are comparable for haloes with M200 ∼ 1012 M⊙, and the contribution from blue satellites appears to increase towards lower halo masses. Note that, although there are marked upturns in the stellar mass functions at the low-mass ends for red galaxies, the low-mass galaxies in the upturns (M* 108 M⊙) contribute little to the total stellar mass. Our results are qualitatively consistent with estimates based on data with more limited dynamical ranges (e.g. Leauthaud et al. 2012a,b; Kravtsov, Vikhlinin & Meshscheryakov 2014).

2020MNRAS.491.5759H__Dodson-Robinson_&_Salyk_2011_Instance_1

More recently, Long et al. (2018) performed an analysis on 32 discs in the Taurus star-forming region using ALMA. From this sample 12 discs containing axisymmetric structure were identified. This structure takes the form of dark band bright ring pairs, emission bumps and cavities. Overall 19 gap ring pairs were identified, indicating that a number of these systems contain multiple gaps. These gaps range in location from R = 10–120 au with no preferred distance. The majority of these gaps are narrow, but the weak correlation between gap location and gap width potentially implies formation via planet–disc interactions. In addition a significant number of these gaps cannot be explained by condensation fronts. Long et al. (2018) perform an analysis on the width of these gaps in order to determine possible masses of planets that could form them. Assuming the width of the gaps corresponds to 4RHill (Dodson-Robinson & Salyk 2011) they estimate the masses of the planets to be in the 0.1–0.5MJ (q = 1.0 × 10−4–5.0 × 10−4) range, however they stress that these masses have large uncertainties and that the gap widths may be as large as 7–10RHill (Pinilla, Benisty & Birnstiel 2012). Alternatively they compare the distance between the minimum of the gaps and the maximum of the rings to the results of hydrodynamic simulations (Rosotti et al. 2016) with an α = 10−4 (ν = 2.5 × 10−7), resulting in a predicted planet mass of 0.05MJ (q = 5.0 × 10−5) and an α = 10−2 (ν = 2.5 × 10−5), resulting in a predicted planet mass of 0.3MJ (q = 3.0 × 10−4). Using equation (7) and an estimated disc lifetime of 2 Myr [based on stellar age estimates for the spectral type of the target stars (Baraffe et al. 2015; Feiden 2016)] we can see that in the higher viscosity case planets of 0.3MJ (q = 3.0 × 10−4) can form at any of these radii without creating vortices. The lower viscosity case is more difficult to predict as it is significantly lower than the viscosities we have explored, however for this planet mass it seems unlikely from our results that the predicted planet could form without creating vortices. Instead, we expect the maximum mass planets that could be found here to be roughly half the mass they predict. In addition, from our results planets of mass 0.5MJ (q = 5.0 × 10−4) would require the disc viscosity to be ν ⪆ 3.0–5.0 × 10−6 to form without creating vortices, depending on the radius at which they are located. Conversely we find planets as small as 0.1MJ (q = 1.0 × 10−4) can even form at ν = 2.0 × 10−6 without creating vortices. This can be seen in Fig. 8. Hence our results show that these gaps could be opened by planets of the masses predicted by Long et al. (2018) without forming vortices.

2017MNRAS.464.4534Q__Schmidt_et_al._2012_Instance_1

Space missions have traditionally focused on performing spectropolarimetric observations measuring the four Stokes parameters in a narrow spectral window where one or two photospheric absorption lines of interest are present. For instance, see Hinode/SP (Tsuneta et al. 2008; Lites et al. 2013), SDO/HMI (Pesnell, Thompson & Chamberlin 2012; Schou et al. 2012), and Solar Orbiter/PHI (Gandorfer et al. 2011; Solanki et al. 2015). On the contrary, ground-based telescopes usually have instruments which can cover several spectral lines simultaneously, e.g. THEMIS/MTR (López Ariste, Rayrole & Semel 2000; Paletou & Molodij 2001), SST/CRISP (Scharmer et al. 2003, 2008), DST/IBIS (Cavallini 2006) and DST/SPINOR (Socas-Navarro et al. 2006), Gregor/GRIS (Collados et al. 2012; Schmidt et al. 2012), or ZIMPOL (Povel 2001), among others. In most of the mentioned cases, the light beam occupies almost the full length of the camera in one of its directions due to the larger spectral coverage which directly increases the amount of data generated. Although the data rate is not usually a crucial factor for ground-based telescopes, space-based missions have an extremely limited telemetry. Thus, unless there is a strong reason to expand the wavelength coverage it is highly recommendable to keep focusing on narrow spectral windows which contain a high density of useful lines. In this regard, we find it extremely helpful to perform theoretical studies and observations to define the optimum spectral window for the purposes of a given mission. For example, Hinode/SP was strongly supported by ASP (Elmore et al. 1992) observations and SDO/HMI benefited from ground-based observations but also from specific supporting works like Norton et al. (2006). We performed a similar study in Quintero Noda et al. (2016) aiming to support future missions with chromospheric polarimetry as the main target, for instance Solar-C (Katsukawa & Solar-C Working Groups 2011; Katsukawa et al. 2012; Watanabe 2014; Suematsu & Solar-C Working Group 2016). We concluded that the Ca ii 8542 Å is a unique spectral line which is sensitive to a large range of heights, from the photosphere to the chromosphere (see figs 4 and 6 of the mentioned work). However, its sensitivity to the magnetic field at photospheric layers is low which precludes examining quiet Sun magnetic fields at these heights. Therefore, there is still room for improvement and, for this reason, we decided to examine the solar spectrum at the vicinity of the Ca ii 8542 Å line. In this regard, if we observe additional spectral lines we will increase the number of spectral points with valuable information, particularly if these lines have different heights of formation, which will enhance the accuracy of the inferred atmospheric parameters (for example Asensio Ramos et al. 2007). However, in order to achieve this purpose we have to expand the spectral window which will increase the data rate if we maintain the same spectral sampling. Therefore, it is essential that these additional lines provide valuable information complementing the Ca ii 8542 Å spectral line.

2019ApJ...875..129K__Smith_et_al._2013_Instance_1

We used the band high-resolution spectra of Arcturus and μ Leo, obtained with WINERED, to estimate the microturbulence and iron abundance with a precision similar to that of previous results from spectra at different wavelengths. Our lists of Fe i lines in the 0.91–1.33 μm range will be useful for obtaining the precise metallicities of stars obscured by severe interstellar extinction compared with the optical regime, for which the extinction is stronger. For many objects in the Galactic disk found in recent infrared surveys, this new wavelength window may be ideal for detailed abundance analyses. One of the major error sources is the uncertainty in ξ in various studies, including ours, based on spectra at different wavelengths from the optical (e.g., Table 3 of Jofré et al. 2014) to the H band (e.g., Table 7 of Smith et al. 2013). Furthermore, how to determine the microturbulence and its error is not established or straightforward. The bootstrap method that we demonstrated in this paper can give quantitative estimates of the microturbulence and its error. The error in microturbulence is 0.11–0.24 for each combination of target and line list. The obtained microturbulences are consistent with those that were estimated or assumed in previous studies on the same targets. Note, however, that using different line lists (or different sets of lines) can result in slightly different microturbulences depending especially on the values of strong lines used in the analysis. The very strong lines (X > −6) were rejected because these lines are likely to introduce problems into a chemical abundance analysis due to severe saturation, non-LTE effects, contribution of EW from the damping wing, and so on. Considering the comparison of our estimates with previous ones in addition to the scatters of , we adopt the measurements with the Fe i lines selected from MB99 as our best estimates: and for Arcturus and μ Leo, respectively.

2017AandA...605A..96D__Goździewski_et_al._2016_Instance_1

In this article, I present an analytical model of resonant chains. Analytical models have already been proposed, in particular to study the dynamics of the Laplace resonance (1:2:4 chain) between Io, Europa, and Ganymede (e.g., Henrard 1983). However, while several numerical studies have been dedicated to the capture of planets in various resonant chains (e.g., Cresswell & Nelson 2006; Papaloizou & Terquem 2010; Libert & Tsiganis 2011; Papaloizou 2016), general analytical models have not yet been proposed. Recently, Papaloizou (2015) proposed a semi-analytical model of three-planet resonances taking into account only the interactions between consecutive planets in the chain, with a particular focus on the Kepler-60 system (12:15:20 resonant chain, see also Steffen et al. 2013; Goździewski et al. 2016). This model is very similar to the studies of the Laplace resonance between the Galilean moons, but is not well suited in the general case. For instance, four-planet (or more) resonances are not considered. Moreover, for some three-planet resonances, the interactions between non-consecutive planets cannot be neglected. For instance, in a 3:4:6 resonant chain, each planet is locked in a first-order resonance with each of the other planets. In particular, the innermost and outermost planets are involved in a 2/1 MMR that strongly influences the dynamics of the system. I describe here a general model of resonant chains, with any number of planets, valid for any resonance order. I particularly focus on finding the equilibrium configurations (eccentricities, resonant arguments, etc.) around which a resonant system should librate. While a real system may be observed with significant amplitude of libration around the equilibrium, or could even have some angles circulating, the position of the equilibria still provides useful insights into the dynamics of the system. In Sect. 2, I describe this analytical model, and the method I use to find the equilibrium configurations. In Sect. 3, I apply the model to Kepler-223. I show that six equilibrium configurations exist for this resonant chain, and that the system is observed to be librating around one of them. I also show that knowing the current configuration of the system allows for interesting constraints to be put on its migration scenario, and in particular on the order in which the planets have been captured in the chain.

2021AandA...654A..34B__Grassi_et_al._2014_Instance_1

The simulations presented in this work have been performed with the publicly available hydrodynamic code GIZMO (Hopkins 2015), which is a descendant of GADGET2 (Springel 2005). The code evolves the magneto-hydrodynamics equations for the gas including a constrained-gradient divergence-cleaning method (Hopkins & Raives 2016; Hopkins 2016), together with the gas self-gravity. For the purpose of this study, we equipped the code with an on-the-fly non-equilibrium chemical network, which was implemented via the public chemistry library KROME (Grassi et al. 2014), similarly to Bovino et al. (2019). In particular, in our simulations, we assumed an isothermal equation of state for the gas, with the temperature set to 10 K or 15 K. These temperatures are in line with kinetic temperatures obtained from NH3 for the same regions (Friesen et al. 2009, 2017). The initial conditions of our simulations consist of a collapsing filament, modelled as a cylinder with a typical observed (Arzoumanian et al. 2011) Plummer-like density profile $n(R) = n_0/[1+(R/R_{\textrm{flat}}){}^2]^{p/2}$ n(R)= n 0 / [1+ (R/ R flat ) 2 ] p/2 , where R is the cylindrical radius, n0 is the ridge volume density and is constant along the filament axis, Rflat is the characteristic radius of the flat inner part of the density profile, and p is the characteristic exponent. Following observed estimates (Arzoumanian et al. 2011), we set p = 2 and Rflat = 0.033 pc, which gives a mean filament width of 3 × Rflat ~ 0.1 pc. The setup follows previous works (Seifried & Walch 2015; Körtgen et al. 2018). To avoid any spurious effect at the edges of the filament along its axis, we embed the cylinder in an exponentially decaying background, with the decay scale length set to the filament length Lfil = 1.6 pc. The box is large Lbox = 2.4 pc. We initialised the filament in a turbulent state, assuming a Burgers-like power spectrum that grows as ∝ k10 up to λpeak = Lbox∕6 and then decays as ∝ k−2 (see also Körtgen et al. 2018). Finally, we assumed the box is permeated by a constant magnetic field B0 = 40 μG (Seifried & Walch 2015; Körtgen et al. 2018).

2017ApJ...836..124D__Davenport_et_al._2015_Instance_1

In Table 3, we list the physical parameters of our model (not including the normalizations for each telescope and each eclipse), and in Tables 4 and 5 we show the resulting best-fit model and the 16th and 84th percentiles (approximately 1σ) for each parameter for each season. We are able to fit our measured light curves equally well regardless of which star we place star spots. However, if we place the star-spot signal on the secondary component, the out-of-eclipse model parameters vary significantly between observing seasons, whereas if the star-spot signal is originating on the primary component, the parameters are stable between seasons. Particularly, F, the ratio between the orbital and rotational frequency has different values for each season if the secondary component is responsible for the star-spot modulation signal. Since the rotation period of the secondary star should not change (and the effect of differential rotation for star spots at different latitudes is small for M dwarfs; Davenport et al. 2015), we conclude that the star-spot signal cannot be originating on the secondary component. Since we only detect one rotational frequency, and this frequency differs from the orbital frequency of the system, we have assumed that it is originating solely on one component of the system (the primary component); though, in reality, there is likely to be some star spots on the surface of both components and that rotation of the secondary component may also be contributing somewhat to this signal (though we do not have a significant detection). We will utilize the model for which star spots are located on the primary component for the rest of this paper. We note, however, that this choice does not significantly affect the values of the masses and radii of the components but does change the uncertainty. The light curves themselves are most directly sensitive to the sum of the component radii and their ratio. The eclipse duration measures a combination of ( )/a (where R1 and R2 are the component radii and a is the semimajor axis) and the inclination of the orbit. In the case of grazing eclipse (where we cannot break this degeneracy with a measurement of the duration of the total phase), this degeneracy is instead broken by the eclipse depth, which also depends on the limb darkening of the stars and the star spots on the stellar surfaces.

2019MNRAS.486.1781R__Fossati_et_al._1998_Instance_1

Blazars are a peculiar class of active galactic nuclei (AGNs) that have their relativistic jets pointed close to the line of sight to the observer with angle ≤ 10° (Antonucci 1993; Urry & Padovani 1995). They are classified as flat-spectrum radio quasars (FSRQs) and BL Lacerate (BL Lac) objects based on the strength of the emission lines in their optical/infrared (IR) spectrum. Both classes of objects emit radiation over the entire accessible electromagnetic spectrum from low-energy radio to high-energy γ-rays. As blazars are aligned close to the observer, the emission is highly Doppler boosted, causing them to appear as bright sources in the extragalactic sky. They dominate the extragalactic γ-ray sky first hinted by the Energetic Gammma-ray Experiment Telescope (EGRET) observations onboard the Compton Gamma-Ray Observatory (CGRO; Hartman et al. 1999) and now made apparent by the Large Area Telescope (LAT) onboard the Fermi Gamma-ray Space Telescope (Atwood et al. 2009). The broad-band spectra of blazars are dominated by emission from the jet with weak or absent emission lines from the broad-line region (BLR). One of the defining characteristics of blazars is that they show flux variations (Wagner & Witzel 1995) over a wide range of wavelengths on time-scales ranging from months to days and minutes. In addition to flux variations they also show large optical and radio polarization as well as optical polarization variability. In the radio band they have flat spectra with the radio spectral index (αr) 0.5 ($S_{\nu } \propto \nu ^{-\alpha _r})$. The broad-band spectral energy distribution (SED) of blazars is characterized by a two-hump structure, one peaking at low energies in the optical/IR/X-ray region and the other one peaking at high energies in the X-ray/MeV region (Fossati et al. 1998; Mao et al. 2016). In the one-zone leptonic emission models, the low-energy hump is due to synchrotron emission processes and the high-energy hump is due to inverse Compton (IC) emission processes (Abdo et al. 2010b). The seed photons for the IC process can be either internal to the jet (synchrotron self-Compton or SSC; Konigl 1981; Marscher & Gear 1985; Ghisellini & Maraschi 1989) or external to the jet (external Compton or EC; Begelman et al. 1987). In the case of EC, the seed photons can be from the disc (Dermer & Schlickeiser 1993; Boettcher, Mause & Schlickeiser 1997), the BLR (Sikora, Begelman & Rees 1994; Ghisellini & Madau 1996), and the torus (Błaerrorzdotejowski et al. 2000; Ghisellini & Tavecchio 2008). Though leptonic models are found to fit the observed SED of a majority of blazars, for some blazars, their SEDs are also well fitted by either hadronic (Mücke et al. 2003; Böttcher et al. 2013) or lepto-hadronic models (Diltz & Böttcher 2016; Paliya et al. 2016). In the hadronic scenario, the γ-ray emission is due to synchrotron radiation from extremely relativisitic protons (Mücke et al. 2003) or the cascade process resulting from proton–proton or proton–photon interactions (Mannheim 1993). Even during different brightness/flaring states of a source, a single emission model is not able to fit the broad-band SED at all times. For example in the source 3C 279, while the flare during 2014 March–April is well fitted by a leptonic model (Paliya, Sahayanathan & Stalin 2015b), the flare in 2013 December with a hard γ-ray spectrum is well described by lepto-hadronic processes (Paliya et al. 2016). Thus, the recent availability of multiwavelength data coupled with studies of sources at different active states indicates that we still do not have a clear understanding of the physical processes happening close to the central regions of blazars.

2018MNRAS.475.3419A__Davis_et_al._1999_Instance_1

If we consider for the bulk density the value 4500 kg m−3, which is one of the highest measured in the asteroid population out of those asteroids with good quality of data (see Carry 2012), it will strengthen the hypothesis that Psyche could be an exposed metal core of a differentiated asteroid (Elkins-Tanton et al. 2017). According to the models of asteroid differentiation, the process that led to the formation of Psyche happened very early. Considering Psyche's current diameter, Deff = 226 km (Shepard et al. 2017), the Psyche parent body (PPB) was supposed to be ∼500 km in diameter and have suffered severe ‘hit-and-run’ impact events capable of removing all crust and mantle, exposing the core (Elkins-Tanton et al. 2016). In addition, Psyche should have ∼40 per cent macroporosity, if we assume that it is made of blocks of iron/nickel with a density around 7500 kg m−3. In that case, the core itself was possibly destroyed and re-accumulated, implying a severe collisional history. When an asteroid is disrupted catastrophically, with a remaining mass ≤50 per cent of the initial one, after a collision with another body, an asteroid family is formed. If the collision happened in the Main Belt, a family of asteroid fragments should be in the region of Psyche; however, no family related to Psyche has been found yet (Davis, Farinella & Marzari 1999). One possibility to solve this issue is that the potential Psyche asteroid family was created at an early time, e.g. within the first 500 Myr of Solar system history (Davis et al. 1999). This would allow the family fragments to be ground down by collisional evolution and be unobservable today. The same models show that, even in this case, today there should be several surviving fragments having diameters around 20 km and above the detection limit. There is a lack of primordial asteroid families in the Main Belt (Brož et al. 2013; Spoto, Milani & Knežević 2015), very likely due to the classical methods that are used to identify them. The hierarchical clustering method (HCM) is not sensitive enough to find old and dispersed families, as it searches for asteroids forming compact groups in orbital element space (semi-major axis, eccentricity and inclination). A new approach has been proposed and implemented with success (Walsh et al. 2013; Delbo’ et al. 2017), as it is able to distinguish very old families, having eccentricities and inclinations dispersed in space. Therefore the possibility of the absence of a Psyche family could be due to searching biases. However, this may be an unlikely hypothesis, because A-type asteroids that could represent mantle material (almost pure olivine) from differentiated bodies do not exist extensively in the orbital space related to Psyche, but instead are distributed randomly in the Main Belt (Davis et al. 1999; DeMeo et al. 2015). In order to study this puzzling small body further, NASA is sending a new Discovery Mission to Psyche. The main goal is to get insight into whether it is a core of a parent body and understand the procedures of differentiation, making all the above questions more valid than ever. The alternative theory is that Psyche is a planetesimal that bears primitive unmelted material (Elkins-Tanton et al. 2016).

2019AandA...627A.173V__Epstein_et_al._(2014)_Instance_1

Since the age determination using asteroseismology relies on the mass-age relation that red giants follow, this means that the method is biased by any event that changes the stellar mass, such as mass accretion from a companion or stellar mergers (blue stragglers or stars rejuvenated by accretion, e.g. Boffin et al. 2015) or mass loss. One way to look for mass accretion events from a companion is to look for radial velocity, photometric variations, or chemical signs of accretion (e.g. high carbon and s-process enhancements; Beers & Christlieb 2005; Abate et al. 2015). The effect of mass loss can be minimised by looking at stars in the low-RGB phase, in which the effect of mass loss is smaller compared to red clump stars (Anders et al. 2017). The first study to determine masses for a sample of metal-poor halo giants with both seismic information (from Kepler; Borucki et al. 2010) and chemistry from high-resolution APOGEE (Majewski et al. 2017) spectra, was by Epstein et al. (2014). The authors used scaling relations at face value and reported masses larger (M >  1 M⊙) than what would be expected for a typical old population. Similar results were obtained by Casey et al. (2018), also using scaling relations for three metal-poor stars. These findings led to the need for further tests of the use of asteroseismology in the low metallicity regime. Miglio et al. (2016) analysed a group of red giants in the globular cluster M4 ([Fe/H] = −1.10 dex and [α/Fe] = 0.4 dex) with seismic data from K2 mission (Howell et al. 2014). These authors found low seismic masses compatible with the old age of the cluster, hence suggesting that seismic masses and radii estimates would be reliable in the metal-poor regime provided that a correction to the Δν scaling relation is taken into account for red giant branch (hereafter RGB) stars. The correction presented in Miglio et al. (2016) is a correction that is theoretically motivated based on the computation of radial mode frequencies of stellar modes.

2016AandA...589A..73R__Kurucz_1992_Instance_1

Single-burst stellar population (SSP) models mimic uniform stellar populations of fixed age and metallicity, and are an important tool to study unresolved stellar clusters and galaxies. They are created by populating theoretical stellar evolutionary tracks with stars of a stellar library, according to a prescription given by a chosen initial mass function (IMF). Thus, the quality of the resulting SSP models depends significantly on the completeness of the used input stellar library in terms of evolutionary phases represented by the atmospheric parameters temperature, Teff, surface gravity, log (g), and metallicity. A sufficiently large spectral coverage is equally crucial when constructing reasonable SSP models. Theoretical stellar libraries like, e.g. BaSeL (Kurucz 1992; Lejeune et al. 1997, 1998; Westera et al. 2002), or PHOENIX (Allard et al. 2012; Husser et al. 2013) are generally available for both a large range in wavelength and in stellar parameters, whereas empirical libraries are found to be more incomplete in both respects. However, the advantage of the latter ones is that they are not hampered by the still large uncertainties in the calculation of model atmospheres. Examples of empirical stellar libraries in the optical wavelenth range encompass the Pickles library (Pickles 1998), ELODIE (Prugniel & Soubiran 2001), STELIB (Le Borgne et al. 2003), Indo-US (Valdes et al. 2004), MILES (Sánchez-Blázquez et al. 2006), and CaT (Cenarro et al. 2001, 2007). In the near-infrared (NIR) and mid-infrared (MIR)1, only very few empirical libraries have been observed so far (e.g. Lançon & Wood 2000; Cushing et al. 2005; Rayner et al. 2009). The NASA Infrared Telescope Facility (IRTF) spectral library, described in the latter two papers, is to date the only empirical stellar library in the NIR and MIR which offers a sufficiently complete coverage of the stellar atmospheric parameter space to construct SSP models. In the future, the X-Shooter stellar library, which contains around 700 stars, and which covers the whole optical (see Chen et al. 2014) and NIR wavelength range until 2.5 μm, will clearly improve the current situation in the NIR.

2020MNRAS.492..821C__Masters_et_al._2014_Instance_1

As a general remark, whether the locally calibrated metallicity diagnostics are applicable to high-redshift galaxies is still a matter of great debate. Diagnostics that are expected to be little affected by the ionization conditions of the gas (see e.g. Dopita et al. 2016) have been suggested to be valuable for high-redshift galaxies, where strong variations in ionization parameter and excitation conditions compared to local galaxies have been invoked to explain the observed evolution in the emission-line ratios (as seen, for example, from the offset of high-z sources in the classical BPT diagrams with respect to the local sequence (Kewley et al. 2013; Nakajima et al. 2013; Steidel et al. 2014; Kashino et al. 2017; Strom et al. 2017). However, since such diagnostics usually involve the [N ii]/[O ii] or the [N ii]/[S ii] line ratios, they are strongly dependent on the assumed relation between the N/O ratio as a function of the oxygen abundance O/H, which is affected by a large scatter and whose evolution with cosmic time and/or dependence on galaxy mass is also indicated as a possible origin of the observed evolution of the emission-line properties in high-zgalaxies (Masters et al. 2014, 2016; Shapley et al. 2015). Therefore, strong-line indicators based only on alpha elements (like, e.g. oxygen) have also been suggested as appropriate to high redshift studies, since galaxies at z ∼ 1.5–2.5 seem to show no appreciable offset from local trends in oxygen-based diagnostic diagrams (e.g. R23 versus O32, Shapley et al. 2015). However, the location on the abovementioned diagram could even be sensitive to a variation in the hardening of the radiation field at a fixed metallicity rather than a variation in abundances (Steidel et al. 2016; Strom et al. 2017). In any case, at redshifts ∼1.5 (where the majority of KLEVER galaxies considered in this work lie), the lack of the [O ii] doublet in the NIR bands observable from KMOS prevents us from using purely oxygen diagnostics, thus forcing us to exploit the nitrogen-based ones. When the survey will be complete, we will investigate the spatially resolved behaviour of z ∼ 2 galaxies on the R23 versus O32 diagram in a more statistically robust manner. Recently, Patrício et al. (2018) have shown that oxygen-based diagnostics z ∼ 2 provide metallicities comparable to those inferred from the electron temperature method; unfortunately, just a handful of robust auroral line detections have been reported so far in high-z sources (e.g. Jones, Martin & Cooper 2015b; Sanders et al. 2016; see also Patrício et al. 2018, and references therein), due to the intrinsic observational challenges in detecting the faint auroral lines with current instrumentation. Only the advent of new facilities like JWST or the MOONS spectrograph on the VLT will ultimately allow us to tackle this issue in the next few years, allowing us to properly calibrate the metallicity diagnostics against fully Te-based abundance determination at high redshifts and providing the key to overcome all these potential discrepancies.

2020AandA...637A..44N__Kerszberg_et_al._2017_Instance_1

Among the existing IACT systems, HESS has the largest FoV and hence provides the highest sensitivity for the diffuse γ-ray flux. Its electron spectrum analysis technique could be directly used to obtain a measurement of the diffuse Galactic γ-ray flux above energies of several TeV in the Galactic Ridge (|l| 30°, |b| 2°) region; see Figs. 3 and 4. A multi-year exposure of HESS could be sufficient for detection of the diffuse emission even from regions of higher Galactic latitude. This is illustrated in Fig. 5, where thick blue and red data points show mild high-energy excesses of the electron spectra derived by Kraus (2018), Kerszberg et al. (2017), Kerszberg (2017) over broken power-law models derived from the fits to lower energy data. Comparing these excesses with the level of the IceCube astrophysical neutrino flux and with the Fermi/LAT diffuse sky flux from the region |b| > 7° (corresponding to the data selection criterium of HESS analysis Kerszberg et al. 2017; Kerszberg 2017) we find that the overall excess flux levels are comparable to expected diffuse γ-ray flux from the sky region covered by the HESS analysis (the quoted systematic error on the electron flux is Δlog(EFE) ≃ 0.4). The overall excesses within 805 and 1186 h of HESS exposures (Kraus 2018; Kerszberg 2017) are at the levels of >4σ for the analysis of Kraus (2018) and 1.7σ for the analysis of Kerszberg (2017). A factor-of-ten longer exposure (which is potentially already available with HESS) could reveal a higher significance excess at the level of up to 5σ. Such an excess is predicted in a range of theoretical models including interactions of cosmic rays injected by a nearby source (Andersen et al. 2018; Neronov et al. 2018; Bouyahiaoui et al. 2019) or decays of dark matter particles (Berezinsky et al. 1997; Feldstein et al. 2013; Esmaili & Serpico 2013; Neronov et al. 2018) or a large-scale cosmic ray halo around the Galaxy (Taylor et al. 2014; Blasi & Amato 2019).

2019ApJ...887...26O__Garon_et_al._2019_Instance_1

In an effort to understand the character of RG encounters with ICM shocks and the observable consequences, we have initiated an MHD simulation study of such interactions. The simulations include passive, but energy-dependent transport of CRe, so that resulting radio synchrotron properties can be modeled appropriately. Nolting et al. (2019a, 2019b) explored interactions between ICM-strength shocks and lobed RGs. Here, we extend that work to include such shock interactions with so-called “narrow-angle tail” (NAT) RGs. NATs, which are especially common in clusters, (e.g., Blanton et al. 2001; Garon et al. 2019) result from sustained relative motion between an RG and its ambient ICM; that is, the RG effectively evolves in a wind that deflects the RG jets and their discharge downwind. The NATs can extend multiple hundreds of kpc from their AGN sources, and, so long as the AGN jets remain “powered on,” simulations suggest that NAT tails remain dynamic and highly inhomogeneous (O’Neill et al. 2019). Their considerable lengths and elongated morphologies make them interesting candidates to supply CRe at ICM shocks to form radio relics. We refer readers to O’Neill et al. (2019) and references therein for details of NAT formation dynamics. We do provide in Section 2 an outline of salient features of the simulated NAT presented in O’Neill et al. (2019) that are especially relevant to the present work. Our focus in this paper is on the interactions of shocks with such a previously formed NAT. Specifically, we use new three-dimensional (3D) MHD simulations to see what happens when plane shocks moving transverse to the NAT major axis collide with the NAT. That geometry is both relatively simple, and, on the face of it, perhaps the best able to form a radio relic in such a manner. Our study includes cases with shock Mach numbers, , in order to span the range of shock strengths most often mentioned in this context. As in the aforementioned studies, we carry out synthetic, frequency-dependent radio synchrotron observations of our simulated objects in order to better understand the relationships between their dynamical and observable properties.

2016ApJ...826..106G__Mazzarella_et_al._2012_Instance_1

The galaxy merging process can cause enhanced accretion onto the central SMBHs and thus initiate activity. One or both of the dual SMBHs may be active or re-activated. Simulations (e.g., Van Wassenhove et al. 2012) suggest that simultaneous activity is mostly expected at the late phases of mergers, at or below 10 kpc scale separations. Additionally, the recent work of Fu et al. (2015b) indicates that mergers trigger and tend to synchronize activity at separations of a few kiloparsecs. Therefore dual AGNs are expected to be observed, but they are not easily resolvable with current observing facilities. The number of convincing dual AGN systems, mostly detected by X-ray and radio observations (e.g., Beswick et al. 2001; Junkkarinen et al. 2001; Komossa et al. 2003; Hudson et al. 2006; Bianchi et al. 2008; Fu et al. 2011b; Koss et al. 2011, 2012; Mazzarella et al. 2012; Liu et al. 2013; Comerford et al. 2015; Müller-Sánchez et al. 2015), are relatively few compared to the theoretically predicted abundance (see also, Komossa & Zensus 2016). While very long baseline interferometric (VLBI) radio observations currently provide the highest spatial resolutions, making them a promising tool for the detection of dual AGNs6 6 In fact, thanks to its superior resolution, the first binary AGN was discovered using VLBI technique (Rodriguez et al. 2006). , only about 10% of AGNs are radio-loud, therefore dual radio-emitting active nuclei would be quite rare. Moreover, when trying to confirm the existence of dual AGNs in candidate sources, non-detection in the radio bands does not immediately falsify the dual AGN hypothesis, because radio-quiet nuclei may also be present. Burke-Spolaor (2011) searched the archival VLBI observations of 3114 radio-emitting AGNs for sources containing compact double sources. Only one source (B3 0402+379) was found with a double nucleus with separation of about 7 pc, which was already known from the observations of Rodriguez et al. (2006).

2021MNRAS.503.2973C__Carvalho_et_al._2020_Instance_1

As a first approximation, we consider that most part of the accretion luminosity is emitted in the soft X-ray spectrum (10 eV–10 keV), thus, soft X-ray luminosity is simply estimated by using the mass accretion parameter, $\dot{m}$(1)$$\begin{eqnarray*} L_{\rm AC}= \frac{GM\dot{m}}{R}, \end{eqnarray*}$$where M and R are the mass and radius of the object, respectively, which can be either a white dwarf, neutron star, or black hole. Mass–radius relation is employed to estimate radius in case of white dwarfs and neutron stars (see e.g. Barstow 1991, 1993; Lopes & Menezes 2012; Berti 2013; Carvalho, Marinho & Malheiro 2018; de Santi & Santarelli 2019; Carvalho et al. 2020), and Schwarzschild radius is taken for black holes. We reinforce that the model above is only a way to estimate the X-ray flux produced by compact objects due to accretion. The accretion rate, $\dot{m}$, is the parameter which defines the luminosity for a given system. However, in this work, we will not study any particular object, instead we will take fiducial values for mass and radius of the compact object along with standard values for snow line distances to estimate the time-scales ices need to reach chemical equilibrium. So, the model does not define if the compact object is isolated or in a binary system. We assume that accretion rate, $\dot{m}$, is constant on time, which means that the systems we are studying are well behaved, not presenting any sudden infall of matter, bursts, substantial outflows, or strong winds. The main aim, as stated, is to obtain an estimate of the time-scales for ices to reach chemical equilibrium. The assumption of constant accretion rate is feasible in most cases because the time-scale for ices close to compact systems to reach chemical equilibrium is smaller than the time-scale for changes on mass, as one can notice from power-law parametrizations (Chen et al. 2019), where we see that $\dot{m}$ scales with Myr or Gyr, while the time-scales for chemical equilibrium are at most 0.1 Myr for distances smaller than 100 LY, as we are going to show.

2019MNRAS.484.3785B__Huchra_&_Sargent_1973_Instance_1

Once we have the completeness corrections, we can estimate the relative luminosity function of SNe Ia, which we show in Fig. 3. In the left panel we show the observed distribution of absolute magnitudes. The black histogram shows the full sample, and the red histogram shows the volume-limited sample; the dot--dashed blue histogram shows the distribution of the volume-limited LOSS sample (Li et al. 2011b). The volume-limited samples have a higher fraction of low-luminosity SNe Ia than the full magnitude-limited sample. We convert the ASAS-SN distributions into estimates of the true luminosity function using the V/Vmax method (Schmidt 1968; Huchra & Sargent 1973; Felten 1976). For each SN, we compute the maximum volume (Vmax) in which the SN could be recovered by a survey with a limiting magnitude mV = 16.8. This is an empirical limiting magnitude; this value produces a median value of V/Vmax close to 0.5, which is to be expected if sources uniformly populate the survey volume. We compute the relative luminosity function for each bin in absolute magnitude centred on M as (2) \begin{eqnarray*} \Phi (M) = \sum _{i=1}^{N} \frac{1}{V_{M,i}} \times w_i \times (1+z_i), \end{eqnarray*} where the sum is over all the SNe within the bin. The weights $w$i correct for the incompleteness given the apparent peak magnitude of each SN, and the factor of (1 + $z$) accounts for time dilation. The results are shown in the right panel of Fig. 3. The black circles show the relative luminosity function computed from the full sample, the red squares show the results for the volume-limited sample, and the blue crosses show the control-time weighted counts from Li et al. (2011b). The luminosity functions are normalized to the bin at MV = −19. In this paper we are not aiming for an absolute rate calibration. The shape of the relative luminosity function is consistent with the volume-limited luminosity function presented in Li et al. (2011b). We fit a Schechter (1976) function (3) \begin{eqnarray*} \phi (L) \propto \left(\frac{L}{L_*}\right)^{\alpha } \exp \left(-\frac{L}{L_*}\right) \end{eqnarray*} to the relative luminosity function of both the full and volume-limited samples, where α is the faint-end slope, and L* (alternatively M* in magnitude space) determines the ‘knee’ of luminosity function. Our fits are shown in Fig. 3 as dashed lines. We find (α, M*) corresponding to (1.3 ± 0.4, −18.1 ± 0.1) and (2.1 ± 0.3, −17.8 ± 0.1) for the full sample and the volume-limited sample, respectively.

2016AandA...592A..19C__Davies_et_al._1993_Instance_1

The downsizing scenario is evident in several cases of galaxy evolution (Fontanot et al. 2009). In the case of ETGs at z ~ 0, one of the first pieces of observational evidence can be referred to the studies of Dressler et al. (1987), Faber et al. (1992) and Worthey et al. (1992), who found more massive elliptical galaxies to be more enriched in α-elements than less massive ones. These works suggested selective mass-losses, different initial mass functions (IMF) and/or different star-formation timescales as possible explanations for the high level of [α/Fe]. Subsequent studies found the same trend of [α/Fe] with mass (Carollo et al. e.g 1993; Davies et al. 1993; Bender et al. 1993; Thomas et al. 2005; 2010; McDermid et al. 2015), leading to the dominant interpretation that in more massive ETGs, the duration of star formation was substantially shorter than in less massive ones, with timescales short enough (e.g. 0.5 Gyr) to avoid the dilution of the α element abundance (produced by Type II supernovae) by the onset of Fe production by Type Ia supernovae. This is considered to be one of the main pieces of evidence for the shortening of star formation. The age of the ETG stellar populations at z ~ 0 also show evidence of downsizing, with more massive objects being older than less massive ones. These results have been derived both in clusters (Thomas et al. 2005, 2010; Nelan et al. 2005) and in the field (Heavens et al. 2004; Jimenez et al. 2007; Panter et al. 2007; see also Renzini 2006). Most of these studies are based on fitting individual spectral features with the Lick/IDS index approach (Burstein et al. 1984; Worthey et al. 1994) which allows us to mitigate the problem of the age-metallicity degeneracy (Graves & Schiavon 2008; Thomas et al. 2005, 2010; Johansson et al. 2012a; Worthey et al. 2013). However, more recently, other approaches based on the full-spectrum fitting have been developed (e.g. STARLIGHT, Cid Fernandes et al. 2005; VESPA, Tojeiro et al. 2009, 2013, and FIREFLY, Wilkinson et al. 2015), and applied to samples of ETGs at z ~ 0 (Jimenez et al. 2007; Conroy et al. 2014; McDermid et al. 2015). The results based on Lick indices are in general quite consistent with those of full spectral fitting within 10−30% (e.g. Conroy et al. 2014) and support the downsizing evolutionary pattern.

2017AandA...604A.112G__Hansen_2010_Instance_1

In stars, there are two components to describe the tidal interaction, equilibrium tides and dynamical tides. Equilibrium tides correspond to a large-scale hydrostatic adjustment of a body and the resulting flow due to the gravitational field of a given companion (Zahn 1966; Remus et al. 2012). It is usually employed in the framework of the constant time lag model (see Mignard 1979; Hut 1981; Eggleton et al. 1998; Bolmont et al. 2011, 2012), which allows a fast computation of the orbital evolution of the planet and works for all eccentricities (Hut 1981; Leconte et al. 2010). In this model, the dissipation of the kinetic energy of the equilibrium tide inside the star is often taken to be constant throughout the system evolution and calibrated on observations (Hansen 2010, 2012). While considering such a constant equilibrium tide dissipation is a sensible assumption, several studies have shown that this quantity might vary during the different phases of stellar evolution. For example, Zahn & Bouchet (1989) showed that the dissipation of the equilibrium tide by the turbulent friction in the convective envelope of late-type stars is strongest during their PMS. Using this theoretical framework, Villaver & Livio (2009, see also Verbunt & Phinney 1995 recalled that the variation of the semi-major axis of a planet induced by such friction can be expressed as a function of the ratio of the mass of the convective envelope to the total mass of the star, the ratio between the radius of the star and the orbital semi-major axis (to the power 8), and finally of a power of the ratio between the tidal period and the convective turnover timescale. This allows the loss of efficiency of tidal friction to be modelled for rapid tides (e.g. Zahn 1966; Goldreich & Keeley 1977). Because of the variations of these quantities during post-MS phases (e.g. Charbonnel et al. 2017), this could lead to a more efficient dissipation than during the MS. Finally, Mathis et al. (2016) demonstrated that the action of rotation on convection deeply modifies the turbulent friction it applies on the equilibrium tide. In the regime of fast rotation, which corresponds to the end of PMS and early MS phase, the friction is several orders of magnitude lower than in a model ignoring rotation. This may lead to a loss of efficiency of the dissipation of the equilibrium tide. This shows that care should be taken when assuming a calibrated constant dissipation of the equilibrium tide during the evolution of stars.

2016ApJ...827..104S__Risaliti_&_Lusso_2015_Instance_1

Using these quasar survey data, Clowes et al. (2013), Nadathur (2013), Einasto et al. (2014), and Park et al. (2015) found very large quasar groups and discussed the cosmological implications of the existence and properties of these extreme objects. Nadathur (2013), Einasto et al. (2014), and Park et al. (2015) pointed out that the cosmological interpretation by Clowes et al. (2013) of large quasar groups, which questions the validity of the cosmological assumption of homogeneity and isotropy, is misleading. They stressed the importance of a statistically precise analysis in order to draw conclusions on cosmological implications from the observation of one or a few extreme objects. Park et al. (2015) also emphasized that statistical comparison with cosmological simulations must be employed as well. The quasar survey data are also used for studies to constrain cosmological parameters (Han & Park 2015; Risaliti & Lusso 2015). Besides the studies directly related to cosmology, there are more studies exploring the clustering properties of quasars. Einasto et al. (2014) made a catalog of quasar groups with different linking lengths and examined their properties. They found that the characteristics of quasar groups such as number density, size, and richness, identified with linking lengths varying from 20 to 40 h−1 Mpc, are well correlated with those of galaxy superclusters. Therefore such quasar groups can be markers of galaxy superclusters. As a classical way to study clustering properties, correlation functions have been measured for quasars (AGNs in general) by many different groups (Shen et al. 2007b, 2009, 2013; Ross et al. 2009; Krumpe et al. 2010, 2012, 2015; Miyaji et al. 2011; Cappelluti et al. 2012; Richardson et al. 2012; Allevato et al. 2014; Eftekharzadeh et al. 2015). They have measured two-point cross-correlation functions (2PCCFs) between quasars and galaxies, and have found the typical mass of quasar-hosting dark matter halos (DMHs) and the dependence of the mass on quasar luminosity.

2018MNRAS.481..533L__Ilić,_Kovačević_&_Popović_2009_Instance_1

We refer to models for which the pressure P depends on the radial distance r from the central continuum source, (2) \begin{eqnarray*} P(r)\propto r^{-s} \, , \end{eqnarray*} as pressure-law models. In this work, we examine two limiting cases, s = 0 and s = 2, representing constant density and constant ionization parameter models, respectively. We here adopt a spherically symmetric BLR geometry spanning more than two decades in radial extent. This model is chosen for (i) its simplicity, and because (ii) we can compare our radial pressure-law models with the Local Optimally emitting Cloud model for this source presented by Korista & Goad (2001), which also adopts spherical symmetry. Here, we summarize the radial dependencies of various physical quantities for spherically symmetric pressure-law models; the derivations in this section follow Rees et al. (1989) and Goad et al. (1993). We make the simplifying assumption that the cloud temperature does not vary with radius; for solar composition, photoionization equilibrium is achieved at temperatures T ∼ 104 K across a wide range of ionization parameter (equation 5), and the gas temperature will therefore vary weakly with radius (e.g. Netzer 1990; Ilić, Kovačević & Popović 2009). For constant cloud temperatures, the cloud hydrogen gas density nH is proportional to the pressure, P, and so (3) \begin{eqnarray*} n_\mathrm{H}(r)\propto r^{-s} \, . \end{eqnarray*} Thus, s = 0 corresponds to a constant nH throughout the BLR. The ionization parameter U is defined as (4) \begin{eqnarray*} U(r)=\frac{Q_\mathrm{H}}{n_\mathrm{H}(r)4\pi r^2c} \, , \end{eqnarray*} where QH is the number of hydrogen-ionizing photons emitted by the central continuum source per second. Thus, s = 2 corresponds to a constant ionization parameter model, since: (5) \begin{eqnarray*} U(r)\propto r^{s-2} \, . \end{eqnarray*} The surface area per cloud, Ac, is proportional to $R_{\rm c}^2$, where Rc denotes the radius of a cloud. In general, Rc depends on the pressure P, and is therefore constant for s = 0. If we demand that the mass of each cloud is conserved as the clouds move radially outwards (i.e. clouds do not break up or coalesce within our region of interest), mass conservation implies that $R_c^3n_H={\rm constant}$. Thus, we obtain the relation (6) \begin{eqnarray*} A_{\rm c}(r) \propto R_{\rm c}^2(r) \propto r^{2s/3} \, . \end{eqnarray*} The column density of each cloud, Ncol, depends on the gas density and cloud radius: (7) \begin{eqnarray*} N_\mathrm{col}(r)\propto R_cn_\mathrm{H}\propto r^{-2s/3} \, . \end{eqnarray*} The above relations determine the local physical conditions, as parametrized by Ncol, nH and incident ionizing photon flux ΦH, at any radius in a spherically symmetric pressure-law BLR model. These conditions determine the local surface emissivity ε(r) of a BLR cloud at radius r (as determined via cloudy modeling; Section 3.1). The total luminosity for an emission line is then found by integrating over the distribution in cloud properties such that (8) \begin{eqnarray*} L_{\mathrm{line}}=4\pi \int _{r_{\mathrm{in}}}^{r_{\mathrm{out}}}\epsilon (r)A_{\rm c}(r)n_{\rm c}(r)r^2{\rm d}r \, , \end{eqnarray*} where rin  and rout  are the inner and outer BLR radii, respectively, Ac is the surface area of a single cloud, and nc is the local number density of clouds. As dust grains strongly absorb UV photons, routis chosen to approximately coincide with the distance at which dust grains can form and survive (≈140 light-days for NGC 5548).

2018AandA...614A..66S__Jiang_et_al._2008_Instance_1

Virtually all formulas that are currently used to estimate the merger time, τmer, are based on the idealised Chandraseckhar (1943) description of the deceleration caused by dynamical friction on a point mass (representing the satellite) travelling through an infinite, uniform, and collisionless medium (representing the host). They are mostly prescriptions inferred from the analytic or semi-analytic modelling of mergers (e.g. Lacey & Cole 1993; van den Bosch et al. 1999; Taffoni et al. 2003; Gan et al. 2010; Petts et al. 2015; Silva et al. 2016) or parametric equations tuned by direct comparison with the outcome of numerical simulations of collisions of live galaxy pairs (e.g. Boylan-Kolchin et al. 2008; Jiang et al. 2008; Just et al. 2011; McCavana et al. 2012; Villalobos et al. 2013), or both (e.g. Colpi et al. 1999). However, although several decades of studies of galactic mergers have allowed reaching a general consensus on what probably are the most relevant parameters in any process of this type, the extent of their impact is still a matter of debate. Factors such as continued mass losses due to tidal interactions, the drag force exerted by tidal debris, the re-accretion of some of this material onto the colliding galaxies, or the mutual tidal distortion of their internal structures are elements that introduce nontrivial uncertainties in any attempt to calculate τmer using analytical expressions. To these difficulties, which are caused by physical complexity, one must add the lack of a 100% standard methodology regarding the way mergers are tracked. The starting point of this work is precisely the definition of a new metric for calculating τmer, which is an obligatory first step to compare the outcomes of different experiments under equal conditions and derive universally valid formulas, in a form that is suitable for major mergers. With this aim, we have run a suite of nearly 600 high-resolution N-body simulations of isolated major mergers, with which we further explored the dependence of the merger time on a range of orbital parameters and on the mass-ratios, spins, and morphologies of the progenitors that are representative of such systems.

2018MNRAS.477L..80K__Colella_&_Woodward_1984_Instance_1

The numerical schemes in this work are essentially the same as those in Kuroda et al. (2016). Regarding the metric evolution, we evolve the standard BSSN variables (Shibata & Nakamura 1995; Baumgarte & Shapiro 1999; Marronetti et al. 2008) with a finite-difference scheme in space and with a Runge–Kutta method in time, both in fourth-order accuracy. The gauge is specified by the ‘1+log’ lapse and by the Gamma-driver-shift condition. Regarding the radiation-hydrodynamic evolution, the conservation equation $\Delta_\alpha T^{\alpha \beta }_{\rm (total)}=0$ is solved using the piecewise parabolic method (Colella & Woodward 1984; Hawke, Löffler & Nerozzi 2005). $T^{\alpha \beta }_{\rm (total)}$ is the total stress-energy tensor, (1)\begin{equation*}T_{\rm (total)}^{\alpha \beta } = T_{\rm (fluid)}^{\alpha \beta } +\int \mathrm{ d}\varepsilon \sum _{\nu \in \nu _e, \bar{\nu }_e, \nu _x}T_{(\nu , \varepsilon )}^{\alpha \beta }, \end{equation*} where $T_{\rm (fluid)}^{\alpha \beta }$ and $T_{(\nu , \varepsilon )}^{\alpha \beta }$ are the stress-energy tensor of the fluid and the neutrino radiation field, respectively. We consider three-flavour of neutrinos ($\nu \in \nu _e, \bar{\nu }_e, \nu _x$) with νx denoting heavy-lepton neutrinos (i.e. νμ, ντ, and their antiparticles). ϵ represents the neutrino energy measured in the comoving frame which logarithmically covers from 1 to 300 MeV with 12 energy bins. Employing an M1 analytical closure scheme (Shibata et al. 2011), we solve spectral neutrino transport of the radiation energy and momentum, based on the truncated moment formalism (e.g. Kuroda et al. 2016; Roberts et al. 2016; Ott et al. 2018). We include the gravitational red- and Doppler-shift terms to follow the neutrino radiation field in highly curved space–time around BH. Regarding neutrino opacities, the standard weak interaction set in Bruenn (1985) plus nucleon–nucleon bremsstrahlung (Hannestad & Raffelt 1998) is taken into account.

2016MNRAS.457..212S__Singh,_Sami_&_Dadhich_2003_Instance_1

One of the first and simplest proposed Friedmann–Robertson–Walker (FRW) cosmological model is the Λ cold dark matter (ΛCDM) universe, which involves Einstein's cosmological constant Λ. This standard model of cosmology, which is also referred to as the concordance model, assumes that the total energy density ρ of the universe is made up of three components, namely matter ρm (baryonic and dark matter), radiation ρr, and dark energy or vacuum energy ρΛ, which produces the necessary gravitational repulsion. In this model, dark energy which has an equation of state (EOS) ωΛ = pΛ/ρΛ = −1, is a property of the space itself and its density ρΛ = −pΛ = Λc4/8πG is constant, such that as the universe expands the constant vacuum energy density will eventually exceed the matter density of the universe which is ever decreasing. The spatially flat ΛCDM model dominated by vacuum energy with ΩΛ ∼ 0.70, with the rest of the energy density being in the form of non-relativistic cold dark matter with Ωm ∼ 0.25 and non-relativistic baryonic matter with Ωb ∼ 0.05, fits observational data reasonably well (Riess et al. 1998; Permutter et al. 1999; Knop et al. 2003; Riess et al. 2004). However, the main problem in this model is the huge difference of about 10120 orders of magnitude between the observed value of the cosmological constant and the one predicted from quantum field theory; known as the cosmological constant problem (Weinberg 1989). Another issue is the so called coincidence problem which expresses the fact that although in this model the matter and dark energy components scale differently with redshift during the evolution of the universe, both components today have comparable energy densities, and it is unclear why we happen to live in this narrow window of time. Besides these main issues, there are other inherent problems faced by the ΛCDM, some of which arose as a result of recent observations that are in disagreement with the model's predictions. For example, in order to account for the general isotropy of the cosmic microwave background (CMB), the standard model invokes an early period of inflationary expansion (Kazanas 1980; Guth 1981; Linde 1982). However, the latest observations by Planck (Planck Collaboration XXIII 2003) indicate that there may be some problems with such an inflationary scenario (Ijjas, Steinhardt & Loeb 2013; Guth, Kaiser & Nomura 2014). It was partly due to these issues of the standard ΛCDM, that during the last decade several alternative dark energy models have been proposed and tested with observations. In these models the dark energy density component ρde is not constant and in most cases ωde = pde/ρde depends on time, redshift, or scale factor. For example in some of these so called dynamical dark energy models, late time inflation is achieved using a variable cosmological term Λ(t) (Ray et al. 2011; Basilakos 2015) sometimes taken in conjunction with a time dependent gravitational constant G(t) (Ray, Mukhopadhyay & Dutta Choudhury 2007; Ibotombi Singh, Bembem Devi & Surendra Singh 2013). Other sources of dark energy include scalar fields such as quintessence (Peebles & Ratra 2003), K-essence (Armendariz-Picon et al. 2001) and phantom fields (Singh, Sami & Dadhich 2003). An alternative approach to the dark energy problem relies on the modification of Einstein's theory itself such that in these alternative theories of gravity, cosmic acceleration is not provided solely by the matter side Tμν of the field equations, but also by the geometry of spacetime. These theories include the scalar-tensor theory with non-minimally coupled scalar fields (Barrow & Parsons 1997; Bertolami & Martins 2000), f(R) theory (Tsujikawa 2008), conformal Weyl gravity (Mannheim 2000) and higher dimensional theories such as the Randall–Sundrum (RS) braneworld model (Randall & Sundrum 1999), and the braneworld model of Dvali–Gabadadze–Porrati (DGP) (Dvali, Gabadadze & Porrati 2000). Over the last few years considerable interest has been shown in the simple FRW linearly expanding (coasting) model in Einstein's theory with a(t) ∝ t, H(z) = H0(1 + z). Like the ΛCDM the total energy density and pressure in this model are expressed in terms of matter, radiation and dark energy components, such that p = ωρ with ρ = ρm + ρr + ρde and p = pr + pde (since pm ≈ 0), but it includes the added assumption ω = −1/3, i.e. the cosmic fluid acting as the source has zero active gravitational mass. So this would definitely exclude a cosmological constant as the source of the dark energy component in this case. The model was first discussed by Kolb (1989) who referred to this zero active mass cosmic fluid as ‘K-matter’. Interest in this model has been revived recently after it was noted (Melia 2003) that in the standard model the radius of the gravitational horizon Rh(t0) (also known as the Hubble radius) is equal to the distance ct0 that light has travelled since the big bang, with t0 being the current age of the universe. In the ΛCDM this equality is a peculiar coincidence because it just happens at the present time t0. It was then proposed (Melia 2007, 2009; Melia & Shevchuk 2012) that this equality may not be a coincidence at all, and should be satisfied at all cosmic time t. This was done by an application of Birkhoff's theorem and its corollary, which for a flat universe allows the identification of the Hubble radius Rh with the gravitational radius Rh = 2GM/c2, given in terms of the Misner–Sharp mass $M = (4\pi /3)R_{{\rm h}}^{3}(\rho /c^2)$ (Misner & Sharp 1964). The added assumption of a zero active gravitational mass ρ + 3p = 0 implies (Melia & Shevchuk 2012) that Rh = ct or H = 1/t for any cosmic time t. This linear model became known as the Rh = ct universe. Unlike the ΛCDM/ωCDM which contains at least the three parameters H0, Ωm and ωde, the Rh = ct model depends only on the sole parameter H0, so that for example the luminosity distance used to fit Type Ia supernova data (Melia 2009) is given by the simple expression dL = (1 + z)Rh(t0)ln (1 + z). Also while the ΛCDM would need inflation to circumvent the well-known horizon problem, the Rh = ct universe does not require inflation. One should also point out that the condition Rh = ct is also satisfied by other linear models such as the Milne universe (Milne 1933), which however has been refuted by observations. Unlike the Rh = ct model discussed here, the Milne universe is empty (ρ = 0) and with a negative spatial curvature (k = −1). As a result of these properties its luminosity distance is given by $d_{L}^{\rm {Milne}} = R_{{\rm h}}(t_0)(1 + z)\sinh [\ln (1+z)]$, and it was shown that this is not consistent with observational data (Melia & Shevchuk 2012). In the last few years the Rh = ct universe received a lot of attention when it was shown (Melia & Maier 2013; Wei, Wu & Melia 2013, 2014a, 2014b, 2015; Melia, Wei & Wu 2015) that it is actually favoured over the standard ΛCDM (and its variant ωCDM with ω ≠ −1) by most observational data. This claim has been contested by Bilicki & Seikel (2012) and Shafer (2015) who argued that measurement of H(z) as a function of redshift and the analysis of Type Ia supernovae favoured the ΛCDM over the Rh = ct universe. However, this was later contested by Melia & McClintock (2015) who showed that the Rh = ct was still favoured when using model-independent measurements that are not biased towards a specific model. Others (see for example van Oirschot, Kwan & Lewis 2010; Lewis & van Oirschot 2012; Mitra 2014) have also criticized the model itself, particularly the validity of the EOS ω = −1/3 (Lewis 2013). These and other criticisms have been addressed by Bikwa, Melia & Shevchuk (2012); Melia (2012) (see also Melia 2015 and references therein.) As pointed out above the Rh = ct model would still require a dark energy component ρde, albeit not in the form of a cosmological constant. So the obvious question at this point would be: what are the possible sources for this component that together with the matter and radiation components will give the required total EOS, ω = −1/3? The purpose of this paper is to answer this question by discussing the various possible sources of dark energy that are consistent with this EOS. Since the radiation component ρr at the present time t0 is insignificant (at least for the ΛCDM with which this model has been compared) we assume that the total energy density ρ = ρde + ρm and the total pressure p = pde (pm ≈ 0), as is normally done in the other alternative dynamical dark energy models found in the literature. So in the next three sections we examine three possibilities for the source of dark energy in the Rh = ct model, namely a variable cosmological term Λ(t), a non-minimally coupled scalar field in Brans–Dicke theory which is equivalent to a variable gravitational constant G(t), and finally quintessence represented by a minimally coupled scalar field ϕ. We show that although the first two sources are consistent with the model, they are both unphysical, which leaves the third source of quintessence as the viable source of dark energy in the Rh = ct universe. Results are then discussed in the Conclusion. Unless otherwise noted we use units such that G = c = 1.

2020ApJ...893..124Z___2017_Instance_1

The terrestrial magnetosheath, downstream of the bow shock generated by the interaction between the supersonic solar wind and the Earth’s magnetosphere, is representative of turbulence downstream of collisionless plasma shock in the universe. Usually, the downstream sheath regions have larger density, temperature, mean magnetic intensity, and compressibility, and higher plasma β, as compared to the upstream solar wind (Sahraoui et al. 2006; Alexandrova 2008; Hadid et al. 2015; Huang et al. 2016, 2017). Abundant unstable kinetic wave modes and multiscale coherent structures exist in the magnetosheath. For instance, mirror mode, ion cyclotron wave, kinetic Alfvén wave, and so forth are routinely observed (Sahraoui et al. 2006; Lucek et al. 2005; Alexandrova 2008; Zhao et al. 2018; Zhang et al. 2018). Hence, the turbulent energy in the kinetic range might not only consist of energy cascading from larger scales, but also comprise direct injection of energy due to local instability at these scales. This raises an interesting question: what are the differences and similarities of turbulence intermittency between solar wind and magnetosheath? This is one goal of our research. When going down to the ion scales, PSD(δB) steepens, while PSD(δE) is relatively flattened (Chen & Boldyrev 2017; Matteini et al. 2017). The coexistence of steep PSD(B) and flat PSD(E) is caused by the ion demagnetization, which gives rise to the Hall term and/or quasi-electrostatic field (Cramer 2001; Schekochihin et al. 2009). If monofractal scaling is a common phenomenon for magnetic fluctuations at kinetic scales both in the solar wind and in the magnetosheath, what would happen to the electric field intermittent fluctuations? is the electric field intermittency monofractal as well, or is it totally different from the magnetic one? Studying the above questions would help us better understand the characteristics and nature of turbulence at kinetic scales, which is more electrostatic than that at magnetohydrodynamic (MHD) scales.

2019ApJ...870...70S__Bosch_2016_Instance_1

The large differences noted in Figure 9 are surely related to the different approximations underlying both the complex hydrodynamical simulations of ILLUSTRIS-1 and our simplified approach. As recently discussed by Chua et al. (2017), the distribution of noncollisional matter (i.e., stars and DM) can effectively cool down in the cores of galaxies formed in the ancient epochs, at variance with those born more recently. This provides them a much better chance of survival, or at least stronger resilience against potentially disruptive interactions that they would surely undergo throughout the cluster environment. A key role is also played by the host cluster itself, because the mass profile of this latter becomes steeper and steeper in the core due to dynamical relaxation. Consequently, the environment gets denser with time; this trend is found to be quantifiable in terms of the number of dynamical times elapsed since the cluster formation (e.g., Jiang & van den Bosch 2016). Therefore, each galaxy has to attain a sufficiently cold core in order to first survive in the cluster environment and then grow by accretion of matter from the ICM and mergers with other substructures. Given these preliminary considerations, which explain the evaporation of galaxies while falling into more massive halos, the inclusion of the BM physics in this context is crucial, and its effects are still not fully explored. Based on a systematic comparison of the full-physics and DM-only ILLUSTRIS suites, Chua et al. (2017) claimed that galactic winds and photoionization from UV radiation may effectively inhibit mass aggregation, at least at the low-mass end and in certain regimes of redshifts (see also Despali & Vegetti 2017 for similar reasoning for DM subhalos of intermediate masses inside ILLUSTRIS and EAGLE simulations; Schaye et al. 2015). In particular, photometric data inside ILLUSTRIS are bound to whether stellar particles, i.e., stellar populations, are found inside a bound subhalo. Recalling that in ILLUSTRIS-1, the average gas cell mass is 8.86 × 105 M⊙, from which integrated stellar populations might stem, and that a prescription for subgrid physics is always needed to follow up the integrated properties of all kinds of particles, these could be a substantial limit on state-of-the-art hydrodynamical simulations in describing photometry, especially for galaxies inside more recently formed DM halos of lower masses. On the other side, our numerical approach, at the basis of the buildup of samples inside clusters, provides a population of galaxies that should reflect the observational evidence of the mass–radius relation at z ∼ 0 (Chiosi et al. 2012). If observed galaxies are merely those that survived all disrupting interactions they underwent, then at least a minimum fraction of the sample (not as close to zero as the one of ILLUSTRIS-1) should have formed in an evidently large range of look-back time. However, acknowledging that our approach might not be the most refined one, we feel that the best interpretation might be a middle ground between the two models.

2022AandA...663L...4L___2020_Instance_2

While previous studies mainly focused on the optical-UV properties of a MAD in RLAGNs, for the first time we try to investigate their X-ray properties in this work. The origin of X-ray emission in RLAGNs is still under debate, which may come from a corona, jet, or both. In observations, there is a big difference between the X-ray properties of radio-quiet AGNs (RQAGNs) and RLAGNs. Firstly, the average X-ray flux in RLAGNs is found to be 2–3 times higher than that in RQAGNs (e.g., Zamorani et al. 1981; Wilkes & Elvis 1987; Li & Gu 2021). Secondly, Laor et al. (1997) reported that RLAGNs have harder 2–10 kev X-ray spectra than RQAGNs by compiling a sample of 23 quasars observed with ROSAT, which was subsequently confirmed by Shang et al. (2011) with a larger sample. Comparing the X-ray spectrum of 3CRR quasars and that of radio-quiet quasars, Zhou & Gu (2020) also gave a similar result. In addition, the X-ray reflection features of RLAGNs are weaker than those of RQAGNs (Wozniak et al. 1998). All of these results seem to indicate that the contribution of a jet to X-ray spectra cannot be neglected. However, several recent works suggested a totally different result. First, the slope of LUV − LX is found to be consistent for RLAGNs and RQAGNs (Zhu et al. 2020, 2021; Li & Gu 2021). Second, Gupta et al. (2018, 2020) discovered that the distributions of X-ray photon spectral indices between RLAGNs and their radio-quiet counterpart are very similar (see Zhu et al. 2021 either). This opposite conclusion may be due to the effect of sample selection. The sample of Gupta et al. (2018, 2020) was X-ray selected (and optically selected for the sample of Zhu et al. 2021), which may lead to the radio jet power being very feeble compared to the bolometric luminosity in most of the RLAGNs. These weakly jetted RLAGNs can therefore have different X-ray photon indices compared to the strong jetted RLAGNs, such as the 3CRR quasars of Zhou & Gu (2020). P15 also indicated that the weakly jetted RLAGNs have similar αEUV as RQAGNs. However, interestingly, Markoff et al. (2005) demonstrated that both the corona model and the jet model can fit the X-ray data of some Galactic X-ray binaries well and that the jet base may be subsumed to corona in some ways. The 3CRR quasars are low frequency radio selected and have a strong jet on a large scale. However, it is still unclear whether all the objects with a strong jet harbor a MAD, or just containing MAD when jet is firstly launching millions of years ago. We focus on the RLAGNs with an EUV deficit in this work, which should possess a MAD in the inner disk region as suggested by P15. The presence of a MAD surrounding the black hole may bring a remarkable difference to the X-ray emissions since the structure of disk-corona greatly changes in the case of MAD (e.g., Tchekhovskoy et al. 2011; McKinney et al. 2012; White et al. 2019). In theory, it has been suggested that X-ray emission increases when an advection-dominated accretion flow (ADAF) becomes a MAD in its inner region (Xie & Zdziarski 2019). Nevertheless, how MAD affects the disk-corona corresponding to the X-ray emission of quasars is still an open issue. This work can constrain a future theoretical model for MAD in RLAGNs.