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2020ApJ...892L..10Y__Macchi_2013_Instance_2

In this section, we consider the plasma properties under the propagation of strong waves. In strong waves, the motion of electrons in the plasma becomes relativistic. However, different from free electrons that have a relativistic drift velocity in the direction of the incident electromagnetic wave (see Section 3.1), in plasma the space-charge potential is important in preventing the drift of electrons (Waltz & Manley 1978). For nonrelativistic electrons in plasma, if the wave duration τ is much larger than c/ωp, where is the plasma frequency, the drift velocity would be close to zero (Waltz & Manley 1978; Sprangle et al. 1990b). In this case, electrons in plasma under a strong wave would have a typical Lorentz factor ( ) similar to that (γ) in the laboratory frame, so that is satisfied. Due to the relativistic and magnetic force effects, the propagation and dispersion properties of an electromagnetic wave depend on its amplitude. For a circular polarized wave, the dispersion relation in the laboratory frame is given by (e.g., Gibbon 2005; Macchi 2013; Macchi et al. 2013; see the Appendix) 11 The dispersion relation of strong electromagnetic waves is altered due to the effective electron mass increased by the relativistic effect (e.g., Sarachik & Schappert 1970; Gibbon 2005; Macchi 2013). One can define the effective plasma frequency as 12 so that the wave can propagate in the region where . With respect to the nonrelativistic linear case, this is known as relativistically self-induced transparency. We note that since the dispersion depends on the electromagnetic field amplitude in the nonlinear case, the dispersion relation must be taken with care. The propagation of a pulse will be affected by the complicated effects of nonlinear propagation and dispersion, and finally the spatial and temporal shape of the pulse itself would also be modified. In particular, for linear polarization, the relativistic factor γ is not a constant (see Section 3.1). The propagation of the linearly polarized wave with a relativistic amplitude would lead to generation of the higher-order harmonics. Sprangle et al. (1990b) proved that the propagation of the first harmonic component, i.e., of the “main” wave, is still reasonably described by Equation (11) with . Thus, we will directly adopt Equation (11) in the following discussion.

2021ApJ...921...20H__Hayasaki_et_al._2013_Instance_1

It is still debated if and how all the stellar debris efficiently circularizes via the stream–stream collision. Some hydrodynamical simulations show that the TDE disk retains a significantly elliptical shape because the orbital energy is not dissipated efficiently enough to reduce the eccentricity of the entire disk to zero in a reasonable time (Guillochon et al. 2014; Shiokawa et al. 2015; Sądowski et al. 2016). Lu & Bonnerot (2020) show that a significant fraction of the debris can become unbound causing an outflow from the self-interaction region. Nevertheless, the debris that remains bound eventually contributes to the accretion flow around the SMBH. This part of the debris stream will finally be circularized by energy dissipation, leading to the formation of a small, initially ring-like, accretion disk around the black hole (Hayasaki et al. 2013; Bonnerot et al. 2016; Hayasaki et al. 2016). Note that, in an inefficient debris circularization case, the subsequent fallback material interacts with the outer elliptical debris so that their combined effect on the subsequent evolution of the initial ring is negligible. Angular momentum conservation allows us to estimate the circularization radius of the stellar debris, which is given by 2 r c = ( 1 + e * ) r p , = 1 + e * β r t , where e* is the orbital eccentricity of the stellar orbit, rp = rt/β is the pericenter distance radius. If debris circularization takes place only through dissipation at the self-interaction shock, the circularization timescale for the non-magnetized, most tightly bound debris can be estimated based on the ballistic approximation (Bonnerot et al. 2017) as 3 t circ ≈ 8.3 η − 1 β − 3 M bh , 6 − 5 / 3 t mtb ∼ 0.93 η 1.0 − 1 β − 3 M bh , 6 − 7 / 6 m * , 1 − 1 r * , 1 3 / 2 yr , where the orbital period of the stellar debris on the most tightly bound orbit: 4 t mtb = π 2 1 Ω * M bh m * 1 / 2 ≈ 0.11 M bh , 6 1 / 2 m * , 1 − 1 r * , 1 3 / 2 yr , Ω * = Gm * / r * 3 is the dynamical angular frequency of the star, and we introduce η(≤1) as the circularization efficiency which represents how efficiently the kinetic energy at the stream–stream collision is dissipated and the most efficient (η = 1) case corresponds to that of Bonnerot et al. (2017). Note that tcirc is not the circularization timescale of all the stellar debris. Our interest here is in the circularization timescale and radius of the most tightly bound debris because the accretion of this debris contributes most to the delayed X-ray peak luminosity in terms of the emitted energy.

2016ApJ...816...41Y__Litvinenko_&_Wheatland_2005_Instance_1

Some previous observations have shown that surface motions (e.g., shearing motions and converging motions) acting on preexisting coronal fields to form filaments and filament channels always act over a short period of a few days (Gaizauskas et al. 1997; Schmieder et al. 2004; Wang & Muglach 2007; Yan et al. 2015; Yang et al. 2015) to a period of months (Gaizauskas et al. 2001). However, in this event, the filament is formed within about 20 minutes. On this timescale, only the reformation of the filament in the same filament channel after the partial filament eruption has been observed (Tripathi et al. 2009a; Joshi et al. 2014). The rapid formation of the filament is a rare observation that has not been previously reported. Even though our event follows the “head-to-tail” scenario, there is a little difference. It is worth noting that the “head-to-tail” scenario seems to support the idea that the filament is supported by sheared arcades (Litvinenko & Wheatland 2005; Welsch et al. 2005). However, our event may present a picture in which the filament is supported by a twisted flux rope. Therefore, we could not fully exclude any other explanations, although we are inclined toward the “head-to-tail” scenario of Martens & Zwaan. Twisted structures in filaments have been previously observed during their eruptions (Wang et al. 1996; Bi et al. 2012, 2015; Li & Zhang 2013; Yan et al. 2014; Yang et al. 2014; Filippov et al. 2015; Raouafi 2009). In particular, Bi et al. (2012, 2015) found that the erupted filaments appear to be composed of two intertwined twisted flux ropes. However, in the present case, it is found that the flux rope appeared in the course of the formation of the filament, and as reported by Li & Zhang (2013; Yang et al. 2014), it can be observed in all seven AIA EUV lines formed from 0.05 MK to 11 MK. The AIA 304 Å observations (see panel (e) in Figure 2) also indicate that the filament may be composed of two sets of intertwined dark threadlike structures. These observations may be direct evidence that the magnetic structure of the formed filament is a flux rope. As mentioned before, the sigmoids and the elongated EUV channel-like structures are evidences for the existence of flux ropes in the higher solar atmosphere. More recently, observational evidence of the detailed structure and evolution of flux ropes in the lower solar atmosphere has been presented by Wang et al. (2015) and Kumar et al. (2015). The flux rope in our case can be observed in all of the AIA EUV passbands, but it is still in the lower solar atmosphere. Our observation is quite similar to the formation of a chromospheric flux rope as a result of magnetic reconnection between two sheared cool Hα loops (Wang et al. 2015) in the lower atmosphere, but sheds more light on the formation of the filament. This event may also be a distinct example revealing the evolution of the flux rope in the lower solar atmosphere.

2020MNRAS.494.4382S___2010_Instance_2

It has been thought that QPOs originate from the innermost part of an accretion disc, which is associated with strong gravity, so that we might detect general relativistic effects. Miller et al. (1998) proposed beat-frequency models and estimated the parameters of NSs using this model. Stella & Vietri (1999) developed the relativistic precession model. In the last 20 years, disc-oscillation and resonance models and wave models have been proposed (e.g. Osherovich & Titarchuk 1999; Abramowicz & Kluźniak 2001; Abramowicz et al. 2003; Zhang 2004; Li & Zhang 2005; Erkut, Psaltis & Alpar 2008; Shi & Li 2009, 2010; Shi 2011; Shi, Zhang & Li 2014, 2018; de Avellar et al. 2018). Shi & Li (2009, 2010) obtained the twin modes of MHD waves in LMXBs (including NS LMXBs and black hole LMXBs), which are considered as the sources of high-frequency QPOs. Shi, Zhang & Li (2014, 2018) also considered the waves produced by the two MHD oscillation modes at the magnetosphere radius as the origin of kHz QPOs. A relationship between the frequencies of the twin-peak kHz QPOs and the accretion rate, in which parallel tracks can be explained, was obtained (Shi, Zhang & Li 2018). Recently, many simulations on the oscillations of accreting tori in the accretion process of NS LMXBs (e.g. Kulkarni & Romanova 2013; Parthasarathy, Kluźniak, Čemeljić 2017) have been performed, and almost every model can reproduce some of the observed characteristics of QPOs. However, most models cannot fit the observed data perfectly, the observed data. Belloni, Méndez & Homan (2005) suggested that the twin kHz QPOs showed no intrinsically preferred frequency ratio, and this weakened support for the resonance models. Morsink & Stella (1999) were able to fit the overall NS data with different masses and spins of NSs using the relativistic precession model; however, Belloni, Méndez & Homan (2007) found that there were deviations between the expected and the observed trends. Recently, Török et al. (2016b, 2018) identified the observed QPO frequencies with the frequencies of the epicyclic modes of torus oscillations, and suggested that the relationship between the strong modulation of the X-ray flux and high values of QPO frequencies is connected to the orbital motion in the innermost part of an accretion disc. In addition, there are studies that compared a large set of models with the data of many sources in a complex manner (Lin et al. 2011; Török et al. 2012, 2016a).

2021ApJ...921..179L__Kuznetsov_&_Kolotkov_2021_Instance_1

Solar flares are powerful eruption events on the Sun associated with a rapid and violent release of magnetic free energy through a reconnection process. A typical flare can radiate at almost all wavelengths constituting the solar spectrum, ranging from radio through optical and ultraviolet (UV) to soft/hard X-ray (SXR/HXR) and even γ-rays (e.g., Benz 2017; Tan et al. 2020). Only a small part of the flare radiation is emitted at the shortest wavelengths in the X-ray and extreme-UV (EUV) ranges (Emslie et al. 2012). The quantitative estimation of the radiated flare energy partition suggested about 70% in white light (WL) for solar flares (e.g., Kretzschmar 2011) and 55%–80% in WL for stellar flares (e.g., Kuznetsov & Kolotkov 2021). In other words, most of the flare energy is radiated in the longer wavelengths (Kleint et al. 2016). Between those extremes, the solar UV spectrum from 1000 to 3000 Å, which can be further split into the far-ultraviolet (FUV), the mid-ultraviolet (MUV), and the near-ultraviolet (NUV), is thought to provide an important contribution to the flare radiation (Woods et al. 2006; Milligan et al. 2014; Dominique et al. 2018). For instance, the Lyα spectral line produced by the chromospheric neutral hydrogen, which is centered at 1216 Å (in the FUV spectrum), is among the spectral lines in which flares radiate the most (Allred et al. 2005; Curdt et al. 2001; Lu et al. 2021a). The hydrogen Balmer continuum emitted during flares, which is thought to be generated during the recombination of flare-produced free electrons in the chromosphere, is often detected in the MUV and NUV ranges, as well as close to the Balmer recombination edge at 3646 Å (Heinzel & Kleint 2014; Kotrč et al. 2016; Dominique et al. 2018). Both the Lyα and hydrogen Balmer continuum emissions during solar flares are expected to be nonthermal profiles, i.e., similar to the HXR radiation that is produced by the beam of electrons that are accelerated by the magnetic reconnection during the solar flare (e.g., Avrett et al. 1986; Rubio da Costa et al. 2009; Heinzel & Kleint 2014).

2021AandA...655A..25Z__García-Burillo_et_al._2014_Instance_2

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.

2020MNRAS.499.4666F__Popping_et_al._2017_Instance_1

An example of these implications is the so-called ‘dust budget crisis’ introduced in Section 4.4: the dust masses currently estimated at z > 5 are not compatible with standard dust production channels and require an overhaul in our models of the initial mass function for star formation, of supernova production rates, or of dust growth in the ISM. Overall, the dust production rate would need to increase by one to two orders of magnitudes, as shown by Rowlands et al. (2014). The growth of dust grains through accretion in the ISM has been proposed as a solution (e.g. Mancini et al. 2015; Michałowski 2015; Popping et al. 2017), but there are doubts on the efficiency of accretion at high z, where high dust temperatures due to the CMB (see Section 3.3) keep the desorption time-scale for accreted materials short (Ferrara et al. 2016). The dust budget crisis is not only a problem at high redshift; it is observed, e.g. in the Magellanic Clouds (SMC, LMC). As explained in Srinivasan et al. (2016) using the dust mass fits by Gordon et al. (2014), the dust replenishment time-scale in the SMC from stellar sources alone is expected to be larger than the dust destruction time-scale and, in the worst-case scenario, longer than the lifetime of the Universe. Similarly, the ratio between the best LMC dust mass estimate by Gordon et al. (2014) and the dust injection estimates by Riebel et al. (2012) results in an LMC replenishment time-scale of 34 ± 8 Gyr, exceeding the age of the Universe. Both the high redshift and the local Universe, therefore, show a dust budget crisis that could be alleviated – and, in the best case scenario, fully resolved – if the actual dust masses turned out to be lower than currently estimated, as our results suggest. More specifically, Rowlands et al. (2014) mention that dust opacity needs to be increased by just a factor of 7 to solve the high-redshift crisis (provided dust destruction by SNe is not efficient); in the LMC, the aforementioned replenishment time-scale would decrease to less than 2 Gyr if the dust mass were decreased by a factor of 20.

2018ApJ...853..148C__Shibuya_et_al._2014_Instance_1

LAE galaxies are defined by a high equivalent width (EW > 20 Å) Lyα line and are believed to be composed of extremely large regions of active star formation. Many efforts have been made to detect and characterize LAE galaxies (e.g., Conselice et al. 2003; Conselice 2004; Ravindranath et al. 2006; Shimasaku et al. 2006; Bournaud et al. 2007; Ouchi et al. 2008, 2017; Elmegreen et al. 2009a, 2009b; Tacconi et al. 2010; Gronwall et al. 2011; Kashikawa et al. 2011; Mandelker et al. 2014; Moody et al. 2014; Guo et al. 2015). In general, these galaxies appear as clusters of bright clumps, sometimes with a background of continuum emission. Evidence suggests that these clumps are larger and brighter than most star-forming regions in nearby low-redshift galaxies (Elmegreen et al. 2009a). Efforts have been made in quantifying mass, star formation rates, gas composition, and kinematics, as well as other LAE properties (e.g., Nilsson et al. 2009; Ono et al. 2010a, 2010b; Swinbank et al. 2010; Tacconi et al. 2010; Shibuya et al. 2014; Livermore et al. 2015; Nakajima et al. 2016; Hashimoto et al. 2017). These have revealed a wealth of information about the early universe, but they are ultimately limited by LAE surface brightnesses. Most studies rely upon stacks of galaxies and can draw only limited inferences about individual LAEs. Other studies show that LAE dust content, particularly clumpy dust, in the interstellar medium (ISM) can have an impact on most LAE observables (Kobayashi et al. 2007, 2010; Verhamme et al. 2008; Duval et al. 2014). Finkelstein et al. (2009) showed that clumpy dust models can provide a good fit to a set of z ∼ 4.5 LAEs, although they invoked a multiphase ISM that may be unlikely to form in nature (Laursen et al. 2013). Nevertheless, dust in LAE galaxy ISM could cause some of the irregularity in LAE surface-brightness profiles (Buck et al. 2017). With limited resolution, however, it is difficult to make this distinction. A further challenge to morphological studies is that the clump sizes are near the resolution limit of instrumental point spread functions (PSFs) and often cannot be distinguished from point sources (Guo et al. 2015). As a result, direct imaging studies cannot decisively determine whether the clumps are different in nature from star-forming regions in our local universe or if the larger apparent size is merely an artifact of insufficient resolution (Shibuya et al. 2014; Kobayashi et al. 2016; Tamburello et al. 2017; Fisher et al. 2017).

2018AandA...618A.128C__Croft_et_al._2005_Instance_1

As described in Sect. 1, the galaxy protocluster associated with 7C 1756+6520 is characterized by a high fraction of AGN protocluster members: seven AGN, including the central radio galaxy, have been spectroscopically confirmed in close proximity both spatially and in redshift space of the protocluster. This high AGN fraction detected so far, ~23%, makes the overdensity around 7C 1756+6520 similar to the interesting and well-studied cluster around the radio galaxy PKS 1138−262 from this point of view as well (Pentericci et al. 2002; Croft et al. 2005), in addition to the extension (see Sect. 5.2). The source PKS 1138−262 is a massive forming radio galaxy at z ~ 2.16 (Pentericci et al. 1998) that is surrounded by overdensities of Lyα emitters (Pentericci et al. 2000), extremely red objects (Kurk et al. 2004a; Koyama et al. 2013), Hα emitters (Kurk et al. 2004b), X-ray emitters (Pentericci et al. 2002), and an overdensity of dusty starbursts (Dannerbauer et al. 2014; Rigby et al. 2014), several of which are spectroscopically confirmed to be close to the radio galaxy redshift. Five of the 18 X-ray sources (~28%) detected by Pentericci et al. (2002) are AGN, including the central radio galaxy. From the soft X-ray luminosity function of AGN, Pentericci et al. (2002) estimated how many sources are expected in a given region of the cluster PKS 1138−262, finding that it contains about twice the number of expected AGN. More recently, Pentericci et al. (2013) also found high AGN fractions by studying eight galaxy groups from z ~ 0.5 to z ~ 1.1. They found that the fraction of AGN with Log LH > 42 erg s−1 in galaxies with MR −20 varies from less than 5% to 22%, with an average value of 6.3%, which is more than double the fraction for massive cluster at similar high redshifts (e.g., Overzier et al. 2005). Martini et al. (2013) estimated that the cluster AGN fraction in a sample of 13 clusters of galaxies at 1 z 1.5 is ~3% for AGN with rest-frame, hard X-ray luminosity greater than LX, H ≥ 1044 erg s−1. Based on these findings, the galaxy protocluster around 7C 1756+6520 seems to be a particularly interesting object.

2021MNRAS.500.2336Y__Lin_et_al._2020_Instance_2

Various surveys of SNRs in our Galaxy and nearby galaxies have been carried out at radio, X-ray, Infrared (IR), and optical wavelengths. The first extragalactic SNR candidates were identified in the LMC by Mathewson & Healey (1964) and later confirmed with a combination of radio and optical techniques by Westerlund & Mathewson (1966). To date, a total of 60 SNRs have been confirmed in the LMC with an additional 14 suggested candidates (Maggi et al. 2016; Bozzetto et al. 2017; Maitra et al. 2019). However, sensitivity and resolution limitations severely reduce the effectiveness of the past and present generations of radio and X-ray searches for SNRs in galaxies beyond the Small and Large Magellanic Clouds (MCs) (Goss et al. 1980; Long et al. 1981; Cowan & Branch 1985; Matonick et al. 1997; Matonick & Fesen 1997; Millar, White & Filipovic 2012; Galvin & Filipovic 2014; Sasaki et al. 2018; Lin et al. 2020; Sasaki 2020). As a result, optical studies have produced the largest number (∼1200) of new extra-Magellanic SNR candidates. Optical extragalactic searches for SNRs are mainly done by using an emission line ratio criterion of the form [S ii]/H α > 0.4–0.5 (Mathewson & Clarke 1973; Dodorico, Dopita & Benvenuti 1980; Fesen 1984; Blair & Long 1997; Matonick & Fesen 1997; Dopita et al. 2010b; Lee & Lee 2014; Vučetić et al. 2019b, a, 2018; Lin et al. 2020). This criterion separates shock-ionization from photoionization in SNRs from H ii regions and Planetary Nebulae (PNe) (Frew & Parker 2010). SNR radiative shocks collisionally excite sulphur ions in the extended recombination region resulting in S+, hence the larger contribution of [S ii] accounting for an increase of the [S ii] to H α ratio. In typical H ii regions, sulphur exists predominantly in the form of S++, yielding low [S ii] to H α emission ratios. Ratios from narrow-band imaging are usually verified spectroscopically, since [N ii] lines at 6548 and 6584 Å can contaminate the H α images at an unknown and variable level. Spectroscopic observations of such emission nebulae also can provide other evidence of shock heating, such as strong [O i] λ6300 emission, elevated [N ii] to H α with respect to H ii regions, or high [O iii] electron temperatures, verifying the candidate as being an SNR (Blair, Kirshner & Chevalier 1981, 1982; Long et al. 1990; Smith et al. 1993; Blair & Long 1997). Although somewhat biased as an isolated criterion, this method is proven and a good way of identifying ordinary radiatively cooling SNRs in nearby galaxies. We note that young, Balmer-dominated SNRs (Chevalier, Kirshner & Raymond 1980) would be missed by this criterion.

2022AandA...658A.188S__Kreckel_et_al._2019_Instance_1

We assumed a screen geometry and used PYNEB3 (Luridiana et al. 2015) to correct line fluxes for dust extinction via the Hα/Hβ ratio, adopting the O’Donnell (1994) reddening law with RV = 3.1 and a theoretical Hα/Hβ = 2.86. The extinction-corrected emission line luminosities of the H II regions were then computed using the distances reported in Table 1. For every H II region in our catalog, we also estimated the gas-phase metallicity and the gas ionization parameter by using extinction-corrected emission line fluxes. The gas-phase metallicity O/H was calculated using the Pilyugin & Grebel (2016) S calibration (Scal hereafter). This calibration relies on three diagnostic line ratios (i.e., [N II]/Hβ, [S II]/Hβ, and [O III]/Hβ) and provides an empirical calibration against H II regions that have direct constraints on their nebular temperatures and hence their abundances. This calibration is relatively insensitive to changes in gas pressure or ionization parameter, and we adopted it as fiducial approach in this paper (see Kreckel et al. 2019, for a discussion). In addition, for each galaxy we fit the radial metallicity gradient by using an unweighted least-square linear fitting of the trend between 12 + log(O/H) and the deprojected galactocentric radius (see Fig. A.19). The gas ionization parameter represents the ratio between the ionizing photon flux density and the gas hydrogen density. In this paper, we express the ionization parameter as q = U × c = Q(H0)/4πR2n, where c is the speed of light, U is the dimensionless ionization parameter, Q(H0) is the number of hydrogen ionizing photons (E >  13.6 eV) emitted per second, R is the empty (wind-blown) radius of the H II region, and n its hydrogen density. The ionization parameter is ultimately defined by the structure of an H II region (e.g., size, gas density, filling factor) and the properties of its ionizing source. Photoionization models show that it can be extracted via different diagnostic lines (Kewley & Dopita 2002; Dors et al. 2011). In this paper we use the calibration proposed by Diaz et al. (1991) based on the [S III](9069+9532)/[S II](6717+6713) line ratio. As the [S III]λ9532 Å line falls outside the wavelength range covered by MUSE, we assumed that [S III]λ9532 Å = 2.47[S III]λ9069 Å according to default atomic data in PYNEB (Luridiana et al. 2015). It should be noted that for about 3000 H II regions we are not able to estimate the ionization parameter due to lack of detection of the [S III]λ9532 Å emission line.

2016AandA...587A.159G__Tian_et_al._2014_Instance_2

One has to be sure to rule out cases where inorganic chemistry can mimic the presence of life (“false positives”). Potential abiotic ozone production on Venus- and Mars-like planets has been discussed by Schindler & Kasting (2000, and references therein). While this is based on photolysis of e.g., CO2 and H2O and is thus limited in extent, a sustainable production of abiotic O3 which could build up to a detectable level has been suggested by Domagal-Goldman & Meadows (2010) for a planet within the habitable zone of AD Leonis with a specific atmospheric composition. Indeed, other studies confirm that abiotic buildup of ozone is possible (e.g., Hu et al. 2012; Tian et al. 2014); however, detectable levels are unlikely if liquid water is abundant, as e.g. rainout of oxidized species would keep atmospheric O2 and O3 low (Segura et al. 2007), unless the CO2 concentration is high and both H2 and CH4 emissions are low (Hu et al. 2012). False-positive detection of molecules such as CH4 and O3 is discussed by von Paris et al. (2011). Seager et al. (2013) present a biosignature gas classification. Since abiotic processes cannot be ruled out for individual molecules (e.g. for O3), searches for biosignature molecules should search for multiple biosignature species simultaneously. It has been suggested that the simultaneous presence of O2 and CH4 can be used as an indication for life (Sagan et al. 1993, and references therein). Similarly, Selsis et al. (2002) suggest a so-called “triple signature”, where the combined detection of O3, CO2 and H2O would indicate biological activity. Domagal-Goldman & Meadows (2010) suggest to simultaneously search for the signature of O2, CH4, and C2H6. Of course, care has to be taken to avoid combinations of biosignature molecules which can be generated abiotically together (see e.g. Tian et al. 2014). The detectability of biosignature molecules is discussed, e.g. by von Paris et al. (2011) and Hedelt et al. (2013). In particular, the simulation of the instrumental response to simulated spectra for currently planned or proposed exoplanet characterization missions has shown that the amount of information the retrieval process can provide on the atmospheric composition may not be sufficient (von Paris et al. 2013).

2022ApJ...926...21B__Vida_et_al._2014_Instance_1

Characterizing the differential rotation (DR) realized at the base and in the convective envelope of solar-type stars is central to the understanding of their magnetic field generation, activity level, and rotation, as it is directly linked to the Ω effect (e.g., stretching of the poloidal magnetic field lines by large-scale shear). Hence, the role of DR in driving the star’s magnetic activity level and field properties should be clarified (Donahue et al. 1996). Doppler imaging (Donati & Collier Cameron 1997; Barnes et al. 2005), asteroseismology (Gizon & Solanki 2004; Reinhold et al. 2013; García et al. 2014), classical spot models (Lanza et al. 2014), and short-term Fourier transform (Vida et al. 2014) are methods to infer DR. The combination of all these observations on stellar rotation and magnetism helps constrain the trends linking rotation with stellar DR and magnetic activity. Various analyses of stellar DR revealed different dependencies between DR and star’s rotation (ΔΩ ∝ Ω n ), with n varying between 0.15 and 0.7 (Barnes et al. 2005; Reiners 2006; Reinhold et al. 2013). There is no clear consensus in the community for now; some authors are even advocating that such laws should be derived per spectral stellar classes and that the confusion comes from mixing together F and K stars (Balona & Abedigamba 2016). Saar (2011), Brandenburg & Giampapa (2018) also propose that the dependency of the DR with the rotation rate may not be monotonic, with a break near Rossby equals unity. By contrast, a more systematic and stronger dependency is observed with the star’s temperature ( ΔΩ∝Teff8.92 , Barnes et al. 2005, Reinhold et al. 2013; and ΔΩ∝Teff8.6 , Collier Cameron 2007). Hence, we expect large-scale shear to vary both in amplitude and profile (as a function of latitude and depth) as the global stellar parameters change. Some recent studies have confirmed this is happening in solar-type stars by inverting seismically their profile (Benomar et al. 2018), pointing to a possible antisolar DR state (e.g., slow equator/fast poles), which was possibly already guessed in F stars (Reiners 2007) and advocated to exist in numerical simulations (Matt et al. 2011; Gastine et al. 2014; Brun et al. 2015, see below).

2019ApJ...886...15T__Gruendl_&_Chu_2009_Instance_1

ALMA is capable of resolving internal structures of molecular clouds even in external galaxies. In particular, the Large Magellanic Cloud (LMC) is an ideal laboratory to investigate high-mass star formation thanks to its nearly face-on view (Balbinot et al. 2015) and the close distance, ∼50 kpc (Schaefer 2008; de Grijs et al. 2014). It is also a great advantage to directly compare the distributions of molecular gas observed by ALMA and positions of massive YSOs identified by Spitzer and Herschel (e.g., Gruendl & Chu 2009; Chen et al. 2010; Seale et al. 2014) without any serious contamination in the line of sight. Earlier studies using the H i gas observations by Fukui et al. (2017) found that there are supergiant shells (Kim et al. 1999, 2003) and kiloparsec-scale gas flows caused by the last tidal interaction between the LMC and the Small Magellanic Cloud (SMC). Therefore, we may be able to examine the relation between such large-scale gas kinematics and the local star formation activities. Our present target in this paper is the N159W-South clump, which was discovered by our previous ALMA Cycle 1 observations (Fukui et al. 2015, hereafter Paper I) with an angular resolution of ∼1″ (∼0.24 pc) toward a GMC in the N159W region (e.g., Johansson et al. 1998; Minamidani et al. 2008, 2011). Paper I revealed that the GMC is composed of many filamentary molecular clouds and discovered the first example of protostellar outflows in the external galaxies. Paper I also found that the protostellar source with a stellar mass of ∼37 M⊙ in the N159W-South clump is located toward an intersection of two filaments and suggested that the filament–filament collision triggered the protostar formation. Although the ALMA observations significantly improved our understanding of molecular cloud structures and star formation in this object, much higher-angular-resolution studies are needed to further resolve the filamentary structures down to a width of ≲0.1 pc (see, Arzoumanian et al. 2011, 2019) and investigate the star formation activities therein.

2019AandA...627A..53H__Sutherland_&_Bicknell_2007_Instance_1

A spatial coincidence of the radio jet morphology and velocity dispersion of the ionised gas has already been reported for spatially-resolved spectroscopy of more luminous radio-quiet AGN (e.g. Husemann et al. 2013; Villar-Martín et al. 2017) and powerful compact radio sources (e.g. Roche et al. 2016), but it has been correctly proposed that the fast moving plasma itself can lead to radio emission that mimics jet activity (Zakamska & Greene 2014; Hwang et al. 2018). In the case of HE 1353−1917 we can rule out that the ionised plasma is creating the radio emission because the high-velocity ionised gas traced by [O III] is significantly displaced compared to the observed jet-like radio emission. Hence, we think that the radio jet is transferring its energy and momentum to the ambient medium through an extended shock front, which creates turbulence in a dense clumpy ISM. Such a great impact of the radio jet has been observationally shown in many cases (e.g. Villar-Martín et al. 1999, 2014; O’Dea et al. 2002; Nesvadba et al. 2006; Holt et al. 2008; Guillard et al. 2012; Harrison et al. 2015; Santoro et al. 2018; Tremblay et al. 2018; Jarvis et al. 2019) and theoretically supported through detailed hydrodynamic simulations (e.g. Krause & Alexander 2007; Sutherland & Bicknell 2007; Wagner & Bicknell 2011; Wagner et al. 2012; Cielo et al. 2018; Mukherjee et al. 2018). As we discussed in Sect. 3.6, the jet power alone is sufficient to energetically drive the outflow because only a small fraction of the AGN luminosity would impact the thin disc of the galaxy implying conversion efficiencies of more than 10% of Lbol. Hopkins & Elvis (2010) proposed a two-stage process for efficient radiation-driven outflows. They describe a scenario in which an initial weak wind in the hot gas phase, possibly initiated by an accretion disc wind or a radio jet, creates additional turbulence in the surrounding medium so that massive gas clouds will subsequently expand and disperse. This expansion of gas clouds would significantly increase their apparent cross-section with respect to incident radiation field of the AGN. Such a two-stage process may increase the coupling efficiency by an order of magnitude. While we cannot directly confirm this process with our observations, the close alignment of the jet axis and the ionisation cone greatly suggest that the outflow is driven jointly by both mechanical and radiative energy with an unknown ratio of the two. The open question is whether the same powerful outflow could have developed without the fast radio jet impacting the cold gas directly given its unique orientation.

2018AandA...616A..11G__Quinn_et_al._1993_Instance_1

In addition to secular evolutionary processes, a disc galaxy like ours is expected to have experienced several accretion events in its recent and early past (Bullock & Johnston 2005; De Lucia & Helmi 2008; Stewart et al. 2008; Cooper et al. 2010; Font et al. 2011; Brook et al. 2012; Martig et al. 2012; Pillepich et al. 2015; Deason et al. 2016; Rodriguez-Gomez et al. 2016). While some of these accretions are currently being caught in the act, like for the Sagittarius dwarf galaxy (Ibata et al. 1994) and the Magellanic Clouds (Mathewson et al. 1974; Nidever et al. 2010; D’Onghia & Fox 2016), we need to find the vestiges of ancient accretion events to understand the evolution of our Galaxy and how its mass growth has proceeded over time. Events that took place in the far past are expected to have induced a thickening of the early Galactic disc, first by increasing the in-plane and vertical velocity dispersion of stars (Quinn et al. 1993; Walker et al. 1996; Villalobos & Helmi 2008, 2009; Zolotov et al. 2009; Purcell et al. 2010; Di Matteo et al. 2011; Qu et al. 2011; Font et al. 2011; McCarthy et al. 2012; Cooper et al. 2015; Welker et al. 2017), and second by agitating the gaseous disc from which new stars are born, generating early stellar populations with higher initial velocity dispersions than those currently being formed (Brook et al. 2004, 2007; Forbes et al. 2012; Bird et al. 2013; Stinson et al. 2013). These complementary modes of formation of the Galactic disc can be imprinted on kinematics-age and kinematics-abundance relations (Strömberg 1946; Spitzer & Schwarzschild 1951; Nordström et al. 2004; Seabroke & Gilmore 2007; Holmberg et al. 2007, 2009; Bovy et al. 2012a, 2016; Haywood et al. 2013; Sharma et al. 2014; Martig et al. 2016; Ness et al. 2016; Mackereth et al. 2017; Robin et al. 2017), and distinguishing between them requires full 3D kinematic information for several million stars, in order to be able to separate the contribution of accreted from in-situ populations, and to constrain impulsive signatures that are typical of accretions (Minchev et al. 2014) versus a more quiescent cooling of the Galactic disc over time. Accretion events that took place in the more recent past of our Galaxy can also generate ripples and rings in a galactic disc (Gómez et al. 2012b), as well as in the inner stellar halo (Jean-Baptiste et al. 2017). Such vertical perturbations of the disc are further complicated by the effect of spiral arms (D’Onghia et al. 2016; Monari et al. 2016b), which together with the effect of accretion events might explain vertical wave modes as observed in SEGUE andRAVE (Widrow et al. 2012; Williams et al. 2013; Carrillo et al. 2018), as well as in-plane velocity anisotropy (Siebert et al. 2012; Monari et al. 2016b). Mapping the kinematics out to several kiloparsec from the Sun is crucial for understanding whether signs of these recent and ongoing accretion events are visible in the Galactic disc, to ultimately understand to what extent the Galaxy can be represented as a system in dynamical equilibrium (Häfner et al. 2000; Dehnen & Binney 1998), at least in its inner regions, or to recover the nature of the perturber and the time of its accretion instead from the characteristics and strength of these ringing modes (Gómez et al. 2012b).

2017ApJ...834...20A__Temi_et_al._2007a_Instance_2

Lenticular galaxies seems to have a wider range of properties compared to ellipticals that resemble more the old definition of ETGs. However, even in ellipticals, large differences prevail. Recent observations of elliptical galaxies with Spitzer and Herschel (Temi et al. 2005, 2007a, 2007b, 2009; Smith et al. 2012; Agius et al. 2013; Mathews et al. 2013) have revealed that the far-infrared (FIR) luminosity LFIR from these galaxies can vary by ∼100 among galaxies with similar optical luminosity. The 70 μm band luminosities (from Temi et al. 2007a, 2009), is a good example of such a huge scatter in the FIR luminosity of elliptical galaxies. Some of the high L70 galaxies are members of a small subset of ellipticals having radio detections of neutral and molecular gas. A few others may be S0 galaxies which, because of their rotationally supported disks, often contain large masses of cold gas and dust. Ellipticals containing large excess masses of dust and cold gas probably result from significant galaxy mergers in the past. However a fraction of elliptical galaxies appear to be completely normal but L70 in these galaxies still ranges over a factor of ∼30, far larger than can be explained by uncertainties in the estimate of the FIR spectral energy distribution (SED) due to local stellar mass loss. While a significant fraction of the cold gas mass in low- to intermediate-mass ETGs is thought to have an external, merger-related origin (e.g., Davis et al. 2011), in the most massive ETGs the cold gas phases are presumably generated internally (Davis et al. 2011; David et al. 2014; Werner et al. 2014). Mergers with gas- and dust-rich galaxies have often been suggested for the origin of dust in all elliptical galaxies (e.g., Forbes 1991). Although the merger explanation is almost certainly correct in some cases, mergers cannot explain most of the observed scatter in L70. A crucial element in our understanding of the evolution of galaxies toward ETGs is the mutual role played by the major merging of galaxies and the secular star formation quenching. Neither of these scenarios yet accounts for all the observational evidence, and one could assume both contributing to some extent.

2021MNRAS.501.4148L__Buchhave_et_al._2012_Instance_1

We derived the photospheric stellar parameters using three different techniques: the curve-of-growth approach, spectral synthesis match, and empirical calibration. The first method minimizes the trend of iron abundances (obtained from the equivalent width, EW, of each line) with respect to excitation potential and reduced EW respectively, to obtain the effective temperature and the microturbulent velocity, ξt. The gravity log g is obtained by imposing the same average abundance from neutral and ionized iron lines. We obtained the EW measurements using ARESv2 5 (Sousa et al. 2015). We used the local thermodynamic equilibrium (LTE) code MOOG 6 (Sneden 1973) for the line analysis, together with the ATLAS9 grid of stellar model atmosphere from Castelli & Kurucz (2003). The whole procedure is described in more detail in Sousa (2014). We performed the analysis on a co-added spectrum (SNR > 600), and after applying the gravity correction from Mortier et al. (2014) and adding systematic errors in quadrature (Sousa et al. 2011), we obtained Teff = 5346 ± 69 K, log g =4.60 ± 0.12, [Fe/H] =−0.40 ± 0.05, and ξt =0.78 ± 0.08 km s−1. The spectral synthesis match was performed using the Stellar Parameters Classification tool (SPC, Buchhave et al. 2012, 2014). It determines effective temperature, surface gravity, metallicity, and line broadening by performing a cross-correlation of the observed spectra with a library of synthetic spectra, and interpolating the correlation peaks to determine the best-matching parameters. For technical reasons, we ran the SPC on the 62 GTO spectra only7: the SNR is so high that the spectra are anyway dominated by systematic errors, and including the A40TAC_23 spectra would not change the results. We averaged the values measured for each exposure, and we obtained Teff =5389 ± 50 K, log g =4.49 ± 0.10, [M/H] = −0.36 ± 0.08, and v sin i 2 km s−1. We finally used CCFpams,8 a method based on the empirical calibration of temperature, metallicity, and gravity on several CCFs obtained with subsets of stellar lines with different sensitivity to temperature (Malavolta et al. 2017b). We obtained Teff = 5293 ± 70 K, log g =4.50 ± 0.15, and [Fe/H] = −0.40 ± 0.05, after applying the same gravity and systematic corrections as for the EW analysis. We list the final spectroscopic adopted values, i. e. the weighted averages of the three methods, in Table 3. From the co-added HARPS-N spectrum, we also derived the chemical abundances for several refractory elements (Na, Mg, Si, Ca, Ti, Cr, Ni). We used the ARES + MOOG method assuming LTE, as described earlier. The reference for solar values was taken from Asplund et al. (2009), and all values in Table 3 are given relative to the Sun. Details on the method and line lists are described in Adibekyan et al. (2012) and Mortier et al. (2013). This analysis shows that this iron-poor star is alpha-enhanced. Using the average abundances of magnesium, silicon, and titanium to represent the alpha-elements and the iron abundance from the ARES + MOOG method (for consistency), we find that [α/Fe] = 0.23.

2022MNRAS.514.1961R__Prochaska_&_Zheng_2019_Instance_1

Along with the time-domain detections, we identified J173438.35-504550.4 as a potential host galaxy for FRB 20201123A using robust statistical treatment given the relatively small localization error region. At face value, the low redshift of J173438.35-504550.4 appears at odds with the large dispersion measure for FRB 20201123A (${\rm DM}_{\rm FRB}\approx 434 \, {\rm pc \, cm^{-3}}$). Our Galaxy, however, contributes ${\rm DM}_{\rm ISM}\approx 200 \, {\rm pc \, cm^{-3}}$ (NE2001 gives 229 ${\rm pc \, cm^{-3}}$ and YMW16 gives 162 ${\rm pc \, cm^{-3}}$) from its interstellar medium and a presumed ${\rm DM}_{\rm Halo}\sim 50 \, {\rm pc \, cm^{-3}}$ from its halo (Prochaska & Zheng 2019). This leaves ${\approx}180 \, {\rm pc \, cm^{-3}}$ for the cosmos (DMcosmic) and the host (DMhost). At z = 0.05, the average cosmic contribution is $\langle {\rm DM}_{\rm cosmic}\rangle \sim 42\, {\rm pc \, cm^{-3}}$ (Macquart et al. 2020) but the intrinsic scatter in this quantity is predicted to be large. Adopting the best-fitting model to the Macquart relation by Macquart et al. (2020), the 95 per cent confidence interval is ${\rm DM}_{\rm cosmic}= [15,125] \, {\rm pc \, cm^{-3}}$. Allowing for the maximum value of this interval (which would imply a significant foreground galaxy halo), we recover a minimum host contribution of ${\rm DM}_{\rm host, min} \approx 60~\rm pc~cm^{-3}$. This is consistent with estimates for host galaxy contributions from theoretical and empirical treatments (Prochaska & Zheng 2019; James et al. 2022). For a true DMcosmic value of this sightline closer to (or below) the mean, the host contribution would exceed $100 \, {\rm pc \, cm^{-3}}$. Such values are inferred for other FRB hosts (e.g. FRB 20121102A; Tendulkar et al. 2017). In conclusion, we find no strong evidence to rule out the association with J173438.35-504550.4 based on its redshift and DMFRB. The significant host contribution to the DM, combined with the scattering in FRB 20201123A possibly originating in the host, shows that it shares similarities with other highly active, repeating FRBs like FRB 20121102A and FRB 20190520A and potentially resides in a turbulent and dense environment within the host.

2017ApJ...850...20G__Vidaña_2016_Instance_1

The observation of massive neutron stars Demorest et al. (2010), Antoniadis et al. (2013) indicates that the EoS of nuclear matter must be very stiff in the regime of high densities and low temperatures. The degree of stiffness in the nuclear matter EoS is directly related to the repulsive interaction among particles at high densities, as well as to the particle content in the core of the stars. In particular, it has been extensively discussed in the literature whether exotic degrees of freedom might populate the core of neutron stars. On the one hand, it is more energetically favorable for the system to populate new degrees of freedom, such as hyperons (Dexheimer & Schramm 2008; Ishizuka et al. 2008; Bednarek et al. 2012; Fukukawa et al. 2015; Gomes et al. 2015; Maslov et al. 2015; Oertel et al. 2015; Lonardoni et al. 2015, 2016); Biswal et al. 2016; Burgio & Zappalà 2016; Chatterjee & Vidana 2016; Mishra et al. 2016; Vidaña 2016; Yamamoto et al. 2016; Tolos et al. 2017); Torres et al. 2017), delta isobars (Fong et al. 2010;Schurhoff et al. 2010; Drago et al. 2014; 2016; Cai et al. 2015; Zhu et al. 2016), and meson condensates (Ellis et al. 1995; Menezes et al. 2005; Takahashi 2007; Ohnishi et al. 2009; Alford et al. 2010; Fernandez et al. 2010; Mesquita et al. 2010; Mishra et al. 2010; Lim et al. 2014; Muto et al. 2015), in order to lower its Fermi energy (starting at about two times the saturation density). On the other hand, the EoS softening due to the appearance of exotica might turn some nuclear models incompatible with observational data, in particular with the recently measured massive neutron stars. One possible way to overcome this puzzle is the introduction of an extra repulsion in the YY interaction Schaffner & Mishustin (1996), Bombaci (2016), allowing models with hyperons to be able to reproduce massive stars (Dexheimer & Schramm 2008; Bednarek et al. 2012; Weissenborn et al. 2012; Banik et al. 2014; Bhowmick et al. 2014; Gusakov et al. 2014; Lopes & Menezes 2014; van Dalen et al. 2014; Yamamoto et al. 2014; Gomes et al. 2015). Another possible solution is the introduction of a deconfinement phase transition at high densities Bombaci (2016), with a stiff EoS for quark matter, usually associated with quark vector interactions (see Klähn et al. 2013 and references therein).

2015ApJ...804..130C__Bertschinger_1985_Instance_1

We have rigorously developed the embedded gravitational lensing theory for point mass lenses in a series of recent papers (Chen et al. 2010, 2011, 2015; Kantowski et al. 2010, 2012, 2013) including the embedded lens equation, time delays, lensing magnifications, shears, etc. We successfully extended the lowest-order embedded point mass lens theory to arbitrary spherically symmetric distributed lenses in Kantowski et al. (2013). The gravitational correctness of the theory follows from its origin in Einstein’s gravity. The embedded lens theory is based on the Swiss cheese cosmologies (Einstein & Straus 1945; Schücking 1954; Kantowski 1969). The idea of embedding (or Swiss cheese) is to remove a co-moving sphere of homogeneous dust from the background Friedmann–Lemaître–Robertson–Walker (FLRW) cosmology and replace it with the gravity field of a spherical inhomogeneity, maintaining the Einstein equations. In a Swiss cheese cosmology the total mass of the inhomogeneity (up to a small curvature factor) is the same as that of the removed homogeneous dust sphere. For a galaxy cluster, embedding requires the overdense cluster to be surrounded by large underdense regions often modeled as vacuum. For a cosmic void, embedding requires the underdense interior to be “compensated” by an overdense bounding ridge, i.e., a compensated void (Sato & Maeda 1983; Bertschinger 1985; Thompson & Vishniac 1987; Martínez-González et al. 1990; Amendola et al. 1999; Lavaux & Wandelt 2012). A low-density region without a compensating overdense boundary, or with an overdense boundary not containing enough mass to compensate the interior mass deficit, has a negative net mass (with respect to the homogeneous background) and is known as an “uncompensated” or “undercompensated” void (Fillmore & Goldreich 1984; Bertschinger 1985; Sheth & van de Weygaert 2004; Das & Spergel 2009).3 3 This dichotomy between compensated and uncompensated voids is slightly different from one based on the classification of the small initial perturbations from which voids are thought to be formed. The initial perturbation can be compensated or uncompensated, which leads to different void growth scenarios (Bertschinger 1985), but if the evolved void formed from either perturbation is surrounded by an overdense shell that “largely” compensates the underdense region (i.e., the majority of the void mass is swept into the boundary shell in the snowplowing fashion when the void is growing), we still call it compensated because the small mass deficit originating in the initial perturbation is unimportant for gravitational lensing. Similarly, an overcompensated void has positive net mass with respect to the homogeneous FLRW background. Numerical or theoretical models of over-or undercompensated voids do commonly exist (e.g., Sheth & van de Weygaert 2004; Cai et al. 2010, 2014; Ceccarelli et al. 2013; Hamaus et al. 2014). We focus on compensated void models in this paper, given that uncompensated void models do not satisfy Einstein’s equations. The critical difference between an embedded lens and a traditional lens lies in the fact that embedding effectively reduces the gravitational potential’s range, i.e., partially shields the lensing potential because the lens mass is made a contributor to the mean mass density of the universe and not simply superimposed upon it. At lowest order, this implies that the repulsive bending caused by the removed homogeneous dust sphere must be accounted for when computing the bending angle caused by the lens mass inhomogeneity and legitimizes the prior practice of treating negative density perturbations as repulsive and positive perturbations as attractive. In this paper we investigate the gravitational lensing of cosmic voids using the lowest-order embedded lens theory (Kantowski et al. 2013). We introduce the embedded lens theory in Section 2, build the simplest possible lens model for a void in Section 3, and study the lensing of the CMB by individual cosmic voids in Section 4. Steps we outline can be followed for many void models of current interest.

2019MNRAS.488.2825F__Hamers_et_al._2015_Instance_1

On the other hand, the distribution of the orbital inclination of the third companion with respect to the inner binary in the triple i3 (bottom panel) is found to peak at ∼100°, but with non-negligible tails. For comparison, isolated triples that merge due to the KL mechanism show a very pronounced peak at ∼90°, with only a few mergers happening in low-inclination systems (Antonini et al. 2017). There are two possible caveats to this. First, possible resonances between nodal precession and KL oscillations can arise. As shown in Hamers & Lai (2017), this could make even low-inclination systems merge. Secondly, there are three different KL mechanisms competing, thus the eccentricity of the inner binary of the CO triple can be pumped up by the torque either of the SMBH or the third companion in the CO triple, for which the KL time-scale is shorter (e.g. Hamers et al. 2015). We define the following ratios (18) \begin{eqnarray*} \mathcal {R}_{12M}=\frac{T_{KL}^{123}}{T_{KL}^{12M}} \end{eqnarray*} (19) \begin{eqnarray*} \mathcal {R}_{123M}=\frac{T_{KL}^{123}}{T_{KL}^{123M}} \end{eqnarray*} (20) \begin{eqnarray*} \mathcal {R}_{M}=\frac{T_{KL}^{123M}}{T_{KL}^{12M}}\ , \end{eqnarray*} which we plot in Fig. 4 for the BH–BH binaries that merge in our simulations in Models MW, α = 2, a3, max = 50 au and different values of β. Clearly, the shape of the three distributions does not depend on the assumed value of β, and we find that $\mathcal {R}_{12M}$, $\mathcal {R}_{123M}$, and $\mathcal {R}_{M}$ peak at ∼10−4, ∼5 × 10−3, and ∼10−1, respectively. We note that, however, KL cycles in a given CO triple may be inactive if the relative inclinations are not in the KL window. As a consequence, the initial dynamical evolution of the CO triple can ultimately be dictated by a KL cycle that takes place on a long time-scale. The latter can in turn excite the relative inclination of one of the other orbits, which could activate KL cycles that take place on a shorter time-scale. Thus, the CO triple may experience rather different and rich dynamical paths, which tend to lead to chaotic behaviour (Grishin et al. 2018).

2019AandA...629A..54U__Evans_et_al._2007_Instance_2

NGC 2110. NGC 2110 is another nearby (z = 0.00779, Gallimore et al. 1999), X-ray bright Seyfert galaxy. Diniz et al. (2015) report a black hole mass of 2 . 7 − 2.1 + 3.5 × 10 8 M ⊙ $ 2.7^{+ 3.5}_{- 2.1} \times 10^{8}\,{{M}_{\odot}} $ , from the relation with the stellar velocity dispersion. From BeppoSAX data, Malaguti et al. (1999) found the X-ray spectrum to be affected by complex absorption. This has been later confirmed by Evans et al. (2007), who find the Chandra+XMM–Newton data to be well fitted with a neutral, three-zone, partial-covering absorber. Rivers et al. (2014) find the Suzaku data to be well fitted with a stable full-covering absorber plus a variable partial-covering absorber. A soft excess below 1.5 keV is also present (Evans et al. 2007), and possibly due to extended circumnuclear emission seen with Chandra (Evans et al. 2006). No Compton reflection hump has been detected with Suzaku (Rivers et al. 2014) or NuSTAR (Marinucci et al. 2015), despite the presence of a complex Fe Kα line. According to the multi-epoch analysis of Marinucci et al. (2015), the Fe Kα line is likely the sum of a constant component (from distant, Compton-thick material) and a variable one (from Compton-thin material). Concerning the high-energy cut-off, ambiguous results have been reported in literature (see Table 1). Ricci et al. (2017) report a value of 448 − 55 + 63 $ 448^{+63}_{-55} $ keV, while Lubiński et al. (2016) report a coronal temperature of 230 − 57 + 51 $ 230^{+51}_{-57} $ keV and an optical depth of 0 . 52 − 0.13 + 0.14 $ 0.52^{+ 0.14}_{- 0.13} $ . From 2008–2009 INTEGRAL data, Beckmann & Do Cao (2010) report a cut-off of ∼80 keV with a hard photon index, but these results are not confirmed by NuSTAR (Marinucci et al. 2015). Indeed, only lower limits to the high-energy cut-off have been found with NuSTAR (210 keV: Marinucci et al. 2015), Suzaku (250 keV: Rivers et al. 2014) and BeppoSAX (143 keV: Risaliti 2002). No hard X-ray spectral variability has been detected by Caballero-Garcia et al. (2012) and Soldi et al. (2014) from BAT data, despite the significant flux variability.

2018ApJ...860...24P__Warmuth_2015_Instance_3

Figure 13 shows the temporal evolution of the density, ρ, plasma flow velocity, vx, position of the wave crest, PosA, phase speed, vw, and magnetic field component in the z-direction, Bz, for the primary waves in every different case of initial amplitude, ρIA. In Figure 13(a), we observe that the amplitude of the density remains approximately constant at their initial values until the time when the shock is formed and the density amplitude of the primary wave starts decreasing (see Vršnak & Lulić 2000), i.e., at t ≈ 0.03 (blue), t ≈ 0.04 (red), and t ≈ 0.055 (green). For the case of ρIA = 1.3 (magenta), a decrease of the amplitude of the primary can hardly be observed, as expected for low-amplitude wave (Warmuth 2015). One can see that the larger the initial amplitude, ρIA, the stronger the decrease of the primary wave’s amplitude, which is consistent with observations (Warmuth & Mann 2011; Muhr et al. 2014; Warmuth 2015). The amplitudes decrease to values of ρ ≈ 1.6 (blue), ρ ≈ 1.5 (red), and ρ ≈ 1.4 (green) until the primary wave starts entering the CH. Due to the fact that the waves with larger initial amplitude enter the CH earlier than those with small initial amplitude, we can see in Figure 13(a) that the tracking of the parameters of the faster waves stops at an earlier time than the one for the slower waves. A similar behavior to the one of the density, ρ, can be observed for the plasma flow velocity, vx, in Figure 13(b) and the magnetic field component, Bz, in Figure 13(e). Here, the amplitudes decrease from vx = 0.75, Bz = 1.9 (for ρIA = 1.9, blue), vx = 0.6, Bz = 1.7 (for ρIA = 1.7, red), vx = 0.45, Bz = 1.5 (for ρIA = 1.5, green), and vx = 0.27, Bz = 1.3 (for ρIA = 1.3, magenta) to vx = 0.55, Bz = 1.6 (for ρIA = 1.9, blue), vx = 0.46, Bz = 1.5 (for ρIA = 1.7, red), vx = 0.36, Bz = 1.4 (for ρIA = 1.5, green), and vx = 0.25, Bz = 1.25 (for ρIA = 1.3, magenta). Figure 13(c) shows how the primary waves propagate in the positive x-direction. In all four cases of different initial amplitude, ρIA, the phase speed decreases slighty (consistent with observations; see Warmuth et al. 2004 and Warmuth 2015) until the waves enter the CH at different times, i.e., the values for the phase speed start at vw ≈ 2.2 (for ρIA = 1.9, blue), vw ≈ 1.9 (for ρIA = 1.7, red), vw ≈ 1.7 (for ρIA = 1.5, green), and vw ≈ 1.4 (for ρIA = 1.3, magenta) and decrease to vw ≈ 1.5 (for ρIA = 1.9, blue), vw ≈ 1.39 (for ρIA = 1.7, red), vw ≈ 1.2 (for ρIA = 1.5, green), and vw ≈ 1.13 (for ρIA = 1.3, magenta).

2015AandA...580L...2Z__Fender_et_al._2000_Instance_1

Synchrotron radiation emitted from one relativistic electron population with density Nrel gyrating along a magnetic field B produces a power-law radio spectrum (S ∝ να) with spectral slope αthin 0 above a critical frequency νbreak that depends on B and Nrel. Below this critical frequency self-absorption effects become important and the emission becomes optically thick with a spectral slope αthick = 2.5 (e.g. Kaiser 2006). For instance, the spectrum of the radio outburst in the pulsar system PSR B1259−63 has a spectral index α = −0.7, which is consistent with optically thin synchrotron emission (Fig. 3 in Connors et al. 2002). In contrast, the jets of microquasars often show flat/inverted radio spectra as demonstrated by Fender (2001). Plasma and magnetic field variations along the jet in fact may create regions with different B and Nrel values resulting in spectral components with different turn-over frequencies νbreak. The overlap of the optically thin part of one spectral component with the self-absorbed part of the adjacent one will result in a flat spectrum when observed with a spatial resolution insufficient to resolve the jet (Kaiser 2006). In the microquasar Cygnus X-1, radio emission has been measured with a flat spectrum, i.e. α = −0.06 ± 0.05 and α = 0.07 ± 0.04 (Fender et al. 2000). In Cygnus X-1 the black hole is powered by accretion of the stellar wind of its supergiant companion star. Since the companion is close to filling its Roche lobe, the wind is not symmetric but strongly focused towards the black hole (Miškovičová et al. 2011, and references therein). In contrast, as discussed above in the context of an accretion scenario, accretion in LS I +61°303 occurs in two particular orbital phases due to the eccentric orbit around the Be star. Is the accretion phase and subsequent ejection in LS I +61°303 able to develop a microquasar jet with a flat spectrum? As shown by Corbel et al. (2013), in the early phase of jet formation the low density particles in the jet produce an optically thin synchrotron power-law spectrum at GHz frequencies. Only when the density of the jet plasma increases, a transition to higher optical depth occurs resulting in flat/inverted radio spectra (α ≥ 0). In LS I +61°303 strong evidence for a flat radio spectrum does exist. Early VLA observations at four epochs revealed a flat spectrum between 1.5 and 22 GHz (Gregory et al. 1979). Furthermore, Strickman et al. (1998) carried out multi-frequency VLA observations sparsely covering one orbit and found deviations in the radio spectrum from a simple power law during the outburst. Finally, Massi & Kaufman Bernadó (2009) measured even inverted, optically thick spectra between 2.2 GHz and 8.3 GHz. The primary aim of the current work is to extend previous radio observations of LS I +61°303 and systematically study – for the first time – broad-band cm/mm (2.6–32 GHz) radio spectra and their evolution during a complete outburst. The densely sampled observations of LS I +61°303 were performed over a period of about three weeks (a total of 24 days) using the Effelsberg 100 m telescope at a total of seven frequency bands. The paper is structured as follows. In Sect. 2 we briefly present the observations and data reduction procedures. In Sect. 3 we present the results. Section 4 provides a short discussion and our conclusions.

2016AandA...591A..30L__Fall_et_al._(2010)_Instance_1

The catalog reported by Lada & Lada (2003) for embedded clusters is consistent with the mass-size relation R ~ M1/3 − M1/2 for low-mass clusters as pointed out by Murray (2009), where R and M are the gas radius and mass, respectively. While the stellar mass of an embedded cluster is not directly observable and is obtained by assuming an underlying universal initial mass function (IMF), Adams et al. (2006) compiled data from Lada & Lada (2003) and Carpenter (2000) and showed a number-size relation \hbox{$R_* \sim N_*^{1/2}$}R∗~N∗1/2 between the radius of the cluster and the number of objects it contains. The results of Gutermuth et al. (2009) are also compatible with this relation. The number-size relation is reasonably equivalent to the mass-size relation if we adopt a universal IMF and thus similar averaged mass among clusters. Despite the slight uncertainty in the power-law exponent, varying from 1/3 to 1/2, and the scatter of the data, it is clear that embedded clusters follow some mass-size relation. Larger data sets of star-forming clumps, identified as gaseous protoclusters, also exhibit a mass-size relation. Fall et al. (2010) compiled the observations of Shirley et al. (2003), Faúndez et al. (2004), and Fontani et al. (2005) in their Fig. 1, and found via least-squares regression the relation R ∝ M0.38. A regression fit on the ATLASGAL survey (Urquhart et al. 2014) data gives a dependence of R ∝ M0.50. In their work, they fitted log (M) to log (R) and found M ∝ R1.67, of which the power-law exponent is not the inverse of what we inferred. This indicates that the clump properties are differently dispersed in size and mass, and thus there exist some uncertainties in the power-law dependence. Meanwhile, both studies exhibit mass scatter of about 1 dex and are compatible with constant gas surface density, that is, R ∝ M0.5. These observations are performed with molecular lines and dust continuum of the star-forming gas, suggesting that the mass-size relation and probably some other properties of the stellar cluster are established as early as the gas-dominated phase. Pfalzner et al. (2016) pointed out that the mass-size relation for embedded clusters and gaseous protoclusters follow the same trend with different absolute value, which could be explained with star formation efficiency or cluster expansion. One interesting question to ask would be what physical processes actually determine this mass-size relation. Its existence suggests that this primary phase of cluster formation, the gaseous protocluster, is very likely in some equilibrium state governed by the molecular cloud environment in which it resides, and is crucial for understanding the nature of the cluster and, more generally, star formation. An analytical study by Hennebelle (2012) yielded, by linking the gaseous protocluster to properties of the parent cloud, R ~ M1/3 or R ~ M1/2 for protoclusters with different accretion schemes. We stress that we are emphasizing a global equilibrium, which does not imply that the structure is not locally collapsing. More precisely, we propose that the large scales are supported by a combination of rotation and turbulence that sets the M-R relation.

2021MNRAS.500..291B__Longinotti_et_al._2015_Instance_1

We have presented the analysis of the current X-ray observations of the disc wind in MCG-03-58-007. Here, multiple and variable wind components with velocities ranging from $\sim \! -0.08\, c$ to $\sim \! - 0.2\, c$ (and potentially up to $0.35\, c$) are seen at different times. Multi-epoch observations of disc winds, like the one presented here, are crucial for revealing all the possible phases of the disc wind. For example, over a decade worth of observations of PDS 456 revealed that the wind is most likely clumpy and/or stratified with the ionization ranging from log ($\xi /\rm {erg\, cm \, s^{-1})}\sim 2$ erg cm s−1 up to log ($\xi /\rm {erg\, cm \, s^{-1})}=6$ erg cm s−1 and velocities ranging from $\sim \! -0.2\, c$ up to $\sim \! -0.46\, c$ (Reeves et al. 2016, 2018a, 2020). It is possible, as suggested in other examples of ultra fast disc winds, that we are looking at a stratified wind, where multiple components are launched at different disc radii, but not all of them are always detected. This adds MCG-03-58-007 to the small but growing list of multiphase fast X-ray winds. Other examples of AGN with at least two variable phases of the X-ray winds are PG 1211+143 (Pounds et al. 2016; Reeves et al. 2018b), IRAS 13224-3809 (Parker et al. 2017; Chartas & Canas 2018; Pinto et al. 2018), 1H 0707-495 (Kosec et al. 2018), IRAS 17020+4544 (Longinotti et al. 2015), and PG 1114+445 (Serafinelli et al. 2019). In those cases, multiple phases with a common or different outflowing velocities are detected in the X-ray band. In contrast to most of the cases reported so far, neither of the two phases seen in MCG-03-58-007 requires a different ionization (aside from slice B) suggesting that we are seeing different streamlines of the same highly ionized flow. The only exception could be the eclipsing event seen in 2015, where a solution is found with a lower ionization for the Fe K intervening absorber. However, what we most likely see during this occultation event is a higher density and lower ionization clump of the wind, which could be faster because its higher opacity makes it easier to accelerate (Waters et al. 2017). Note that this does not imply that the soft X-ray wind components, like the ones seen for example in PDS 456 or PG 1211+143, are not present; in contrast to the other examples, MCG-03-58-007 is seen through a relatively high column density (NH ∼ 2 × 1023 cm−2, see Table 2) neutral absorber, therefore these phases may be hidden behind it. MCG-03-58-007 is not the only example where multiple Fe-K zones with the same ionization and outflowing with different velocities had been detected in a single observation. For instance, two simultaneous Fe-K phases were detected at least twice in PDS456 (Reeves et al. 2018a, 2020) and possibly in PG 1211+143 (Pounds et al. 2016) and IRAS 13349+2438 (Parker et al. 2020).

2016ApJ...827...31F__Das_&_Chakrabarti_2007_Instance_1

Accretion physics has been extensively studied for decades, particularly in terms of the theoretical aspects including semi-analytic investigations as well as global numerical simulations, in an effort to further understand its physical nature and observational consequences. Many of the works on BH accretion have, in general, revealed an important generic feature of accretion, i.e., the formation of shocks as an accreting plasma is subject to outward forces via a number of decelerating mechanisms (e.g., Abramowicz & Prasanna 1990) and develops a shock front at r = rsh within the radius of the inner edge of a magnetized accretion disk7 7 Armitage et al. (2001) found an ISCO-like edge in their pseudo-Newtonian MHD accretion simulations. , perhaps equivalent to an innermost stable circular orbit (ISCO) for a pure HD Keplerian disk, before crossing an event horizon at r = rH. Previous studies include hydrodynamic shocks (e.g., Nobuta & Hanawa 1994; Lu et al. 1997; Chakrabarti 1990; Fukumura & Tsuruta 2004) and magnetohydrodynamic (MHD) shocks (e.g., Koide et al. 1998, 2000; Das & Chakrabarti 2007; Takahashi et al. 2002, 2006, hereafter T02, T06, respectively; Fukumura & Kazanas 2007b; Fukumura et al. 2007, hereafter F07; Takahashi & Takahashi 2010). In particular, extensive theoretical studies of various types of shocks have been conducted to date in an attempt to understand their dynamical behavior; e.g., shock oscillation in the context of quasi-periodic oscillations and its spectroscopic signatures (e.g., Chakrabarti & Titarchuk 1995; Molteni et al. 1996, 1999; Acharya et al. 2002; Okuda et al. 2004, 2007; Nagakura & Yamada 2008) that may be relevant for X-ray Binaries (XRBs), for example. Independent general relativistic (GR) MHD (GRMHD) simulations of the tilted accretion disk clearly show that the compression of the plunging plasma in the inner region( ) leads to the formation of standing shocks (e.g., Fragile et al. 2007; Fragile & Blaes 2008; Generozov et al. 2014) depending on the characteristics of the disk geometry and the BH spin (e.g., Morales et al. 2014). The expected highly magnetized shocked region may perhaps correspond to the magnetically arrested plasma seen in other large-scale simulations (e.g., Tchekhovskoy et al. 2011).

2016ApJ...817...12P__Sur_et_al._2007_Instance_2

Large-scale magnetic fields with strength of the order of 1–10 μG have been observed in disk galaxies (e.g., Beck et al. 1996; Fletcher 2010; Beck 2012; Beck & Wielebinski 2013; Van Eck et al. 2015). The origin of these fields can be explained through mean-field dynamo theory (Ruzmaikin et al. 1988; Beck et al. 1996; Brandenburg & Subramanian 2005a; Kulsrud & Zweibel 2008). The conservation of magnetic helicity is one of the key constraints in these models, and also leads to the suppression of the α-effect. The operation of the mean-field dynamo automatically leads to the growth of magnetic helicity of opposite signs between the large-scale and small-scale magnetic fields (Pouquet et al. 1976; Gruzinov & Diamond 1994; Blackman & Field 2002). To avoid catastrophic suppression of the dynamo action (α-quenching), the magnetic helicity due to the small-scale magnetic field should be removed from the system (Blackman & Field 2000, 2001; Kleeorin et al. 2000). Mechanisms suggested to produce these small-scale magnetic helicity fluxes are: advection of magnetic fields by an outflow from the disk through the galactic fountain or wind (Shukurov et al. 2006; Sur et al. 2007; Chamandy et al. 2014), magnetic helicity flux from anisotropy of the turbulence produced by differential rotation (Vishniac & Cho 2001; Subramanian & Brandenburg 2004, 2006; Sur et al. 2007; Vishniac & Shapovalov 2014), and through diffusive flux (Kleeorin et al. 2000, 2002; Brandenburg et al. 2009; Mitra et al. 2010; Chamandy et al. 2014). The outflow of magnetic helicity from the disk through dynamo operation leads to the formation of a corona (Blackman & Field 2000). According to Taylor's hypothesis, an infinitely conducting corona would resistively relax to force-free field configurations under the constraint of global magnetic helicity conservation (Woltjer 1960; Taylor 1974; Finn & Antonsen 1983; Berger & Field 1984; Mangalam & Krishan 2000). In this paper, we include advective and diffusive fluxes in a simple semi-analytic model of a galactic dynamo that transfers magnetic helicity outside the disk and consequently builds up a force-free corona in course of time. We first solve the time-dependent dynamo equations by expressing them as separable in variables r and z. The radial part of the dynamo equation is solved using an eigenvector expansion constructed using the steady-state solutions of the dynamo equation. The eigenvalues of the z part of the solution are obtained by solving a fourth-order algebraic equation, which primarily depends upon the turbulence parameters and the magnetic helicity fluxes. Once the dynamo solutions are written out as parametric functions of these parameters, the evolution of the mean magnetic field is computed numerically by simultaneously solving the dynamical equations for α-quenching and the growth of large-scale coronal magnetic helicity. Since the large-scale magnetic field lines cross the boundary between the galactic disk and the corona, the magnetic helicity of the large-scale magnetic field in the disk volume is not well defined. Hence we use the concept of gauge-invariant relative helicity (Finn & Antonsen 1983; Berger & Field 1984; Berger 1985) to estimate the large-scale magnetic helicity in the disk and the corona. Here the gauge-invariant relative helicity for the cylindrical geometry is calculated using the prescription given in Low (2006, 2011). We then investigate the dependence of the saturated mean magnetic field strength and its geometry on the magnetic helicity fluxes within the disk and the corresponding evolution of the force-free field in the corona.

2022AandA...663A.105P__Brunetti_et_al._2008_Instance_1

Regardless of the cluster orientation, the spectral index observed for the halo at all available frequencies suggests that it is a USSRH. Despite the number of detected USSRH is still low, radio halos with steep indices are being discovered more and more frequently in the last years thanks to the improved observational capabilities of low-frequency instruments such as GMRT, MWA (Murchison Widefield Array) and LOFAR (Shimwell et al. 2016; Wilber et al. 2018; Bruno et al. 2021; Di Gennaro et al. 2021; Duchesne et al. 2022). An in-depth analysis of all radio halos hosted in Planck clusters and observed in LoTSS, including A1550, has recently been presented in Botteon et al. (2022). USSRH are a prediction of turbulent re-acceleration models (Cassano et al. 2006; Brunetti et al. 2008), in which particles are re-accelerated by turbulence (Brunetti et al. 2001, 2017; Petrosian 2001; Brunetti & Lazarian 2011). On the other hand, the detection of such steep indices is not expected from hadronic (or secondary) models, in which the emission of halos comes from the production of secondary electrons from hadronic collisions between thermal and CR protons (Blasi & Colafrancesco 1999; Dolag & Enßlin 2000; Pfrommer et al. 2008). Given that the integrated spectral index observed for the USSRH with LOFAR is α 54 MHz 144 MHz ∼ − 1.6 $ \alpha_{54\,\rm MHz}^{144\,\rm MHz} \sim -1.6 $ , we expect an index for the spectral energy distribution8δ = 2α − 1 = −4.2. If there is no break in the spectrum, the energy budget for these particles would be untenable (Brunetti et al. 2008). Therefore, a break at low energies (∼GeV) should exist, suggesting a possible interplay between radiative losses and turbulent re-acceleration during the lifetime of emitting electrons (Brunetti & Jones 2014). Moreover, re-acceleration models predict that a large fraction of halos associated with clusters of masses between 4 and 7 × 1014 M⊙ should exhibit steep spectra (Cassano et al. 2010, 2012; Brunetti & Jones 2014; Cuciti et al. 2021). The mass of A1550 of ∼6 × 1014 M⊙ estimated from Planck Collaboration XXVII (2016) falls in this range9.

2017MNRAS.470.2517H__D'Angelo_&_Lubow_2010_Instance_1

We carry out analysis of the orbital properties of our clumps only using the sample as detected by the DDS, as this method is sensitive to most clump masses and semimajor axes. The total semimajor axis evolution of all clumps is shown in the left-hand panel of Fig. 3, which we have already discussed, and refer the reader back to. Circles mark surviving clumps (including clumps that subsume another clump), squares mark destroyed clumps and triangles mark merged clumps. Larger markers correspond to more massive clumps. For destroyed clumps, we take the last measured mass. Roughly half of our most massive clumps migrate radially inwards, which is consistent with migration in locally isothermal discs, as objects exchange angular momentum with the surrounding gas and move inwards. However, about half of our most massive clumps migrate radially outwards. This is known to be possible in radiative discs (Kley & Nelson 2012), but requires either large torques or steep surface density gradients (D'Angelo & Lubow 2010). Large torques can have many sources, but in massive, self-gravitating discs they are likely to be in the form of global spiral arms. We carried out a Fourier analysis on the density structure of our discs, to determine the Fourier amplitude of each m mode (where m is the number of spiral arms). The amplitude, Am, of each mode, m, is calculated by (4) \begin{equation} A_{\mathrm{m}} = \Bigg | \sum _{i=1}^{N_{\mathrm{region}}} \frac{{\rm e}^{-im\phi _i}}{N_{\mathrm{region}}} \Bigg |, \end{equation} where Nregion is the number of particles in the region we are considering (for our case, R = 20 to R = 100 au), and ϕi is the azimuthal angle of the ith particle. Some example amplitudes are shown in Fig. 9, which shows the first 10 Fourier components of the density structure of two discs in their initial state (i.e. when they have just begun to fragment), marked in red, and the same two discs in their final state, marked in black. The discs are from simulation 1 and simulation 5, and their final state can be seen in their column density plots, shown in Fig. 1. These discs were selected because they ran for the same length of time, and they have contrasting final m-modes states, simulation 1 ultimately peaks in the m = 2 mode and simulation 5 ultimately peaks in the m = 6 mode.

2016ApJ...824..138Y__Frail_et_al._2013_Instance_1

The above qualitative theoretical reasoning raises the question about why would Swift J1834.9−0846 be the only magnetar so far powering a wind nebula, given that previous searches around individual magnetars have returned no sign of extended emission attributable to wind nebulae (e.g., Viganò et al. 2014). With only one observed so far, it is difficult to draw any firm conclusions. Nevertheless, Swift J1834.9−0846 has some interesting characteristics that are not shared with the entire magnetar population. First, the environment of Swift J1834.9−0846 is extremely crowded, with a Fermi GeV source, an H.E.S.S. TeV source, an SNR, a GMC, and an OH maser in its vicinity (Frail et al. 2013; H.E.S.S. Collaboration et al. 2015). The relationship between all these sources is unclear. However, it is tempting to speculate that environmental effects from such a rich field could be playing a role in the production of this wind nebula (e.g., triggering of pair cascade by external gamma rays from a nearby source; Shukre & Radhakrishnan 1982; Istomin and Sob’yanin 2011). Second, the Swift J1834.9−0846 X-ray luminosity in quiescence is erg s−1. Only five other magnetars (SGR 0418+5729, SGR 1745−2900, XTE J1810−197, Swift J1822.3−1606, 3XMM J185246.6+003317)20 20 http://www.physics.mcgill.ca/ pulsar/magnetar/main.html have luminosities ≲1032 erg s−1. Among these five, three have the smallest surface B fields measured (SGR 0418+5729, Swift J1822.3−1606, 3XMM J185246.6+003317; G), and only one source, SGR 1745−2900, has a rotational energy loss rate similar to Swift J1834.9−0846, while the rest have at least an order of magnitude lower. Hence, from an observational point of view, it seems that the combination of very weak X-ray luminosity, a magnetar-like B-field strength, and a somewhat large (properties that are only shared by the Galactic center magnetar SGR 1745−2900) may favor wind nebula production. Another possibility is that the Swift J1834.9−0846 magnetar/nebula system is an older analog to the Kes 75 system, where the central pulsar evolves into a magnetar while preserving its originial PWN.

2021MNRAS.503..594S__Tremaine,_Ostriker_&_Spitzer_1975_Instance_1

Nuclear star clusters (NSCs) are dense and massive clusters observed with high frequency ($\gt 80{{\ \rm per\ cent}}$) at the centre of galaxies with stellar masses 109–1011 M⊙ (e.g. Böker et al. 2002, 2004; Côté et al. 2006; Turner et al. 2012; Baldassare et al. 2014; den Brok et al. 2014; Georgiev & Böker 2014; Sánchez-Janssen et al. 2019; Pechetti et al. 2020). These extreme environments often harbour a central supermassive black hole (as in the case of our Galaxy), and represent the most dense stellar systems in the Universe. We refer to the recent and complete review by Neumayer, Seth & Böker (2020) for more details about NSCs. There are two main formation channels that are thought to compete in NSC formation: in situ formation via fragmentation of gaseous clouds in the galactic centre (e.g. Loose, Kruegel & Tutukov 1982), or via orbital segregation and merger of massive star clusters that migrate towards the galactic centre via dynamical friction (Tremaine, Ostriker & Spitzer 1975; Capuzzo-Dolcetta 1993). The latter formation channel, named dry-merger scenario, has been widely explored theoretically and numerically (Capuzzo-Dolcetta & Miocchi 2008a, b). For instance, high-resolution N-body models suggested that the Milky Way (MW) NSC might have formed through this mechanism (Antonini et al. 2012; Tsatsi et al. 2017; Arca-Sedda et al. 2020), which might explain both the structure and kinematics of the Galactic NSC. The dry-merger scenario also provides a successful explanation for the potential formation of NSCs in young galaxies and for the seemingly absence of nucleated regions in small dwarf and massive ellipticals (Arca-Sedda & Capuzzo-Dolcetta 2014, 2017). In fact, several semi-analytical models have shown that the dry-merger scenario leads to correlations between the NSC and the host galaxy properties pretty similar to the observed one (Antonini 2013; Arca-Sedda & Capuzzo-Dolcetta 2014; Gnedin, Ostriker & Tremaine 2014; Capuzzo-Dolcetta & Tosta e Melo 2017). None the less, several features of NSCs seem hard to explain as the result of star cluster merging events. For instance, NSCs exhibit a complex star formation history that seems to suggest the occurrence of several episodic star formation events over the entire course of their lifetime (Neumayer et al. 2020). Such a feature can also be easily explained with in situ formation (Böker et al. 2004), thus suggesting that the formation and evolution of NSCs is likely the result of both scenarios operating in concert. Here, we present our numerical approach to test this scenario in one of the clearest examples of two massive star clusters caught in the process of merging in the nucleus of the nearby MW-like spiral galaxy NGC 4654 shown in Fig. 1 and a zoom in on its nucleus in Fig. 2. Their projected separations, photometric mass, and assumption for the local velocity field of an MW-like galaxy suggest that they should be on a short, few tens of Myr, collision course before they completely merge (Georgiev & Böker 2014). However, this pure analytical expectation needs to be tested in order to gain a deeper knowledge on the following: (1) what will be the merging time-scales given the cluster current observational properties; (2) how the physical and observational properties of the final product depend on the merger dynamics and how such properties compare to those of current NSCs in galaxies of similar mass and type as NGC 4654; and (3) in the hypothesis that the two clusters contain different stellar populations, what are the expected distributions and fractions of the stars coming from the two progenitors in the merger product.

2022AandA...659A..41E__Sarbadhicary_et_al._2017_Instance_1

The age of a neutron star is difficult to measure, as for many other astronomical sources. The most robust way to do it is by identifying the birth supernova of the neutron star. However, this can be done precisely only for a very small number of objects, as 5–10 supernovae have historically been observed in our galaxy (Stephenson & Green 2005), and neutron stars are faint sources–practically undetectable at distances beyond the Magellanic Clouds. The explosions, however, leave imprints in the interstellar medium that can remain visible at radio wavelengths for 10 − 100 kyr (Sarbadhicary et al. 2017), thereby allowing the association of pulsars with supernova remnants (SNRs). However, pulsars are rarely found at the centre of SNRs (Frail et al. 1994), as most are expelled like bullets during the explosions possibly due to asymmetries in the process (e.g. Socrates et al. 2005). The transverse velocities of pulsars (based on proper motion and distance estimates) are particularly large, with a mean close to 310 km s−1 (Hobbs et al. 2005), which is at least ten times larger than the average velocities for stars in the solar neighbourhood (e.g. Gaia Collaboration 2018). Moreover, some measured velocities range as high as 1000 km s−1 (Chatterjee et al. 2005; Deller et al. 2019). Thus, associations between SNRs and pulsars are not always straightforward to make (e.g. see the chapter on young pulsars in Lyne & Graham-Smith 2012). The farther the pulsar is from the explosion site, the higher the possibility that the pulsar and SNR are unrelated. In order to confirm an association, it could be necessary to account for up to 100 kyr of evolution of the SNR (that we assume as the maximum possible age of a SNR), and movement across the Galaxy of the pulsar (e.g. Suzuki et al. 2021). In some situations, proper motion measurements for the pulsars can shed light on the matter. For an association to be secure, the pulsar must be moving away from where the explosion took place (usually adopted as the centre of the SNR), and the time necessary to move the pulsar to its current position must match the age of the system. If such time coincided with an independent age measurement of the SNR or the pulsar, or both, then the association would be concretely confirmed. However, this is rarely possible as SNR and pulsar ages are hard to obtain.

2021MNRAS.505.5427N__Buen-Abad_et_al._2018_Instance_1

While the status of the S8 discrepancy is perhaps somewhat less clear than that of the H0 tension, it is beyond question that there overall is some disagreement between high- and low-redshift probes of the amplitude of matter fluctuations (see, for instance, Di Valentino et al. 2020c, for a concise review of the problem). It is thus worthwhile to investigate whether new physics might solve or at least alleviate the S8 discrepancy, a possibility that has been investigated in several works. Models that have been contemplated in this sense include for example active and sterile neutrinos (Battye & Moss 2014; MacCrann et al. 2015; Feng, Zhang & Zhang 2017; Vagnozzi et al. 2017; Mccarthy et al. 2018), ultra-light axions (Hlozek et al. 2015), decaying dark matter (DM; Enqvist et al. 2015; Chudaykin, Gorbunov & Tkachev 2018; Di Valentino et al. 2018; Abellán et al. 2020; Chen et al. 2021; Pandey, Karwal & Das 2020; Xiao et al. 2020; Abellán, Murgia & Poulin2021), extended or exotic DM and/or dark energy (DE) models and interactions (Kunz, Nesseris & Sawicki 2015; Kumar & Nunes 2016; Pourtsidou & Tram 2016; Gariazzo et al. 2017; Benetti, Graef & Alcaniz 2018; Buen-Abad et al. 2018; Kumar, Nunes & Yadav 2018, 2020a, b; Poulin et al. 2018; Archidiacono et al. 2019; Di Valentino et al. 2019b, 2020b; Lambiase et al. 2019; Vagnozzi et al. 2019; Chamings et al. 2020; Dutta et al. 2020; Heimersheim et al. 2020; Jiménez et al. 2020; Choi, Yanagida & Yokozaki 2021) including unified dark sector models (Camera, Martinelli & Bertacca 2019), modified gravity models (Dossett et al. 2015; De Felice & Mukohyama 2017; Nesseris, Pantazis & Perivolaropoulos 2017; Kazantzidis & Perivolaropoulos 2018, 2019; Barros et al. 2020; De Felice, Nakamura & Tsujikawa 2020; Skara & Perivolaropoulos 2020; Zumalacarregui 2020; Marra & Perivolaropoulos 2021), and more generally extended parameter spaces (Di Valentino & Bridle 2018; Di Valentino, Melchiorri & Silk 2020), among the others. It is also worth noting that most of the models invoked to address the S8 discrepancy do so at the expense of worsening the H0 tension, and vice versa (see e.g. Vagnozzi et al. 2018; Poulin et al. 2018; Kumar et al. 2019a; Hill et al. 2020; Alestas & Perivolaropoulos 2021), highlighting the importance of a conjoined analysis of the two tensions (Di Valentino et al. 2020b; Di Valentino, Linder & Melchiorri 2020a).

2021ApJ...917L..38K__Mushtukov_et_al._2015a_Instance_1

With the increase in precision of CRSF measurements, the dependence of CRSFs on luminosity and spin phase has become obvious and is one of the important areas of current X-ray pulsar research, as it potentially allows probing of emission region geometry and properties (Staubert et al. 2019). Of particular interest is the luminosity dependence of the line properties, as it might be used to probe the mechanism of plasma deceleration and the transition between so-called sub- and supercritical accretion regimes associated with the onset of an accretion column first suggested by Basko & Sunyaev (1976). As a result, two main accretion regimes, super- and subcritical accretion, are expected depending on whether the source luminosity is higher or lower than a “critical luminosity” Lcrit, which strongly depends on the magnetic field of the NS (Basko & Sunyaev 1976; Becker et al. 2012; Mushtukov et al. 2015a). In the supercritical regime, for L > Lcrit, an accretion column forms, and the falling plasma is decelerated by a radiation shock that forms at a certain distance from the NS surface. In this case, the emission height increases with increasing luminosity. In the subcritical regime, for L Lcrit, the infalling matter is presumably decelerated by Coulomb interaction, forming a region whose height decreases with increasing luminosity. At even lower luminosity, the description of the deceleration process of falling material is not very conclusive. A reasonable scenario is that there exists a transition for the regime from Coulomb-stopping to gas-shock dominance beyond which only a small accretion mound forms on the NS surface. Based on this picture, and considering that the local magnetic field strength decreases with height, the correlation between the CRSF line energy and luminosity can be used to trace the accretion regimes. For instance, a transition between the sub- and supercritical regimes was recently reported by Doroshenko et al. (2017) and Vybornov et al. (2018) for V0332+53 during a giant outburst.

2018AandA...609A.131G__Heithausen_2012_Instance_1

Moreover, there could also be some contribution to the detected temperature asymmetry from high-latitude gas clouds in our Galaxy along the line of sight toward M 81. In this respect we note that M 81 is at about 40.9° north of the Galactic disk, where contamination from the Milky Way is expected to be low. However, interpretation of astronomical observations is often hampered by the lack of direct distance information. Indeed, it is often not easy to judge whether objects on the same line of sight are physically related or not. Since the discovery of the Arp’s Loop (Arp 1965) the nature of the interstellar clouds in this region has been debated; in particular whether they are related to the tidal arms around the galaxy triplet (Sun et al. 2005; de Mello et al. 2008) or to Galactic foreground cirrus (Sollima et al. 2010; Davies et al. 2010). Already Sandage et al. (1976) presented evidence showing that we are observing the M 81 triplet through widespread Galactic foreground cirrus clouds and de Vries et al. 1987 built large-scale HI, CO, and dust maps that showed Galactic cirrus emission toward the M 81 region with NH ≃ 1−2 × 1020 cm-2. The technique used to distinguish between the emission from extragalactic or Galactic gas and dust relies on spectral measurements and on the identification of the line of sight velocities, which are expected to be different in each case. Unfortunately, in the case of the M 81 Group, this technique appears hardly applicable since the radial velocities of extragalactic and Galactic clouds share a similar LSR (local standard of rest) velocity range (Heithausen 2012). Several small-area molecular clouds (SAMS), that is, tiny molecular clouds in a region where the shielding of the interstellar radiation field is too low (so that these clouds cannot survive for a long time), have been detected by Heithausen (2002) toward the M 81 Group. More recently, data from the Spectral and Photometric Imaging Receiver (SPIRE) instrument onboard Herschel ESA space observatory and Multiband Imaging Photometer for Spitzer (MIPS) onboard Spitzer allowed the identification of several dust clouds north of the M 81 galaxy with a total hydrogen column density in the range 1.5–5 × 1020 cm-2 and dust temperatures between 13 and 17 K (Heithausen 2012). However, since there is no obvious difference among the individual clouds, there was no way to distinguish between Galactic or extragalactic origin although it is likely that some of the IR emission both toward M 81 and NGC 3077 is of Galactic origin. Temperature asymmetry studies in Planck data may be indicative of the bulk dynamics in the observed region provided that other Local (Galactic) contamination in the data is identified and subtracted. This is not always possible, as in the case of the M 81 Group, and therefore it would be important to identify and study other examples of dust clouds where their origin, either Galactic or extragalactic, is not clear. One such example might be provided by the interacting system toward NGC 4435/4438 (Cortese et al. 2010) where the SAMS found appear more consistent with Galactic cirrus clouds than with extragalactic molecular complexes. Incidentally, the region A1 within R0.50 has been studied by Barker et al. (2009), who found evidence for the presence of an extended structural component beyond the M 81 optical disk, with a much flatter surface brightness profile, which might contain ≃10–15% of the M 81 total V-band luminosity. However, the lack of both a similar analysis in the other quadrants (and at larger distances from the M 81 center) and the study of the gas and dust component associated to this evolved stellar population, hamper our understanding of whether this component may explain the observed temperature asymmetry toward the M 81 halo.

2022MNRAS.511.1750J__Zirker_1977_Instance_1

Table 1 depicts the heliolatitudes of the solar offset points on different experiment days and the corresponding heliocentric distances. It is to be noted that owing to the trajectory constraints of the spacecraft along the ecliptic plane, a pure latitudinal dependence (at a constant heliocentric distance) of the spectral index cannot be assessed here. However, variations in the spectral index values with respect to the complementary heliolatitudes of the proximate points of the ray path between 5° and 39° N can be observed, where the higher-heliolatitude spectra are flatter (lower αf) than the lower-heliolatitude spectra (see Table 1). Low-heliolatitude coronal regions have streamer structures where the slow solar winds, which have a highly turbulent structure, are generated (Woo & Martin 1997). The fast solar winds emanate from coronal holes at higher heliolatitudes (Zirker 1977). The coronal holes, which generally have a higher magnetic field, lead to a lower sound wave to Alfvénic wave ratio and a relatively lower turbulence cascading energy. Consequently, the turbulence requires a longer time (and hence larger heliocentric distances) to achieve a fully developed turbulence spectrum. Thus, at a given heliocentric distance (but varying heliolatitude) of a proximate point of the satellite radio ray path, the fast (high-latitude) solar wind has flatter spectra compared with slow (low-latitude) solar wind spectra, as reported in earlier studies (Pätzold et al. 1996; Efimov et al. 2008, 2017). Efimov et al. (2008) and Chashei et al. (2007) observed that this change of turbulence regime is prominent during periods of minimum solar activity, as coronal holes (the source of the high-latitude fast solar wind) exist in abundance, and there is relative lack of the active regions that generate slow solar winds. It is, however, intriguing that we notice this aspect in our study, which was conducted during a period of relatively low solar activity.

2021MNRAS.503.3065S__Silverman_1986_Instance_1

In this work, the difference between G and NG clusters plays a major role. In a first step, we focus our attention on characterizing the structure and distribution of galaxy member properties in each class. In Fig. 1, we show the distributions of: (a) logarithmic virial mass ($\rm log(\mathit{ M}_{200}/M_{\odot })$); (b) r-band absolute magnitude; (c) a proxy for the concentration of stellar mass in each cluster, defined as $R_{80}/R_{20}$, where $R_{x}$ is the projected radius within which the stellar mass represents x per cent of the total stellar mass within $R_{200}$; (d) velocity dispersion along the LOS; and (e) the distribution of cluster mean stellar mass of bright galaxies within $R_{200}$ ($\langle M_{\mathrm{ stellar}}^{C} \rangle$).3 We compare the distributions using two different statistical tests: AD and Wilcoxon Rank Test (Wlx; see Engmann & Cousineau 2011 and Gehan 1965 for a review of both).4 The results are shown in each panel. Aside from the histograms, we add a kernel smoothed curve, shown as shaded area, which is derived directly from the data set using an Epanechnikov Kernel Density estimator (Silverman 1986) with a bandwidth equal to 1.5 times the bin size. We find that G and NG clusters have statistically different distributions. In panel (a), we note that NG clusters tend to have higher values of $M_{200}$ in comparison to G clusters. Namely, we find that 32.4 per cent (11/34) of NG clusters have $\mathrm{ log}(M_{200}/\mathrm{ M}_{\odot })\gt 14.75$, while this fraction decreases to 6.3 per cent (9/143) in G clusters. The majority of G systems ($\sim 71.3{{\ \rm per\ cent}}$) have $\mathrm{ log}(M_{200}/\mathrm{ M}_{\odot })\lt 14.5$. Panel (b) shows an excess of fainter galaxies5 in NG clusters in comparison to G systems. We find that 30.5 per cent of NG cluster members have $M_{r} \ge -19.5$, while the equivalent cut in absolute magnitude yields 14.1 per cent of galaxies in G systems. Panel (c) shows the distribution of concentration in G and NG clusters, revealing that NG cluster galaxies are less concentrated than their G cluster counterparts. Only one NG cluster reaches C > 4.7. In panel (d), we observe an excess of NG clusters with higher velocity dispersion in comparison to G clusters. NG clusters are presumed to be found in a non-virialized state, so that the expected velocity dispersions are higher, and, possibly, the estimated mass may also be an overestimate of the real cluster mass. In any case, this is one more piece of evidence regarding the different state of NG clusters with respect to G systems. Finally, we note in panel (f) an excess of NG clusters with mean stellar mass of $\rm 3 \le \langle \mathit{ M}_{stellar}^{\mathit{ C}} \rangle \lt 4 \times 10^{11}\, M_{\odot }$. In other words, we find that NG clusters have higher virial mass and radii, an excess of fainter galaxies, are less concentrated, contain more massive B galaxies and have higher velocity dispersion in comparison to G systems.

2022ApJ...927..106C__Motte_et_al._2007_Instance_1

To identify the fibers seen in the PPV space, we apply the agglomerative clustering implementation in the scikit-learn package 7 7 https://scikit-learn.org/stable/ (Pedregosa et al. 2011) to the PPV points for each transition. The agglomerative clustering algorithm groups points in N-dimensional space via recursively merging points or clusters (i.e., groups of points) into higher-order clusters such that the pair of points or clusters to be merged minimally increases the linkage distance (Ward 1963). To make the clustering procedure adjustable, we introduce two parameters t x and t v in defining the linkage distance of two PPV points: 7 slink,ij=[tx(xi−xj)]2+yi−yj2+vi−vjtv2, where x and y are the spatial coordinates in physical units, and v is the LoS velocity. For each transition, we adjust the parameters (t x , t v ) and run the algorithm with the modified coordinates of the PPV points as input. By practice we found that the best values of (t x , t v ) for identifying the fiber structures in the H13CO+, N2H+, and NH2D data are (2.8 pc, 1.0 km s−1pc−1), (0.5 pc, 0.8 km s−1pc−1), and (1.0 pc, 2.0 km s−1pc−1), respectively, with which the DR21(OH) ridge is decomposed into 3, 3, and 2 fibers in the PPV space. Figure 4 shows the identified fibers projected on the PoS and their PV plots, with the color coding representing different velocities. In the H13CO+ data, three fibers with roughly north–south orientations are clearly seen; these fibers have distinct velocities, forming a trident with the junction approximately coincident with the DR21(OH) core. We name the three fibers as f1, f2, and f3 from east to west. Among the fibers, f1 is clearly tracing the central-densest part of the dust ridge well known from previous dust-continuum observations (Motte et al. 2007; Hennemann et al. 2012); f2 and f3 are relatively new, not seen in the dust emission, but previous single-dish H13CO+ (1 − 0) observations showed that the ridge slight moves from east to west with the velocity increasing from −5 km s−1 to 0 km s−1 (Schneider et al. 2010), in a manner consistent with the positions and velocities of the three fibers identified here. In particular, f3 is also discernible in the H13CO+(1 − 0) map in Schneider et al. (2010; see their Figure A3), though at a much lower resolution. Given the LoS velocities of the fibers and the existing observations suggesting that the DR21(OH) ridge is in a global collapse (Schneider et al. 2010), one may expect that f1 and f3 are on the far side and the near side along LoS, respectively, and that f2 is probably in the middle. The three fibers in the N2H+ line are less prominent yet are still clearly seen with positions and velocities consistent with those in the H13CO+ line, indicating that they are tracing the same physical entities. While the whole f1 and the southern part of f3 are detected, f2 is fragmented into several parts, probably due to the regional variations of the abundances of the two molecular tracers. On the other hand, in the NH2D line, the whole f1 (though fragmented into parts) and the northern part of f2 are detected, and f3 is completely absent. The appearance of the fibers are also different from that in the H13CO+ and N2H+ lines by their more compact morphologies and smaller widths. In addition, in contrast to what is seen in the H13CO+ and N2H+ lines, the southern end of f1 in the NH2D line is shifted to the west and does not coincide with the DR21(OH) core, indicating that active high-mass star formation has destroyed most of NH2D. The detection information of the fibers in the three transitions is summarized in Table 1.

2022MNRAS.509..212D__Farris_et_al._2015_Instance_1

On the other hand, at separations ≲ 0.01 pc, smaller than those characteristics of spectroscopic binary candidates, many theoretical studies have predicted a significant variability in the observed nuclear light curve due to different physical processes. For example, in studies of the evolution of MBHBs in circumnuclear discs, a modulated gas inflow from the outer gas distribution periodically fuels the accretion discs within the Hill radii of the individual MBHs (that, being smaller than the surrounding circumbinary disc, are commonly referred to as ‘mini-discs’ in the literature, Artymowicz & Lubow 1994; Ivanov, Papaloizou & Polnarev 1999; Hayasaki, Mineshige & Ho 2008; Cuadra et al. 2009; Roedig et al. 2011, 2012; D’Orazio, Haiman & MacFadyen 2013; Farris et al. 2015; D’Orazio et al. 2016; Miranda, Muñoz & Lai 2017; Tang, MacFadyen & Haiman 2017; Bowen et al. 2018; d’Ascoli et al. 2018) as a consequence of the non-axisymmetric and time-dependent potential of the binary. Such modulated inflow could result in a similarly variable luminosity, depending on the properties of the inflowing gaseous streams and of the preexisting mini-discs (see the discussion in Sesana et al. 2012). An alternative cause of observed variability could be the plunging of a very eccentric secondary MBH on to the primary disc, as proposed by Valtonen et al. (2008) for the observed variability of OJ287. Finally, even in the absence of periodic inflows or very eccentric binaries (as expected in the case of a low-mass secondary; D’Orazio et al. 2016; Duffell et al. 2020), variability can be caused by the relativistic Doppler boost of the emitted spectrum during the orbit of the MBHB, resulting in a variable flux observed in fixed observational bands, as proposed for PG 1302-102 in D’Orazio, Haiman & Schiminovich (2015). This last model has the peculiarity of predicting different variability amplitudes at different wavelengths, as demonstrated for the UV versus optical light curves of PG 1302-102 (Xin et al. 2019).

2018MNRAS.478..126G___2018b_Instance_1

It may be appropriate to single out at this point the recent and interesting works by Lin & Ishak (2017a,b) in which the authors run a so-called (dis)cordance test based on using a proposed index of inconsistency (IOI) tailored at finding possible inconsistencies/tensions between two or more data sets in a systematic and efficient way. For instance, it is well known that there is a persistent discrepancy between the Planck CMB measurements of H0 and the local measurements based on distance ladder (Riess et al. 2016, 2018b). At the same time, if one compares what is inferred from Planck 2015 best-fitting values, the LSS/RSD measurements generally assign smaller power to the LSS data parametrized in terms of the weighted linear growth rate f(z)σ8(z). This feature is of course nothing but the σ8-tension we have been addressing in this paper. It is therefore natural to run the IOI test for the different kinds of H0 measurements and also to study the consistency between the H0 and the growth data. For example, upon comparing the constraints on H0 from different methods, Lin & Ishak (2017b) observe a decrease of the IOI when the local H0 measurement is removed. From this fact they conclude that the local measurement of H0 is an outlier compared to the others, what would favour a systematics-based explanation. This situation is compatible with the observed improvement in the statistical quality of the fitting analysis by Solà, Gómez-Valent & de Cruz Pérez (2017b,c) when the local H0 measurement is removed from the overall fit of the data using the RVM and the ΛCDM. In this respect, let us mention that a recent model-independent analysis of data on cosmic chronometers and an updated compilation of SNIa seem to favour the lower range of H0 (Gómez-Valent & Amendola 2018), what would be more along the line of the results found here, which favour a theoretical interpretation of the observed σ8 and H0 tensions in terms of vacuum dynamics and in general of DDE (cf. Fig. 10).

2021AandA...655A..12T__Tang_et_al._2017b_Instance_1

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.

2018MNRAS.480.1796S__Morić_et_al._2010_Instance_1

To distinguish the emission from AGNs and star formation we use the fact that star-forming galaxies (SFGs) are known to exhibit radio–IR correlation across a wide range of luminosities and redshifts (see Condon 1992; Appleton et al. 2004; Basu et al. 2015). The correlation between 1.4 GHz luminosity and IR luminosity (monochromatic IR luminosity as well as bolometric IR luminosity between 8.0 and 1000 $\mu$m) in SFGs is attributed to the fact that both radio and IR emission are closely related to star formation (Ivison et al. 2010). AGNs with predominant radio emission from jet deviate from radio–IR correlation by showing radio-excess (Morić et al. 2010; Del Moro et al. 2013). Therefore, we examine if our NLS1s show radio-excess in the radio–IR correlation. We note that radio–IR correlation can be represented as the ratio of IR flux to 1.4 GHz radio flux density i.e. q=log(SIR/$S_{\rm 1.4 \, GHz}$) (see Appleton et al. 2004). For our radio-detected NLS1s we estimate q$_{\rm 22\, {\mu }m}$=log($S_{\rm 22 {\mu }m}$/$S_{\rm 1.4 \, GHz}$), where IR flux at 22 $\mu$m is taken from Wide-field Infrared Survey Explorer (WISE),2 and 1.4 GHz flux density is taken from FIRST whenever available, otherwise from NVSS. WISE is an all sky survey carried out at four MIR photometric bands namely W1 [3.6 $\mu$m], W2 [4.6 $\mu$m], W3 [12 $\mu$m], and W4 [22 $\mu$m], with 5σ sensitivity of 0.08, 0.11, 1.0, and 6.0 mJy, and angular resolution of 6.1, 6.4, 6.5, and 12.0, respectively (Wright et al. 2010). Using the most recent version of WISE source catalogue (AllWISE3 data release) we obtain MIR counterparts for 481, 480, 440, and 354 of our 498 radio-detected NLS1s with SNR ≥ 5.0 in W1, W2, W3, and W4 bands, respectively. The WISE counterparts of our radio-detected NLS1s are searched within a circle of 2.0 arcsec radius centred at SDSS optical positions. q$_{\rm 22 {\mu }m}$ is estimated using k-corrected fluxes, where IR spectral index is derived using W3 and W4 band fluxes, and radio spectral index is derived using 1.4 GHz and 150 MHz flux densities, whenever available, otherwise an average radio spectral index of −0.7 is considered.

2018AandA...609A...2M__Titov_&_Démoulin_(1999)_Instance_1

The equilibrium between the MFR and the two magnetic charges can lead to an infinite twist on the surface of the MFR because no toroidal field exists outside the MFR. The magnetic field of the line current I0 is included to decrease this to reasonable values, typically less than 4π, suggested by many observations (Liu & Alexander 2009; Wang et al. 2015; Guo et al. 2012). Combining contributions from line current I0 and the poloidal current inside the MFR, the expression for the toroidal field is written as (7)\begin{eqnarray} \label{eq:bt} B_{t}&\approx& \frac{\mu_0 I_0}{2\pi}\sqrt{\frac{2 \alpha I^2}{a^2 I_0^2}\left(1-\frac{r_{\rm a}^2}{a^2}\right)+\frac{1}{R^2}},~r_{\rm a}\leq a,\nonumber \\ &=& \frac{\mu_0 I_0}{2\pi r_{\perp}},~r_{\rm a}>a. \end{eqnarray}Bt≈μ0I02π2αI2a2I021−ra2a2+1R2,ra≤a,=μ0I02πr⊥,ra>a.For an analytical expression of the poloidal magnetic field, we follow the practice of Titov & Démoulin (1999), in which poloidal field is given in terms of the vector potential as in (8)\begin{equation} B_{p}=-\frac{\partial A_{t}}{\partial x}\frac{\vec{r}_{\perp}}{r_{\perp}}+\left(\frac{\partial A_t}{\partial r_{\perp}}+\frac{A_t}{r_{\perp}}\right)\vec{x}. \end{equation}Bp=−∂At∂xr⊥r⊥+∂At∂r⊥+Atr⊥x.The toroidal At outside the MFR (ρ ≥ a) is (9)\begin{equation} A_{t}(x,r_{\perp}) \approx \frac{\mu_{0}I}{2\pi}\sqrt{\frac{R}{r_{\perp}}}\mathcal{A}(k), \end{equation}At(x,r⊥)≈μ0I2πRr⊥𝒜(k),and inside (ρa) is (10)\begin{equation} A_{t}(x,r_{\perp}) \approx \frac{\mu_{0}I}{2\pi}\sqrt{\frac{R}{r_{\perp}}} (\mathcal{A}(k_{\rm a})+\mathcal{A}'(k_{\rm a})(k-k_{\rm a})). \end{equation}At(x,r⊥)≈μ0I2πRr⊥(𝒜(ka)+𝒜′(ka)(k−ka)).Here, \begin{eqnarray} &&\mathcal{A}(k)=k^{-1}[(2-k^2)K(k)-2E(k)], \\ &&k=2\sqrt{\frac{r_{\perp}R}{(r_{\perp}+R)^2+x^2}}, \\ &&k_{\rm a}=2\sqrt{\frac{r_{\perp}R}{4r_{\perp}R+a^2}}, \end{eqnarray}𝒜(k)=k-1[(2−k2)K(k)−2E(k)],k=2r⊥R(r⊥+R)2+x2,ka=2r⊥R4r⊥R+a2,and its derivative (14)\begin{equation} \mathcal{A}'(k)=\frac{(2-k^2)E(k)-2(1-k^2)K(k)}{k^2(1-k^2)}\cdot \end{equation}𝒜′(k)=(2−k2)E(k)−2(1−k2)K(k)k2(1−k2)·These expressions contain the complete elliptic integrals of the first and the second kinds, K(k) and E(k).

2020AandA...633A.147B__Miville-Deschênes_et_al._2017_Instance_1

Another possible indication of the different nature of the smallest clouds with respect to the largest ones is shown by their behaviour in the velocity dispersion vs. radius relation. This relation, known as the first Larson relation (Larson 1981), is expected to be of the form σv ∝ Rβ. The observed non-thermal motions in MCs are always supersonic (thermal motions for gas at T = 10 K is ≃0.1 km s−1), and the expected value of β, if the origin of these motions is purely supersonic turbulence, the so-called Burger turbulence, is β = 0.5 (McKee & Ostricker 2007). Various values of β in MCs have been derived by several authors, ranging from β ∼ 0.38 (Larson 1981) to β = 0.6 ± 0.3 (Miville-Deschênes et al. 2017). Restricting the evaluation of the relation to the interstellar clouds of the outer Galaxy, previous estimates give β = 0.47 ± 0.08 (Sodroski 1991), β = 0.53 ± 0.03 (Brand & Wouterloot 1995), β = 0.45 ± 0.04 (May et al. 1997), and β = 0.53 ± 0.06 for MCs in the third quadrant of the Galaxy (Rice et al. 2016), all in agreement with the a Burger-like turbulence. However, the nature of the non-thermal motions in MCs is still debated (Krumholz et al. 2019), and it is not yet clear whether they originate solely from turbulence or from large-scale gravitational collapse (Ballesteros-Paredes et al. 2011; Traficante et al. 2018a,b; Merello et al. 2019). We plot the σv–R relation for clouds of the FQS catalogue in Fig. 15. Two different regimes appear for clouds with R ≥ 2 pc and clouds with R   2 pc. For MCs with R ≥ 2 pc the velocity dispersion increases with radius with an exponent β = 0.59, similar to what is expected from pure supersonic turbulence, and to that previously measured. On the other hand, for MCs with R   2 pc the relation is almost flat, with β = 0.08. The non-thermal motions in the smallest clouds seem to be independent of cloud size. These condensations, likely to be only transient structures, are probably not formed by the turbulent ISM, which would lead to a Burger-like spectrum. It may be possible that these structures are the result of a large-scale effect, such as spiral-arm density-wave shock that injects the same amount of kinetic energy to all these objects. This large-scale effect would dominate over the local turbulence or the self-gravity within the smallest clouds, becoming increasingly irrelevant in the larger clouds.

2020MNRAS.496.4468S__Chang_et_al._2018_Instance_1

More interestingly, there are hints suggesting that the impacts of projection effects extend beyond richness misidentification. First, when Miyatake et al. (2016) reported a possible detection of assembly bias for subsamples of clusters divided based on the concentration of member galaxies, the signal appeared too large compared to theoretical predictions (Wechsler et al. 2006; Dalal et al. 2008) for the ΛCDM model. Subsequent works found that this large apparent signal might be due to projection effects (Busch & White 2017; Zu et al. 2017; Sunayama & More 2019). Secondly, when More et al. (2016) reported a detection of the so-called ‘splashback’ radius, a physically motivated boundary of cluster haloes, for redMaPPer clusters, the location of the splashback radius was found to be smaller than theoretical expectations (also see Chang et al. 2018, for a similar detection). Follow-up studies (Shin et al. 2019; Zürcher & More 2019) used samples of clusters selected based on the Sunyaev-Zel’dovich effect and found a different location of the splashback radius consistent with theoretical expectations, albeit with larger errors, suggesting that the original location may have been impacted by projection effects. The recent analysis in Murata et al. (2020) further indicated that previous analyses of the splashback radius for optically selected clusters may suffer from projection effects. Third, Murata et al. (2018) developed a forward modelling approach to calibrate the richness–mass relation from a joint measurement of cluster abundances and cluster lensing. However, they found that a population of less massive haloes, down to 1012h−1M⊙, had to be introduced in order for the ΛCDM model prediction to match the observations. Overall, these studies indicate that projection effects may impact not only cluster richnesses but also other cluster observables such as cluster lensing, which, if true, can render problematic the standard approach for cluster cosmology employed by photometric surveys.

2022ApJ...927...89W__Stetson_1987_Instance_1

IRIS always takes short exposure (with the shortest possible exposure time of 2.2 s) and long exposure with specified exposure time (14.5 s) one by one. In order to estimate and subtract the sky background, each field was observed in 10 dithered positions. Raw exposures were sky-subtracted and flat-fielded using standard IRAF (Tody 1986) routines. An astrometric solution was performed with Sextractor (Bertin & Arnouts 1996) and SCAMP (Bertin 2006), and then 10 dithered frames were resampled and combined with SWARP (Bertin 2010) into a single, final image (details of the pipeline used for calibrations can be found in Watermann 2012). Aperture photometry was performed with the DAOPHOT (Stetson 1987) package, and instrumental photometry was tied to the 2MASS system using the constant stars present in a given field as standards (usually more than 3 stars of brightness similar to our target with quality flag AAA in the 2MASS catalog). If there were no comparison stars of similar magnitude in a given field (cases of VY Pyx, SW Tau, and AL Vir), we used long exposures (14.5 s) to measure the brightness of comparison stars while the target was measured in the short exposure (2.2 s). We found a non-negligible color term in J band, and it amounts to −0.07 (J − K s ). Internal precision of our photometry is at a level of 0.02 mag. In order to check correctness of our transformation to the 2MASS system, we compared the magnitudes of the constant stars present in the observed fields (transformed to the 2MASS system using an approach identical to our scientific objects) with the corresponding magnitudes from the 2MASS catalog. The observed fields offered very limited numbers of bright constant stars; thus for this test, we mostly used sources that were adopted as comparison stars in the transformation of the photometry of science targets. Each considered star was excluded from the set of comparison stars while transforming its own photometry. This test is presented in Figure 2. The mean difference between IRIS and 2MASS photometry is zero with the error on the mean of 0.002 mag in each band. We adopt this value as our zero-point uncertainty, which contributes to the systematic error of our calibration of PLRs.

2020ApJ...897...38D__Gough_1990_Instance_1

The problem of imaging global magnetic fields through helioseismology has been relatively underexplored, as studies have mainly focused on the solar rotational profile and other global and local flow fields (Lavely & Ritzwoller 1992; Basu et al. 1999; Giles 2000; Zhao & Kosovichev 2004; Hanasoge et al. 2012b, 2017). Some studies have analyzed the effects of local fields, such as those contained in a sunspot, on the reduction of wave power (Cally 2000; Schunker & Cally 2006), and changes in wave speed and flow patterns (Gizon et al. 2009; Švanda et al. 2014; Khomenko & Collados 2015; Rabello-Soares et al. 2018; Braun 2019). Nevertheless, early attempts (e.g., Gough 1990) that were made to study the impact of global magnetic fields (Lorentz-stress perturbations) on the eigenfrequencies and eigenfunctions of the standard solar model (model S in Christensen-Dalsgaard et al. 1996) focused on the details of the forward problem of frequency shifts induced by an axisymmetric magnetic field not aligned with the rotation axis. Dziembowski & Goode (2004) considered near-surface, small-scale as well as a large-scale deep toroidal fields at the tachocline. However, lacking a formal relation between Lorentz stresses and the consequent seismic signatures (sensitivity kernels), these efforts suffered from the drawback of restricting the field geometry to make the problem tractable. Several numerical studies have also considered small perturbations around a magnetized background medium (Cameron et al. 2008, 2010; Hanasoge et al. 2012a; Schunker et al. 2013). Attempts at deducing temporal changes in magnetic field configurations from variations in angular velocity have been met with limited success (Antia et al. 2013). In a recent analysis, Cutler (2017) explored the potential of learning about magnetic fields from mode-coupling theory. Hanasoge (2017) derived analytical forms of the Lorentz-stress sensitivity kernels in the context of normal-mode coupling. This has made it possible to consider the treatment of a completely general magnetic field configuration as a perturbation around a hydrostatic background state. This forms the basis of the current study.

2018MNRAS.474.3162T__Gavazzi_et_al._2004_Instance_1

We then used the fxcor task to determine the redshifts of selected objects in the field. This task allows to calculate the radial velocity through Fourier cross-correlation between the spectrum of the object under analysis and a reference (template) spectrum (Tonry & Davis 1979). Both spectra are continuum subtracted and Fourier filtered before doing the correlation, while dispersions are equalized by rebinning to the smallest dispersion. For this work, we used two reference spectra: that of NGC 4449 (Kennicutt 1992), an irregular galaxy that has well-defined emissions, and the spectrum of NGC 4387 (Gavazzi et al. 2004), an elliptical galaxy showing strong absorptions. Both templates were downloaded from NED. For those spectra where it was possible to establish a tentative redshift value, typical emission lines were identified, such as H γ, [O ii] (3727 Å), H β, [O iii] (4959 Å) and [O iii] (5007 Å), and/or absorptions, like Ca ii (H+K), Ca+Fe, Na i and H α. Finally, the redshift adopted for each object was computed from Gaussian fits to two or more such features in its spectrum. Three objects (slits #6, #19 and #23) turned out to be Galactic stars5 (we give their radial velocities in Table 1), while no reliable redshift value could be obtained for other five objects (slits #5, #11, #13, #17 and #20), due to the low S/N ratio of their spectra (we will return to objects #5 and #11 in Section 3.2). Tentative redshifts for two of them (slits #17 and #20) were measured through just one emission line each, which we assumed to be [O ii] (3727 Å) and H δ, respectively (based on their colours and spiral morphology). So, their tentative redshift values would be $z_{\#17} \simeq 0.476$ and $z_{\#20} \simeq 0.479$. No clear emission or absorption lines could be identified in the blazar's spectrum, besides telluric lines and diffuse interstellar bands (DIBs), so no definite redshift value could be established in this way for 3C 66A (see, however, Section 3.3 for our analysis on probable foreground absorptions on the blazar's spectrum). Redshifts were thus established for 15 (plus two just tentative) out of the 24 selected objects. Our results are shown in Table 1, where we also include the only galaxy with a previously published redshift within our GMOS field (G2 in Bow97).

2018MNRAS.477..392L__Izotov_&_Thuan_1998_Instance_1

Here, we present new Very Large Telescope (VLT) VIMOS (Le Févre et al. 2003) observations of two star-forming dwarf galaxies using the integral field unit (IFU) spectroscopy mode (hereafter VIMOS-IFU). UM 461 (the upper panel in Fig. 1) is a well-studied H ii/BCD galaxy (e.g. Taylor et al. 1995; van Zee, Skillman & Salzer 1998; Lagos et al. 2011). This galaxy has been described as formed by two compact and off-centre giant H ii regions (GH iiR), some smaller star-forming regions spread across the galaxy disc and an external stellar envelope that is strongly skewed towards the south-west (Lagos et al. 2011). It has been classified as having a cometary-like morphology with an integrated subsolar metallicity of 12 + log(O/H) = 7.73–7.78 (Masegosa, Moles & Campos-Aguilar 1994; Izotov & Thuan 1998; Pérez-Montero & Díaz 2003). As in most H ii/BCD galaxies, UM 461 has an underlying component of old stars (Telles & Terlevich 1997; Lagos et al. 2011) that exhibits an elliptical outer morphology. Deep Near-Infrared observations with the Gemini/NIRI camera (Lagos et al. 2011) revealed that the star-formation activity in this galaxy is taking place in several star clusters with masses typically between ∼104 M⊙ and ∼106 M⊙. Fig. 1 shows the Kp band image of UM 461 obtained by Lagos et al. (2011). Using the same notation as Lagos et al. (2011), the main GH iiR (the brightest one in Fig. 1) in our study is composed of the star clusters nos. 2 and 3, while the faintest one is formed by star clusters nos. 5–7. Taylor et al. (1995) proposed that the SE tail in their H i image of UM 461 was formed as a result of a tidal interaction with UM 462. However, higher resolution H i maps of UM 461 by van Zee, Skillman & Salzer (1998) did not show the extended SE H i tail seen in the Taylor H i map. This discrepancy is attributed to solar interference in the Taylor map (van Zee, Skillman & Salzer 1998). Moreover, the age distribution of the star cluster population in UM 461 indicates that the current starburst has begun within the last few million years (Lagos et al. 2011). This current starburst time-scale is too short to realistically be attributed to a UM 461/UM 462 interaction.

2016AandA...587A.133G__Wise_et_al._2014_Instance_1

Theoretically, the production, propagation, and escape of LyC photons are related to the physical properties of the galaxies. Firstly, the production of LyC radiation implies the presence of young, massive stars, and therefore of on-going star formation. Because of the fast recombination timescale of the HI atoms, previous episodes of star formation have no significant impact on the production of ionizing photons eventually escaping the galaxy (e.g. Paardekooper et al. 2015). Secondly, the propagation of LyC photons within the ISM is favoured by a negligible amount of dust and low column density of HI (N(HI) ≤ 1018 cm-2) in a 10-pc scale region around the emitting star clusters. This could be the case for galaxies embedded in dark-matter halos with masses less than 108 M⊙ (Yajima et al. 2011; Wise et al. 2014; Paardekooper et al. 2015). However, even galaxies residing in more massive halos can have lines of sight favourable to the propagation and the escape of LyC photons (Gnedin et al. 2008; Roy et al. 2015). Supernova explosions could have cleared their ISM, and star-formation episodes could occur in their outskirts. In addition, “runaway” OB stars up to 1 kpc away from the initial-origin regions are proposed to significantly contribute to the amount of LyC photons finally emitted into the IGM (Conroy & Kratter 2012). Thirdly, LyC photons emitted into the IGM can affect the galaxy environment, changing the ratio of neutral vs ionized gas, eventually fuelling the ISM (e.g. Martin et al. 2012, for a study of ionized-metal outflows and inflows). Simulations at intermediate redshift have shown that the LyC escape fraction (fesc(LyC)) steeply decreases as the dark-matter halo mass (Mh) increases at 3 z 6 (e.g. Yajima et al. 2011) and that the median fesc(LyC) also changes with redshift at z = 4−6 (e.g., Cen & Kimm 2015). It is worth stressing that while some authors find that the LyC escape fraction decreases with the increase in the halo mass (see also Ferrara & Loeb 2013), other works find the opposite trend: fesc(LyC) is found to range from a few percent (e.g. Gnedin et al. 2008) up to 20−30% (e.g. Mitra et al. 2013) or even higher (e.g. Wise & Cen 2009).

2018AandA...609A..13K__Mucciarelli_et_al._(2017)_Instance_1

Gaia 1 is a star cluster that was recently discovered by Koposov et al. (2017) in the first Gaia data release (Gaia Collaboration 2016), alongside with another system of lower mass. Its observation and previous detections were seriously hampered by the nearby bright star Sirius, which emphasized the impressive discovery power of the Gaia mission. This object was first characterized as an intermediate-age (6.3 Gyr) and moderately metal-rich (−0.7 dex) system, based on isochrone fits to a comprehensive combination of Gaia, 2MASS (Cutri et al. 2003), WISE (Wright et al. 2010), and Pan-STARRS1 (Chambers et al. 2016) photometry. Hence, this object was characterized by Koposov et al. (2017) as a star cluster, most likely of the globular confession. Further investigation of Gaia 1 found a metallicity higher by more than 0.5 dex, which challenged the previous age measurement and rather characterized it as a young (3 Gyr), metal-rich (−0.1 dex) object, possibly of extragalactic origin given its orbit that leads it up to ~1.7 kpc above the disk (Simpson et al. 2017). Subsequently, Mucciarelli et al. (2017) measured chemical abundances of six stars in Gaia 1, suggesting an equally high metallicity, but based on their abundance study, the suggestion of an extragalactic origin was revoked. While a more metal-rich nature found by the latter authors conformed with the results by Simpson et al. (2017), the evolutionary diagrams of both studies are very dissimilar and could not be explained by one simple isochrone fit. In particular, it was noted that “the Simpson et al. (2017) stars do not define a red giant branch in the theoretical plane, suggesting that their parameters are not correct” (Fig. 1 of Mucciarelli et al. 2017). Such an inconsistency clearly emphasizes that a clear-cut chemical abundance scale is inevitable for fully characterising Gaia 1, and to further allow for tailored age determinations, even more so in the light of the seemingly well-determined orbital characteristics, Thus, this work focuses on a detailed chemical abundance analysis of four red giant members of Gaia 1, based on high-resolution spectroscopy, which we complement with an investigation of the orbital properties of this transition object. Combined with the red clump sample of Mucciarelli et al. (2017) and reaching down to the subgiant level (Simpson et al. 2017), stars in different evolutionary states in Gaia 1 are progressively being sampled.

2018AandA...615A.148D__Sung_et_al._2013_Instance_1

We study here the Sco OB1 association (Figs. 1 and 2), using this and other techniques. The general properties of this large OB association, which spans almost 5° on the sky, and is surrounded by a ring-shaped HII region called Gum 55, are reviewed by Reipurth (2008). Its central cluster NGC 6231 contains several tens of OB stars, which have been extensively studied. On the other hand, many fewer studies, all recent, were devoted to the full mass spectrum, using optical photometry (Sung et al. 1998, 2013) and X-rays (Sana et al. 2006, 2007; Damiani et al. 2016; Kuhn et al. 2017a,b). The currently accepted distance of NGC 6231 is approximately 1580 pc, and its age is between 2and 8 Myr, with a significant intrinsic spread (Sung et al. 2013; Damiani et al. 2016). No ongoing star formation is known to occur therein, however. Approximately one degree North of the cluster, the loose cluster Trumpler 24 (Tr 24) also belongs to the association. There is little literature on this cluster (Seggewiss 1968; Heske & Wendker 1984, 1985; Fu et al. 2003, 2005) which unlike NGC 6231 lacks a well-defined center and covers about one square degree on the sky. Its age is 10 Myr according toHeske & Wendker (1984, 1985), who find several PMS stars, and its distance is 1570–1630 pc according to Seggewiss (1968). Other studies of the entire Sco OB1 association include MacConnell & Perry (1969 – Hα-emission stars), Schild et al. (1969 – spectroscopy), Crawford et al. (1971 – photometry), Laval (Laval 1972a,b – gas and star kinematics, respectively), van Genderen et al. (1984 – Walraven photometry), and Perry et al.(1991 – photometry). At the northern extreme of Sco OB1, the partially obscured HII region G345.45+1.50 and its less obscured neighbor IC4628 were studied by Laval (1972a), Caswell & Haynes (1987), López et al. (2011), and López-Calderón et al. (2016). They contain massive young stellar objects (YSOs; Mottram et al. 2007), maser sources (Avison et al. 2016), and the IRAS source 16562-3959 with its radio jet (Guzmán et al. 2010), outflow (Guzmán et al. 2011), and ionized wind (Guzmán et al. 2014), and are therefore extremely young (1 Myr or less). The distance of G345.45+1.50 was estimated as 1.9 kpc by Caswell & Haynes (1987), and 1.7 kpc by López et al. (2011), in fair agreement with distances of Sco OB1 stars. In Fig. 1 of Reipurth (2008) a strip of blue stars is visible, connecting NGC 6231 to the region of IC4628.

2016ApJ...832..195N__Jin_et_al._2012_Instance_1

We ignore the density stratification effect in Case I, II, and IIa, because the width of the horizontal current sheet in our simulations is much shorter than the length. The simulation domain extends from x = 0 to x = L0 in the x-direction, and from y = − 0.5 L 0 to y = 0.5 L 0 in the y-direction, in the three cases, with L 0 = 10 6 m. Outflow boundary conditions are used in the x-direction and inflow boundary conditions in the y-direction. For the inflow boundary conditions, the fluid is allowed to flow into the domain but not to flow out; the gradient of the plasma density vanishes; the total energy is set such that the gradient in the thermal energy density vanishes; a vanishing gradient of parallel components plus divergence-free extrapolation of the magnetic field. For the outflow boundary conditions, the fluid is allowed to flow out of the domain but not to flow in, and the other variables are set by using the same method as the inflow boundary conditions. The horizontal force-free Harris current sheet is used as the initial equilibrium configuration of magnetic fields in Case I, 13 B x 0 = − b 0 tanh [ y / ( 0.05 L 0 ) ] 14 B y 0 = 0 15 B z 0 = b 0 / cosh [ y / ( 0.05 L 0 ) ] . The magnetic fields in the low solar atmosphere could be very strong (Jin et al. 2009, 2012; Khomenko et al. 2014; Peter et al. 2014; Vissers et al. 2015) and the magnetic field can exceed 0.15 T in both the intranetwork and the network quiet region (e.g., Orozco Suárez et al. 2007; Martínez González et al. 2008; Jin et al. 2009, 2012). In the work by Jin et al. 2012, the maximum of the field strength was found to be 0.15 T. The magnetic field could be even stronger in the active region near the sunspot. Therefore, we set b0 = 0.05 T in Case I and Case II, and b0 = 0.15 T in Case IIa. Due to the force-freeness and neglect of gravity, the initial equilibrium thermal pressure is uniform. The initial temperature and plasma density are set as T0 = 4200 K and ρ0 = 1.66057 × 10−6 kg m−3 in Case I, and T0 = 4800 K and ρ0 = 3.32114 × 10−5 kg m−3 in Case II and Case IIa. Therefore, the initial plasma β is calculated as β ≃ 0.0583 in Case I, β ≃ 1.332 in Case II, and β ≃ 0.148 in Case IIa. The initial ionization degree is assumed as Yi = 10−3 in Case I, and Yi = 1. 2 × 10−4 in Case II and IIa. The magnetic diffusion in this work matches the form computed from the solar atmosphere model in Khomenko & Collados (2012), and we set η = [ 5 × 10 4 ( 4200 / T ) 1.5 + 1.76 × 10 − 3 T 0.5 Y i − 1 ] m2 s−1 in Case I, and η = [ 5 × 10 4 ( 4800 / T ) 1.5 + 1.76 × 10 − 3 T 0.5 Y i − 1 ] m2 s−1 in Case II and IIa. The first part ∼ T−1.5 is contributed by collisions between ions and electrons, the second part ∼ T 0.5 Y i − 1 is contributed by collisions between electrons and neutral particles. Small perturbations for both magnetic fields and velocities at t = 0 make the current sheet to evolve and secondary instabilities start to appear later in the three cases. The forms of perturbations are listed below: 16 b x 1 = − pert · b 0 · sin 2 π y + 0.5 L 0 L 0 · cos 2 π x + 0.5 L 0 L 0 17 b y 1 = pert · b 0 · cos 2 π y + 0.5 L 0 L 0 · sin 2 π x + 0.5 L 0 L 0 18 v y 1 = − pert · v A 0 · sin π y L 0 · random n Max ( ∣ random n ∣ ) , where pert = 0.08, vA0 is the initial Alfvén velocity, randomn is the random noise function in our code, and Max ( ∣ random n ∣ ) is the maximum of the absolute value of the random noise function. This random noise function makes the initial perturbations for the velocity in the y-direction to be asymmetric, and such an asymmetry makes the current sheet gradually become more tilted, especially after secondary islands appear. The reconnection process is not really symmetrical in nature (Murphy et al. 2012), this is one of the reasons that we use such a noise function. Another reason is that the asymmetric noise function makes the secondary instabilities develop faster. Figure 1(a) shows the distributions of the current density and magnetic fields at t = 0 in case I.

2015AandA...580A.135D__Cormier_et_al._2015_Instance_1

How does the propagation of radiation and the ISM composition affect ISM observables in low-metallicity galaxies? Addressing this question is important to understand the evolution of low-metallicity galaxies, which undergo more bursty star formation than normal galaxies. Nearby star-forming dwarf galaxies present distinct observational signatures compared to well-studied disk galaxies. Dwarfs are usually metal poor, H i rich, and molecule poor as a result of large-scale photodissociation (e.g., Kunth & Östlin 2000; Hunter et al. 2012; Schruba et al. 2012). Mid-IR (MIR) and far-IR (FIR) observations have revealed bright atomic lines from H ii regions ([S iii], [Ne iii], [Ne ii], [O iii], etc.) and PDRs ([C ii], [O i]) (e.g., Hunter et al. 2001; Madden et al. 2006; Wu et al. 2008; Hunt et al. 2010; Cormier et al. 2015). Their spectral energy distributions (SEDs) are also different from spiral and elliptical galaxies and indicative of altered dust properties, with a relatively low abundance of polycyclic aromatic hydrocarbons (PAHs) and perhaps a different dust composition (e.g., Madden et al. 2006; Galliano et al. 2008; Rémy-Ruyer et al. 2013). It is still unknown, however, whether these differences between dwarf and disk galaxies are the direct result of recent star formation activity shaping the ISM or instead a consequence of the low-metallicity ISM that is independent of star formation activity. To answer this, one needs to observe tracers of the interplay between the ISM and various stages of star formation activity. While there are now a number of important studies available on PDR properties modeling FIR lines on large scales in various extragalactic environments (e.g., Kaufman et al. 2006; Vasta et al. 2010; Graciá-Carpio et al. 2011; Cormier et al. 2012; Parkin et al. 2013) or in our Galaxy under solar-metallicity conditions (e.g., Cubick et al. 2008; Bernard-Salas et al. 2012, 2015), only a few studies are published on individual extragalactic regions (Mookerjea et al. 2011; Lebouteiller et al. 2012). Of particular interest are dwarf galaxies, where the effect due to radiative feedback is expected to be most significant. The goal of this paper is to investigate how the low-metallicity ISM reacts under the effects of star formation in regions that have undergone different histories. The nearby low-metallicity galaxy NGC 4214 provides an excellent environment to perform this experiment because it has well-separated star-forming centers, one hosting a super star cluster, which allows us to study the effects of extreme star-forming conditions on the surrounding ISM.

2021MNRAS.501.2112S__Rubin_et_al._2012_Instance_1

This work improves the efficiency of component by component modelling that has been successful in recovering the physical conditions for various individual absorbers (e.g. Churchill & Charlton 1999; Charlton et al. 2000, 2003; Ding et al. 2003a, 2003b; Zonak et al. 2004; Ding, Charlton & Churchill 2005; Masiero et al. 2005; Lynch & Charlton 2007; Misawa et al. 2008; Jones et al. 2010; Lacki & Charlton 2010; Muzahid et al. 2015; Richter et al. 2018; Rosenwasser et al. 2018). Rather than averaging over components and phases, it is possible to determine how much of the H i is associated with these different phases in order to derive separate metallicities for various clouds. Resolving the individual clouds allows us to break the degeneracy for components on the flat part of the Lyα curve of growth, even with coverage of just saturated H i lines, and derive metallicity constraints for different parcels of gas along the line of sight. It is important to do so because different processes, e.g. outflows (Bouché et al. 2012; Bordoloi et al. 2014; Rubin et al. 2014; Schroetter et al. 2016), pristine accretion (Martin et al. 2012; Rubin et al. 2012; Danovich et al. 2015), recycled accretion (Ford et al. 2014), minor and major mergers (Martin et al. 2012; Anglés-Alcázar et al. 2017), are surely contributing to the same system, and it is expected that conditions will vary significantly along a line of sight which can span hundreds of kpc spatially (Churchill et al. 2015; Peeples et al. 2019). This will lead to a more meaningful comparison to galaxy properties. For example, Pointon et al. (2019) did not find a difference between the metallicities of absorbers found within an impact parameter of 200 kpc along the major and the minor axes of isolated galaxies. Based on cosmological hydrodynamic simulations, a larger metallicity is expected along the minor axis due to outflows and a lower metallicity along the major axis due to inflows (Peroux et al. 2020). However, an observational trend could exist, for example, for the minor axis to have some, but not all, high metallicity components, or for the minor axis to have one or more low metallicity components. Such results would be ‘washed out’ by deriving an average metallicity for all gas along a line of sight, which clearly often has multiple complex origins. For some data sets/projects, the new analysis could be transformative, however, to make it feasible to use for large statistical studies it is important that the analysis is semi-automated and robust.

2017ApJ...838...32Y__Somov_et_al._1981_Instance_1

The question whether electrons are able to penetrate into deep and dense layers of the solar atmosphere is currently under scrutiny. Calculations by Emslie (1978) showed that for electrons and/or protons to reach the level, their energy has to be of the order of a few MeV, while electron energy in the strongest flares only reaches several hundreds of keV. Considering the continuity equation for electron precipitation, Syrovatskii & Shmeleva (1972) and Dobranskis & Zharkova (2015) calculated that beam electrons with energies above 100 keV can reach the lower chromosphere with a column depth of 2 × 1021 cm−2, while those with energies of the order of 200 keV are even capable of penetrating to the photosphere (1022 cm−2; see also Zharkova & Gordovskyy 2006). Works by other authors seem to support the idea that power-law beam electrons may precipitate throughout the entire flaring atmosphere down to the photosphere (Brown 1971; Syrovatskii & Shmeleva 1972), causing its heating via inelastic collisions with the ambient plasma via a hydrodynamic response (Somov et al. 1981; Nagai & Emslie 1984; Fisher et al. 1985; Zharkova & Zharkov 2007). These power-law electrons also cause nonthermal excitation and ionization of hydrogen and other elements, combined with radiative transfer, leading to occurrence of emission in the lines and continua during flaring events (Aboudarham & Henoux 1986; Zharkova & Kobylinskii 1993). Observations indicate that CE may be a combination of radiation that originates (i) in an optically thin chromospheric layer owing to heating caused by beam electrons (thermal model), with subsequent recombination producing enhanced Balmer and Paschen continuum, and (ii) in the photosphere and TMR, where the plasmas are excited and ionized directly by collisions with high-energy electrons, resulting in radiative transfer processes in spectral lines and continua, combined with backwarming radiation supplied by the heated chromosphere and corona (e.g., Hudson 1972; Metcalf et al. 1990; Babin et al. 2016; Kleint et al. 2016). Indeed, since the early 1980s the enhanced Balmer continuum has been observed close to the Balmer cutoff at 3646 Å (Neidig 1989; Kerr & Fletcher 2014), and more recently a strong increase of Balmer continuum has been detected over a wide spectral range using IRIS data (Heinzel & Kleint 2014; Kleint et al. 2016).

2015AandA...584A.103S__Potekhin_et_al._2013_Instance_4

Douchin & Haensel (2001; DH) formulated a unified EoS for NS on the basis of the SLy4 Skyrme nuclear effective force (Chabanat et al. 1998), where some parameters of the Skyrme interaction were adjusted to reproduce the Wiringa et al. calculation of neutron matter (Wiringa et al. 1988) above saturation density. Hence, the DH EoS contains certain microscopic input. In the DH model the inner crust was treated in the CLDM approach. More recently, unified EoSs for NS have been derived by the Brussels-Montreal group (Chamel et al. 2011; Pearson et al. 2012; Fantina et al. 2013; Potekhin et al. 2013). They are based on the BSk family of Skyrme nuclear effective forces (Goriely et al. 2010). Each force is fitted to the known masses of nuclei and adjusted among other constraints to reproduce a different microscopic EoS of neutron matter with different stiffness at high density. The inner crust is treated in the extended Thomas-Fermi approach with trial nucleon density profiles including perturbatively shell corrections for protons via the Strutinsky integral method. Analytical fits of these neutron-star EoSs have been constructed in order to facilitate their inclusion in astrophysical simulations (Potekhin et al. 2013). Quantal Hartree calculations for the NS crust have been systematically performed by (Shen et al. 2011b,a). This approach uses a virial expansion at low density and a RMF effective interaction at intermediate and high densities, and the EoS of the whole NS has been tabulated for different RMF parameter sets. Also recently, a complete EoS for supernova matter has been developed within the statistical model (Hempel & Schaffner-Bielich 2010). We shall adopt here the EoS of the BSk21 model (Chamel et al. 2011; Pearson et al. 2012; Fantina et al. 2013; Potekhin et al. 2013; Goriely et al. 2010) as a representative example of contemporary EoS for the complete NS structure, and a comparison with the other EoSs of the BSk family (Chamel et al. 2011; Pearson et al. 2012; Fantina et al. 2013; Potekhin et al. 2013) and the RMF family (Shen et al. 2011b,a) shall be left for future study.

2021MNRAS.500.3527B__Bernet_et_al._2008_Instance_1

The origin of kiloparsec scale magnetic fields observed in the nearby galaxies through the polarized radio synchrotron emission (e.g. Fletcher 2010; Beck 2012, etc.) is attributed to the large-scale dynamo operating in the interstellar medium (ISM). This is driven mainly via the helical turbulent motions in the interstellar medium, coupled with the differential shear and vertical density stratification. This mechanism, along with some phenomenological approximations about the properties of background turbulence, in principle explains the growth of magnetic fields from small initial strengths to large-scale equipartition strengths against the diffusive losses (Beck et al. 1996; Shukurov 2005; Beck & Wielebinski 2013), and the characteristic times it takes for the field to reach the equipartition strength turn out to be of the order of $\sim \, {\rm Gyr}\,$. This is perhaps a much too slow to account for the strong equipartition strength magnetic fields observed in the high-redshift galaxies with z > 1 (e.g. Bernet et al. 2008) or even for that in the slowly rotating nearby galaxies. This discrepancy leads one to invoke some additional mechanism such as cosmic rays (CRs) boosting the typical dynamo action. The idea of CR driven dynamo was initially discussed by Parker (1992), this predicted the possibility of enhanced dynamo action by the virtue of additional CR buoyant instability, that inflates the magnetic field structures (see also Brandenburg 2018). Based on the conventional dynamo formulation Parker further suggested a simple model for the flux loss through the gaseous disc due to buoyancy by substituting the transport terms Bϕ/td. These terms are supposed to encapsulate the non advective flux transport associated with the buoyant instability, and leads to the fast dynamo action in characteristic field mixing times. Hanasz & Lesch (2000) indirectly verified such a dynamo action via the numerical simulations of rising magnetic flux tubes and found e-folding times of mean field of the order of $100\, {\rm Myr}\,$. Supplementing this Hanasz, Wóltański & Kowalik (2009), Siejkowski et al. (2010), Kulpa-Dybeł et al. (2015), Girichidis et al. (2016), etc. also demonstrated the fast amplification of regular magnetic fields via the direct magnetohydrodynamic (MHD) simulation of global galactic ISM including CR driven turbulence, along with the differential shear (but excluding the viscous term). To complement this, we aim here to extend our previous analysis of dynamo mechanism in SN driven ISM turbulence (Bendre, Gressel & Elstner 2015) by including the CR component and investigate the influence of magnetic field dependent propagation of CR on the dynamo. Here we focus on estimating the dynamo coefficients from the direct MHD simulations and effect CR component has on them by comparing with our previous analysis without the CR.

2016AandA...592A..19C__Maraston_et_al._(2009)_Instance_1

Since the star-formation histories of galaxies (ETGs included, e.g. De Lucia et al. 2006; Maraston et al. 2009) can be stochastic and include multiple bursts, we also verify the full-spectrum fitting capabilities to retrieve more complex SFHs. In particular, we take an 11 Gyr old composite stellar population with an exponentially delayed SF (τ = 0.3 Gyr) as the main SF episode (this age is compatible with the age of the Universe at z ~ 0.15, which is the median redshift of our sample, see Sect. 3). We then define more complex SFHs by combining this single CSP with a burst of SF at different ages (5, 6, 7 Gyr) and with different mass contributions (3, 5, 10 %). In all cases, we consider a solar metallicity for the main SF episode and, according to the results of Maraston et al. (2009), a subsolar metallicity (Z = 0.004) for the later one. We do not mask any spectral feature of the input spectra, we assume AV = 0.1 mag for the two components and apply a velocity dispersion of 200 km s-1. We show the results for a S/N of 80, which matches the typical S/N of the SDSS median stacked spectra analyzed in the following (see Sect. 3). Useful information can be derived from the comparison between the output SFH obtained from these input simulated spectra and the one provided when the single CSP alone is taken as input SFH. Fig. 5 shows that the single CSP alone is well recovered by the full spectrum fitting. In particular, ~80% of the stellar mass is retrieved within ~1 Gyr from the SFH peak. When a burst is added to this major episode of SF, the full-spectrum fitting is able to recognize the presence of a more complex SFH, as indicated by the tail appearing at smaller ages, and the total mass percentage of the later burst is retrieved within 1 Gyr from the expected age. However, we note that the main episode of SF is spread on a time interval longer than expected, and 50% of the stellar mass is retrieved around ~1 Gyr from the SFR peak. We also find that, in this case, the mean properties of the global stellar population are well retrieved, with a percentage accuracy larger than 10% starting from S/N ~ 15 for age, ~7 for metallicity, ~20 for AV, ~8 for σ and that the metallicities of the two SF episodes are separately recovered. These S/Ns are well below those typical of the stacked spectra analyzed in the following sections.

2022MNRAS.509.6091H__Tremmel_et_al._2017_Instance_1

Galactic winds have been ubiquitously observed in galaxies at both low and high redshifts, and they are critical to galaxy formation and evolution. Simulations calibrated to match these observations predict that a large amount of galactic material is ejected as a wind before reaccreting to either form stars or be ejected once again (Oppenheimer et al. 2010; Anglés-Alcázar et al. 2017). Current cosmological hydrodynamic simulations of galaxy formation employ a variety of subgrid models (e.g. Springel & Hernquist 2003; Oppenheimer & Davé 2006; Stinson et al. 2006; Dalla Vecchia & Schaye 2008; Agertz et al. 2013; Schaye et al. 2015; Davé, Thompson & Hopkins 2016; Tremmel et al. 2017; Pillepich et al. 2018; Davé et al. 2019; Huang et al. 2020a) that artificially launch galactic winds, but the results are sensitive to numerical resolution and the exact subgrid model employed (Huang et al. 2019, 2020a). Simulations without these subgrid wind models (e.g. Hopkins et al. 2018; Kim & Ostriker 2015; Martizzi et al. 2016) allow winds to occur ‘naturally’, but these simulations may not resolve the scales necessary to resolve the important known physical processes (Scannapieco & Brüggen 2015; Brüggen & Scannapieco 2016; Schneider & Robertson 2017; McCourt et al. 2018; Huang et al. 2020b). Hence, modelling galactic winds accurately remains a theoretical challenge for even the most refined high-resolution simulations of galaxies (see Naab & Ostriker 2017, for a review). Even if one were able to accurately model the formation of galactic winds, the subsequent propagation in galactic haloes depends on a complicated interplay of many physical processes that occur on a wide range of physical scales that cannot be simultaneously resolved in a single simulation. For example, to robustly model the propagation and disintegration of moving clouds in various situations requires cloud-crushing simulations with at least sub-parsec scale resolution (Schneider & Robertson 2017; McCourt et al. 2018), which is orders of magnitudes below the resolution limits of cosmological simulations. Furthermore, most cosmological hydrodynamic simulations concentrate their resolution in the dense, star-forming regions of galaxies and thus have lower resolution in the circumgalactic medium (CGM, but see Hummels et al. 2019; Mandelker et al. 2019; Peeples et al. 2019; Suresh et al. 2019; van de Voort et al. 2019). To date, cosmological simulations do not include physically motivated subgrid models for galactic wind evolution, which are required to capture these small-scale physical processes.

2022MNRAS.512.4893Z__Toft_et_al._2014_Instance_1

Submillimetre galaxies (SMGs) are ultraluminous dusty star-forming galaxies (SFGs) with the vast majority of radiation energy in the far-infrared (FIR) and submillimetre bands (Smail, Ivison & Blain 1997; Barger et al. 1998; Hughes et al. 1998; Michałowski et al. 2012; Casey, Narayanan & Cooray 2014). They are extreme starbursts with high star formation rate (SFR) of $\sim 10^2\, -\, 10^3$ M⊙ yr−1 and high stellar mass of ∼1010–11 M⊙, located preferentially at z ∼ 2–3 (Chapman et al. 2005; Wardlow et al. 2011; Smolčić et al. 2012; Simpson et al. 2014; Brisbin et al. 2017; Danielson et al. 2017; Michałowski et al. 2017; Smith et al. 2017; Hodge & da Cunha 2020, and references therein). The physical properties of SMGs derived from detailed studies of individual sources and large sky area submillimetre surveys suggest that SMGs represent an early evolutionary phase of all local ellipticals (Smail et al. 2002; Swinbank et al. 2006; Fu et al. 2013; Toft et al. 2014; Ikarashi et al. 2015; Miettinen et al. 2017; An et al. 2019; Gullberg et al. 2019; Dudzevičiūtė et al. 2020; Rennehan et al. 2020). Although SMGs are a rare population (∼400 deg−2 down to S850 = 4 mJy; Simpson et al. 2019; Shim et al. 2020), they contribute a significant fraction (∼20 per cent) of the cosmic SFR density at z > 2 (Bourne et al. 2017; Koprowski et al. 2017; Zavala et al. 2021). Their extreme SFRs are correlated with higher gas fractions compared to normal SFGs at the same epoch (Bothwell et al. 2013; Scoville et al. 2016; Decarli et al. 2016; Tacconi et al. 2018). The large amount gas is thought to be supplied by gas infall via cold streams from surrounding gas reservoirs (Narayanan et al. 2015; Ginolfi et al. 2017). The extreme starbursts of this population are partially triggered by galaxy major mergers (Tacconi et al. 2008; Engel et al. 2010, but see Davé et al. 2010; Narayanan et al. 2015; McAlpine et al. 2019), while a diversity of morphologies unveiled from the rest-frame ultraviolet (UV) and optical imaging indicate galaxy interactions and disc instabilities to be important mechanisms for enhancing star formation in SMGs (Swinbank et al. 2010; Kartaltepe et al. 2012; Chen et al. 2015), as well as AGN activities (Chapman et al. 2005; Wang et al. 2013). Because the rarity and high SFR of SMGs are sensitive to the physical processes governing galaxy formation (e.g. star formation, stellar and AGN feedback, gas infall, metal enrichment, and galaxy merging/interactions), the SMG population is used to constrain cosmological models. It remains challenging to reproduce the SMG population with high SFRs matching observations at high z (Davé et al. 2010; McAlpine et al. 2019; Hayward et al. 2021; Lovell et al. 2021).

2018ApJ...863..162M__Liu_et_al._2013_Instance_3

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.

2021ApJ...915...86A__Owen_&_Sathyaprakash_1999_Instance_1

This analysis searches for a GW signal compatible with the inspiral of a BNS or NSBH binary—collectively NS binaries—within 6 s of data associated with an observed short GRB. This stretch of data is the on-source window and runs from −5 s to +1 s around the start of the GRB emission (i.e., the GRB trigger time). The surrounding ∼30–90 minutes of data are split into 6 s off-source trials which are also analyzed in order to build a background. Around 30 minutes allows the modeled search to accurately estimate the power spectral density of the available instruments and ensures that it can assess at sub-percent level accuracy the significance of any candidate events found in the on-source window. All the data are processed using PyGRB (Harry & Fairhurst 2011; Williamson et al. 2014), a coherent matched filtering pipeline that is part of the general open-source software PyCBC (Nitz et al. 2020) and has core elements in the LALSuite software library (LIGO Scientific Collaboration 2018). We scan each trial of data and the on-source window in the 30–1000 Hz frequency band using a predefined bank of waveform templates (Owen & Sathyaprakash 1999) created with a hybrid geometric–stochastic method (Capano et al. 2016; Dal Canton & Harry 2017) and using a phenomenological inspiral-merger-ringdown waveform model for non-precessing point-particle binaries (IMRPhenomD; Husa et al. 2016; Khan et al. 2016).210 210 All waveforms mentioned in this section are generated with the LALSimulation package that is part of the LALSuite software library (LIGO Scientific Collaboration 2018). The waveform template bank includes waveforms corresponding to a range of masses ([1.0, 2.8]M for NSs, [1.0, 25.0]M for BHs) and dimensionless spin magnitudes ([0, 0.05] for NSs, [0, 0.998] for BHs) for aligned-spin, zero-eccentricity BNS or NSBH systems that may produce an electromagnetic counterpart via the tidal disruption of the NS (Pannarale & Ohme 2014). Aside from the updated sensitivity of our detectors, the only difference with respect to the second LIGO–Virgo observing run (Abbott et al. 2019b) is that the generation of the bank has been updated to apply more accurate physics to determine whether an NSBH system could produce an accretion disk from this disruption (Foucart et al. 2018). We only search for circularly polarized GWs, which may be emitted by binaries with inclinations of 0° or 180°: such systems have GW amplitudes that are consistent (Williamson et al. 2014) with those of binary progenitors with inclination angles over the full range of viewing angles that we expect for typical brightness GRBs (≲30°; Fong et al. 2015), such as those in our sample.

2016ApJ...826L..14W__Goudfrooij_et_al._2015_Instance_1

Figure 1 shows the NGC 419 CMD after field-star decontamination. The cluster has an eMSTO at 20 ≤ V ≤ 22 mag. We adopted the Padova stellar evolution models (PARSEC CMD 2.7, v. 1.2S; Bressan et al. 2012),8 8 http://stev.oapd.inaf.it/cgi-bin/cmd_2.7 for Z = 0.004 (Glatt et al. 2008; Girardi et al. 2009), and fitted the eMSTO extremes with isochrones of log(t yr−1) = 9.12 (1.32 Gyr; blue line) and log(t yr−1) = 9.31 (2.02 Gyr; red line), adopting a distance modulus (m − M)0 = 18.90 mag and a visual extinction AV = 0.181 mag (Rubele et al. 2010). The maximum possible age spread implied by the extent of the eMSTO is ∼700 Myr, similar to the results of Rubele et al. (2010; ∼700 Myr) and Girardi et al. (2013; ∼670 Myr). However, the cluster exhibits an SGB that is broad on the blue side and that becomes significantly narrower on the red side. Previous studies of intermediate-age star clusters with eMSTOs usually showed tight SGBs throughout (Mackey & Broby Nielsen 2007; Li et al. 2014b; Bastian & Niederhofer 2015; but see Goudfrooij et al. 2015). This feature is, however, already apparent in the NGC 419 data before field-star decontamination and is, hence, not caused by our data reduction. In addition, the significance level of our field-star decontamination is very high along the SGB, while the good agreement between our iraf/daophot and dolphot stellar catalogs further confirms the reality of the SGB morphology. Examination of the SGB stars’ spatial distribution reveals that blending is unlikely responsible for the observed narrowing either. The NGC 419 CMD of Goudfrooij et al. (2014) shows a similar trend, although that of Glatt et al. (2008) resembles an SSP more closely. The latter CMD is, however, composed of HST ACS/High Resolution Channel observations covering a small stellar sample located in the cluster’s central region only. Our data are consistent with the Glatt et al. (2008) CMD for stars drawn from inside the cluster’s core radius. We thus conclude that the observed narrowing of the NGC 419 SGB is real and not caused by artifacts associated with our data reduction (see the Appendix).

2022ApJ...926...21B__Käpylä_et_al._2011_Instance_1

Some studies have used the 2.5D mean-field dynamo approach to do so, extending solar mean-field dynamo models to other stellar spectral types (Chabrier & Küker 2006; Jouve et al. 2010; Küker et al. 2011; Kitchatinov et al. 2018, and references therein). While these studies are very helpful, most of them lack the full nonlinearity and genuine parametric dependence of 3D magnetohydrodynamic (MHD) simulations. Recent developments by Pipin (2021) are starting to overcome these limits and have extended the work of Rempel (2006) on the Sun to solar-type stars with various rotation rates. Nevertheless, with the arrival of more powerful supercomputers, other authors have used instead global 3D MHD simulations to model DR and stellar magnetism in the convection zone of solar-like stars (Glatzmaier & Gilman 1982; Miesch et al. 2000, 2006; Brun et al. 2004, 2011; Brown et al. 2008, 2010; Ghizaru et al. 2010; Käpylä et al. 2011, 2014; Gastine et al. 2014; Augustson et al. 2015; Karak et al. 2015). These studies pointed out the large magnetic temporal variability and the critical effect of stellar rotation and mass on magnetic field generation through dynamo mechanism, leading in some parameter regimes to configurations with cyclic activity (Gilman & Miller 1981; Gilman 1983; Glatzmaier 1985a; Brown et al. 2011; Racine et al. 2011; Augustson et al. 2013, 2015; Käpylä et al. 2013; Nelson et al. 2013; Beaudoin et al. 2016; Guerrero et al. 2016, 2019; Strugarek et al. 2017, 2018; Viviani et al. 2018, 2019; Warnecke 2018; Matilsky & Toomre 2020). Several studies pointed out the positive effect of a stable region underneath the convection zone (Parker 1993) on the efficient storage of intense toroidal field and the lengthening of the stellar dynamo cycle period (Glatzmaier 1985b; Browning et al. 2006; Lawson et al. 2015; Beaudoin et al. 2016; Guerrero et al. 2016, 2019; Käpylä et al. 2019; Bice & Toomre 2020). Over the last decade, significant progress has been made in successfully simulating large-scale mean flows and stellar activity cycle using different numerical codes and methods (Jones et al. 2011). This is quite reassuring that a global consensus is growing on the nature of solar-like star dynamos. It is common knowledge that there are still key transitions in Rossby number (at low and high values of this parameter) that need to be understood further, as well as what is the exact type of convective dynamos realized in solar-like stars as their global parameters are varied. This study continues this effort by doing an even broader systematic parametric study of solar-like star dynamos coupled to a stably stratified layer below than what have been published so far. It extends the work published in Varela et al. (2016) and Brun et al. (2017) with the MHD anelastic spherical harmonic code (ASH) (Brun et al. 2004). In particular, we wish to better characterize energy transfers and how much of a star’s energy (luminosity) is converted into magnetic energy by nonlinear global convective dynamos over a wide range of Rossby numbers, generalizing to solar-like stars the work by Starr & Gilman (1966) and Rempel (2006).

2022MNRAS.513.5377F__Blum_et_al._2017_Instance_1

At each heliocentric distance rh, the activity model (Fulle et al. 2020b) is defined by five analytical equations fixing (i) the gas pressure P(s) depending on the depth s from the nucleus surface (Fig. 1 for the CO2 case), (ii) the gas flux Q from the nucleus surface, (iii) the temperature gradient ∇T at depths of a few cm, (iv) the heat conductivity λs at depths of a few cm below the nucleus surface, and (v) the temperature Ts of the nucleus surface (3)$$\begin{eqnarray*} P(s) = P_0 ~f(s) ~\exp \left[{- {T_0 \over {T_s - s ~\nabla T}}}\right] \end{eqnarray*}$$(4)$$\begin{eqnarray*} Q = {{14 ~r ~P(R)} \over {3 ~R}} \sqrt{{2 ~m} \over {\pi k_B ~(T_s - R ~\nabla T)}} \end{eqnarray*}$$(5)$$\begin{eqnarray*} \nabla T = {\sqrt{\Lambda ~Q ~/ ~\sigma _B} \over {8 ~(T_s - R ~\nabla T) ~R}} \end{eqnarray*}$$(6)$$\begin{eqnarray*} \lambda _s = {32 \over 3} ~(T_s - R ~\nabla T)^3 ~\sigma _B ~R \end{eqnarray*}$$(7)$$\begin{eqnarray*} (1 - A) ~I_\odot ~\cos \theta ~r_h^{-2} = \epsilon \sigma _B T_s^4 + \lambda _s ~\nabla T + \Lambda ~Q , \end{eqnarray*}$$where P0, T0, and Λ values are listed in Table 1, s is the depth from the nucleus surface, $f(s) = 1 - (1 - {s \over R})^4$ for s ≤ R, f(s) = 1 elsewhere, r ≈ 50 nm and R ≈ 5 mm are the radii of the grains of which cometary dust consists (Levasseur-Regourd et al. 2018; Güttler et al. 2019; Mannel et al. 2019) and of the pebbles of which cometary nuclei consist (Blum et al. 2017; Fulle et al. 2020b), m is the mass of the gas molecule, kB is the Boltzmann constant, σB is the Stefan–Boltzmann constant, A is the nucleus Bond albedo (e.g. A = 1.2 per cent measured at 67P; Fornasier et al. 2015), I⊙ is the solar flux at the heliocentric distance of Earth, θ is the solar zenithal angle, and ϵ ≈ 0.9 is the nucleus emissivity. Since the gas originates from the superficial pebbles and is assumed to share the temperature Ts − s ∇T of refractories and ices, the thermal diffusion due to gas convection is negligible with respect to the sublimation sink Λ Q. A nucleus is active if the gas pressure overcomes the tensile strength S bonding dust particles to the nucleus surface (Skorov & Blum 2012), thus defining the activity onset for each ice (Table 2), occurring (i) at rh = 85 au for carbon monoxide (Fulle et al. 2020a); (ii) at rh = 60 au for molecular oxygen; (iii) at rh = 52 au for methane; (iv) at rh = 18 au for ethane; (v) at rh = 13 au for carbon dioxide (dotted line in Fig. 1); and (vi) at rh = 3.8 au for water (Fulle et al. 2020b; Ciarniello et al. 2021). The value R ≈ 5 mm has been constrained by several data collected at comet 67P, by laboratory experiments of dust accretion in conditions expected to occur in the solar protoplanetary disc and by observations of other protoplanetary discs (Blum et al. 2017). Other R-values would not provide the best fit of the 67P water-loss time-evolution (Ciarniello et al. 2021).

2016ApJ...826..168X__Bai_2014_Instance_2

MRI is considered to be the most promising mechanism driving angular-momentum transport in protoplanetary disks (Balbus & Hawley 1991; Brandenburg et al. 1995; Hawley et al. 1995; Balbus et al. 1996; Balbus & Hawley 1998). However, protoplanetary disks are cold, dense, and, therefore, poorly ionized. The low level of ionization tends to decouple the disk gas from magnetic fields, which generates non-ideal MHD effects: Ohmic dissipation, ambipolar diffusion (AD), and the Hall effect (e.g., Armitage 2011; Turner et al. 2014). These effects quench MRI in different ways: Ohmic dissipation originates from collisions between electrons and neutrals, AD from collisions between ions and neutrals, and the Hall effect from drift between electrons and ions (Fleming et al. 2000; Sano & Stone 2002; Bai & Stone 2011). Ohmic dissipation operates in high-density regions with weak field, AD dominates in highly ionized and low-density regions, and the Hall effect lies in between (Fleming & Stone 2003; Bai & Stone 2011; Bai 2014). So far, the effect of Ohmic dissipation has been best studied. Investigations show the layered accretion in the inner disk, where the midplane region is “dead” due to low ionization while the surface layer is “active” due to sufficient ionization (Gammie 1996; Jin 1996; Fleming et al. 2000; Fleming & Stone 2003; Turner et al. 2007; Ilgner & Nelson 2008; Oishi & Mac Low 2009; Okuzumi & Hirose 2011). Recent works that take into account both Ohmic dissipation and AD show that AD may render the surface layer and portions of the outer disk inactive (Bai & Stone 2011; Landry et al. 2013; Kalyaan et al. 2015). Bai & Stone (2013) find that MRI is completely suppressed in the inner disk and a strong magnetocentrifugal wind is launched. Three-dimensional simulations that include all three non-ideal MHD effects are also performed (Bai 2014, 2015; Lesur et al. 2014; Simon et al. 2015). In the inner disk, the influence of the Hall effect on midplane angular-momentum transport depends on the orientation of the vertical magnetic field with the disk rotation axis. When the field is aligned with the axis, the enhanced Maxwell stress promotes angular-momentum transport. When the field is anti-aligned with the axis, the midplane remains quiescent. In the outer disk, the Hall effect has little influence on the disk turbulence. Although the inclusion of AD and the Hall effect substantially changes the level of turbulence in the protoplanetary disks, the feature that the viscosity is low in the inner disk and high in the outer disk is still valid. In this study, we assume that gas giant planets form in situ via the core accretion scenario, which implies that their formation locations are always in the low-viscosity region. Since in this study we focus on the relation between photoevaporation and planet formation and gap opening by planets in the disk, we adopt Ohmic dissipation to represent the non-ideal MHD effects on the MRI. We consider that this simplification has little influence on our main calculation results.

2021AandA...653A..36M__Goulding_&_Alexander_(2009)_Instance_1

The SFG sample was constructed using the Great Observatories All-Sky LIRG Survey (GOALS sample, Armus et al. 2009), from which we extracted 158 galaxies, with data from Inami et al. (2013), who report the fine-structure lines at high resolution in the 10 − 36 μm interval, and Stierwalt et al. (2014), who include the detections of the H2 molecular lines and the PAH features at low spectral resolution. For those galaxies in the GOALS sample that have a single IRAS counterpart, but more than one source detected in the emission lines, we have added together the line or feature fluxes of all components, to consistently associate the correct line or feature emission to the total IR luminosity computed from the IRAS fluxes. To also cover lower luminosity galaxies, as the GOALS sample only includes luminous IR galaxies (LIRGs) and ultra-luminous IR galaxies (ULIRGs), we included 38 galaxies from Bernard-Salas et al. (2009) and Goulding & Alexander (2009), to reach the total sample of 196 galaxies with IR line fluxes in the 5.5 − 35 μm interval in which an AGN component is not detected. For the Bernard-Salas et al. (2009), Goulding & Alexander (2009), and the GOALS samples, we excluded all the composite starburst-AGN objects identified as those with a detection of [NeV] either at 14.3 or 24.3 μm. It is worth noting that the original samples from Goulding & Alexander (2009) and Bernard-Salas et al. (2009) have spectra solely covering the central region of the galaxies. To estimate the global SFR, we corrected the published line fluxes of the Spitzer spectra by multiplying them by the ratio of the continuum reported in the IRAS point source catalogue to the continuum measured on the Spitzer spectra extracted from the CASSIS database (Lebouteiller et al. 2015). We assumed here that the line emission scales (at first order) with the IR brightness distribution. In particular, we considered the continuum at 12 μm for the [NeII]12.8 μm and [NeIII]15.6 μm lines, and the continuum at 25 μm for the [OIV]25.9 μm, [FeII]26 μm, [SIII]33.5 μm, and [SiII]34.8 μm lines. This correction was not needed for the AGN sample and the GOALS sample because of the greater average redshift of the galaxies in the 12MGS and GOALS samples. In particular, the 12MGS active galaxy sample has a mean redshift of 0.028 (Rush et al. 1993), while the GOALS sample has a mean redshift of 0.026. The galaxies presented by Bernard-Salas et al. (2009) have instead an average redshift of 0.0074, while the sample by Goulding & Alexander (2009) has an average redshift of 0.0044. For the other lines in the 10 − 36 μm interval, Goulding & Alexander (2009) did not report a detection, and we used the data presented in Bernard-Salas et al. (2009) for a total of 15 objects. Both Bernard-Salas et al. (2009) and Goulding & Alexander (2009) reported data from the high-resolution Spitzer-IRS spectra. Data in the 50 − 205 μm interval were taken from Díaz-Santos et al. (2017). For the GOALS sample, 20 starburst galaxies were taken from Fernández-Ontiveros et al. (2016), and 23 objects were taken from the ISO-LWS observations of Negishi et al. (2001). As a result, we obtained a total sample of 193 objects. Lastly, the PAH features’ fluxes were measured from the low-resolution Spitzer-IRS spectra by Brandl et al. (2006), including 12 objects from the sample of Bernard-Salas et al. (2009) and 179 objects from Stierwalt et al. (2014).

2020MNRAS.492..686L__Shiokawa_et_al._2015_Instance_1

After the disruption phase, the star is tidally stretched into a very long thin stream and the evolution of the stream structure in the transverse and longitudinal directions are decoupled (Kochanek 1994). Thus, the system enters the free-fall phase where each stream segment follows its own geodesic like a test particle (Coughlin et al. 2016). Then, after passing the apocentres of the highly eccentric orbits, the bound debris falls back towards the BH at a rate given by the distribution of specific energy (Evans & Kochanek 1989; Phinney 1989). Due to relativistic apsidal precession, the bound debris, after passing the pericentre, collides violently with the still in-falling stream (see Fig. 1). It has been shown that shocks at the self-intersection point is the main cause of orbital energy dissipation and the subsequent formation of an accretion disc (Rees 1988; Kochanek 1994; Hayasaki, Stone & Loeb 2013; Guillochon, Manukian & Ramirez-Ruiz 2014; Shiokawa et al. 2015; Bonnerot et al. 2016). However, the aftermath of the self-intersection is an extremely complex problem, which depends on the interplay among magnetohydrodynamics, radiation, and general relativity in 3D. No numerical simulations to date have been able to provide a deterministic model for TDEs with realistic star-to-BH mass ratio and high eccentricity (see Stone et al. 2018a, for a review). Many simulations consider either an intermediate-mass BH (e.g. Guillochon et al. 2014, Evans, Laguna & Eracleous 2015; Shiokawa et al. 2015; Sa̧dowski et al. 2016) or the disruption of a low-eccentricity (initially bound) star (e.g. Bonnerot et al. 2016; Hayasaki, Stone & Loeb 2016). It is unclear how to extrapolate the simulation results to realistic configurations and provide an answer to the following questions: How long does it take for the bound gas to form a circular accretion disc (if at all)? How much radiative energy is released from the system? What fraction of the radiation is emitted in the optical, UV, or X-ray bands?

2018ApJ...854..141D__Vegetti_et_al._2014_Instance_1

Our approach shares its transdimensional nature with that of Brewer et al. (2015), which implemented an independent transdimensional framework to infer substructure in strong lens systems. However, in addition to differences concerning substructure modeling and the sampling method used, our code and analysis were developed independently of theirs, and hence provide an important cross-check of the method and results. It has become customary in cosmological analyses to have at least two publicly available independent codes to check for numerical accuracy and find bugs. While both codes are public, we understand that this approach is not yet ubiquitous in the field of galaxy-scale strong lensing (for instance, it has been difficult thus far to reproduce/check the results of Vegetti et al. 2014 and Hezaveh et al. 2016), but we hope that our work can help make substructure lensing more reproducible. Given the uniqueness of the constraints it can provide on low-mass substructures and the impact that these detections can have on dark matter physics, we believe it is extremely important to have multiple cross-checks to validate any results. On a more technical level, our approach differs from that of Brewer et al. (2015) in two major ways: 1. Subhalo lens modeling. We represent subhalos with NFW profiles with a certain mass, scale, and cutoff radius, whereas Brewer et al. (2015) uses so-called blobs, i.e., deflection as a power law in the subhalo-centric radius. We think that choosing a physically motivated basis function set to represent the deflection field due to subhalos is essential for correctly marginalizing them out, i.e., propagating their (rather large) uncertainties to the parameter of interests such as the subhalo mass function. 2. The sampling method and the sampler used to sample from the posterior probability distribution of the (different) lens models given the data. Brewer et al. (2015) uses Diffusive Nested Sampling (DNS) as implemented in RJObject. We use a homebrew reversible-jump MCMC sampler with Metropolis-type within-model proposals.

2016AandA...586A..80O__Fornasier_et_al._2015_Instance_1

Figure 1 shows that in the regions where activity was detected visually, i.e., Hapi, Seth, and Ma’at pits have lower (8–13%/100 nm) spectral slopes than the rest of the comet surface (13–22%/100 nm). In addition to those places, Seth alcoves, the wall of the large Anuket alcove, around the circular features, both clustered and isolated bright features (see Thomas et al. 2015b; Auger et al. 2015; Pommerol et al. 2015b, for definitions) show similar lower spectral slopes than the rest of the surface, even though there was no visual detection of activity features rising from them at the time of the observations used in this study4. This may be because the observing geometry was not suited for their detections during the observations. In the regions we investigated, the Hapi region displays the lowest spectral slopes 8–11%/100 nm (see also Fornasier et al. 2015) together with the isolated bright features (IBFs) and the clustered bright features in the Imhotep region. The locations of the bright features on the Imhotep image (image #4) are shown in Fig. B.4. According to the spectral slope values, the IBFs of Imhotep seem to be more similar to the Hapi region than the active pits of Seth and Ma’at regions. Active pits, alcoves, and the large alcove of Anuket have slope values of typically 10–13%/100 nm. The Ma’at region, which is located on the smaller lobe (head) of the comet, displays higher spectral slope values than the Seth region, which is located on the larger (body) lobe of the comet. In the investigated regions, the highest slope values are detected in the Imhotep region (see Fig. 1d). Here it should be mentioned that the comparison of spectral slopes is performed under the assumption of no spectral reddening between the phase angles of the images we investigated, although the spectral slopes show reddening by phase as presented in Fornasier et al. (2015). Unfortunately, the previous work does not cover all the phase angles of the images we investigated, but the spectral slope variation between 35–54° (Fig. 3 of Fornasier et al. 2015) is small so that we can make this comparison. However, if we follow the linear trend of the phase reddening, for the image taken in 70.45° phase angle (image #4), the spectral slopes would vary from 15%/100 nm to 18%/100 nm in the observations we used.

2021ApJ...906...57S__Mishra-Sharma_et_al._2017_Instance_1

Complementary to studies using the integrated emission and angular power spectrum of DM annihilation from a population of Galactic subhalos, in this paper we present a novel strategy using one-point photon statistics to search for the annihilation signature. Our technique takes advantage of the information in the entire population of sources, including both those that are resolved and those that are faint and unresolved. The concept of leveraging the one-point photon-count distribution to search for DM has previously been studied in Dodelson et al. (2009) and Feyereisen et al. (2015) in the context of emission from extragalactic sources and in Lee et al. (2009) and Koushiappas et al. (2010) with application to Galactic subhalos. We introduce a method to search for signatures of DM annihilation from a Galactic subhalo population using the non-Poissonian template fitting (NPTF) framework (Malyshev & Hogg 2011; Lee et al. 2015, 2016; Mishra-Sharma et al. 2017), which has previously been applied to characterize unresolved point sources in the inner Galaxy (Lee et al. 2016; Linden et al. 2016; Leane & Slatyer 2019, 2020a, 2020b; Chang et al. 2020; Buschmann et al. 2020) and at high latitudes (Zechlin et al. 2016; Lisanti et al. 2016; Zechlin et al. 2018). Using simulations, we show that the NPTF can constrain DM annihilation from a population of subhalos in the face of astrophysical background emission. We find that using photon statistics to look for collective emission from a subhalo population can be especially promising when a large number of individual subhalo candidates are identified in point-source catalogs. This establishes a method complementary to the established ones based on characterizing individual resolved point sources as subhalo candidates, as well as those based on using the measured 0-point (overall flux) and two-point (angular power spectrum) statistics to characterize a subhalo population. Moreover, note that our methodology is completely independent of assumptions about, e.g., the location of stellar overdensities, so we are less sensitive to certain uncertainties which can bias dwarf galaxy constraints such as those in modeling tracer populations. Thus, this framework provides an important comparison for the dwarf galaxy analyses as well.

2020AandA...635A.121M__Matter_et_al._(2016)_Instance_1

Our model consists of four zones; an inner disk (zone 1, as in Matter et al. 2016), and three zones into which the outer disk is divided (zones 2–4) in order to produce the azimuthal asymmetries seen in our SPHERE observations. The radial extent of these zones were constrained from our images: zone 2 corresponds to the component inside ~14 au observed in the noncoronagraphic J-band image, zone 3 corresponds to the bright ring at 16 au, and zone 4 corresponds to the asymmetrical outer disk. The radial extent of each of these zones, as well as their disk masses, is summarized in Table 2. No gaps were introduced between the three outer disk zones, but the gap between zones 1 and 2 from the Matter et al. (2016) model was kept. We note that these radii are only loosely constrained from our data, and are determined by eye from both the coronographic and noncoronographic data. We ran a grid of models with different inclinations and position angles for zones 1–2 (note that we systematically use the same inclination and position angle for these two zones) and for zone 3 – that is, a total offour free parameters, keeping in mind that the relative inclination between components must be small to allow a single broad shadow to be cast (as opposed to two narrow shadow lanes). The inclination and position angle of zone 4 is set to the values provided in Sect. 3. The grid was sampled in steps of 1° for inclination, and 2° for PA, and later refined to 0.5° steps for inclination and 0.5° for PA after a good initial agreement is found between the average azimuthal profile of the model and the scattered light images. The best fitting model was picked not only based on the location of the shadows cast by the inner components on the outer disk, but also on the shape (slope) of the resulting azimuthal profile of the outer disk. There is also a degeneracy if we consider that the inclinations and PAs of the two inner components (zones 1–2 and zone 3) can be exchanged and produce very similar results. However, doing this would cast a shadow in a different location, and thus produce a different azimuthal profile for zone 3.

2017AandA...601A.134M__Fung_&_Dong_(2015)_Instance_1

Several predictions for planet(s) shaping the disk of SAO 206462 have been proposed (Muto et al. 2012; Garufi et al. 2013; Fung & Dong 2015; Bae et al. 2016; van der Marel et al. 2016a. Using linear equations from the spiral density wave theory, Muto et al. (2012) suggested two planets with separations beyond ~50 au by fitting independently the two spiral arms seen in Subaru/HiCIAO data and with masses of ~0.5 MJ by using the amplitude of the spiral wave. Garufi et al. (2013) proposed that one planet of mass 5–13 MJ located inside the cavity at a separation of 17.5–20 au could be responsible for the different cavity sizes measured for the small and large dust grains. Fung & Dong (2015) presented scaling relations between the azimuthal separation of the primary and secondary arms and the planet-to-star mass ratio for a single companion on a circular orbit with a mass between Neptune mass and 16 MJ around a 1 M⊙ star. They predicted with 30% accuracy that a single putative planet responsible for both spiral features of SAO 206462 would have a mass of ~6 MJ. Bae et al. (2016) presented dedicated hydrodynamical simulations of the SAO 206462 disk and proposed that both the bright scattered-light feature (Garufi et al. 2013) and the dust emission peak (Pérez et al. 2014) seen for the southwestern spiral arm result from the interaction of the spiral arm with a vortex, although a vortex alone can account for the S1 brightness peak. They suggested that a 10–15 MJ planet may orbit at 100–120 au from the star. However, ALMA observations at two different frequencies seem to contradict a dust particle trapping scenario by a vortex (Pinilla et al. 2015). Stolker et al. (2016) performed new fitting of the spiral arms observed in SPHERE data and found a best-fit solution with two protoplanets located exterior to the spirals: r1 ~ 168 au, θ1 ~ 52° and r2 ~ 99 au, θ2 ~ 355°. van der Marel et al. (2016a) proposed that the features seen in thermal emission in ALMA data and the scattered-light spiral arms are produced by a single massive giant planet located inside the cavity at a separation of ~30 au. Recently, Dong & Fung (2017) used the contrast of the spiral arms to predict a giant planet of ~5–10 MJ at ~100 au.

2021AandA...650A.205V__Jones_et_al._2021_Instance_2

The question of the evolution of exoplanet systems after the main sequence of their host is generally addressed by studying exoplanets around subgiants, RGB stars, and normal HB (RC) stars (hereafter the ’classical’ evolved stars). These classical evolved stars are typically very large stars, with radii ranging from ~ 5− 10 R⊙ to more than 1000 R⊙. This is much larger than hot subdwarfs, which have radii in the range ~ 0.1−0.3 R⊙ (Heber 2016). Their mass is typically higher than ~ 1.5 M⊙, compared to~0.47 M⊙ for hot subdwarfs. The transit and radial velocity (RV) methods are both challenging for these classical evolved stars because the transit depth is diluted and there are additional noise sources (Van Eylen et al. 2016). Another difficulty forthe question of the fate of exoplanet systems after the RGB phase itself is the difficulty of distinguishing RGB and RC stars based on their spectroscopic parameters alone, which is sometimes hard even with help of asteroseismology (Campante et al. 2019). As a consequence, only large or massive planets are detected around the classical evolved stars (Jones et al. 2021, and references therein). A dearth of close-in giant planets is observed around these evolved stars compared to solar-type main-sequence stars (Sato et al. 2008; Döllinger et al. 2009). This may be caused by planet engulfment by the host star, but current technologies do not allow us to determine whether smaller planets and remnants (such as the dense cores of former giant planets) are present. The lack of close-in giant planets may also be explained by the intrinsically different planetary formation for these intermediate-mass stars (see the discussion in Jones et al. 2021). Ultimately, the very existence of planet remnants may be linked to the ejection of most of the envelope on the RGB that occurs for hot subdwarfs, while for classical evolved stars, nothing stops the in-spiraling planet inside the host star, and in all cases, the planet finally merges with the star, is fully tidally disrupted, or is totally ablated by heating or by the strong stellar wind. In other words, the ejection of the envelope not only enables the detection of small objects as remnants, but most importantly, it may even be the reason for the existence of these remnants by stopping the spiraling-in in the host star.

2022ApJ...939L..19I__Petigura_et_al._2017_Instance_1

Kepler transit observations have shown that planets with sizes between those of Earth (1 R ⊕) and Neptune (∼4 R ⊕) are extremely common (Lissauer et al. 2011; Batalha et al. 2013; Fressin et al. 2013; Howard 2013; Fabrycky et al. 2014; Marcy et al. 2014). Demographics analysis suggests that at least 30%–55% of the Sun-like stars host one or more planets within this size range and with orbital periods shorter than 100 days (Mayor et al. 2011; Howard et al. 2012; Fressin et al. 2013; Petigura et al. 2013; Mulders 2018; Mulders et al. 2018; Zhu et al. 2018; He et al. 2019, 2021). Uncertainties in stellar radius estimates from photometric Kepler observations prevented a detailed assessment of the intrinsic planet size distribution (Fulton et al. 2017; Petigura et al. 2017). Specific trends in planet sizes only started to emerge from the data with more precise determination of stellar radii by follow-up surveys (California Kepler Survey, CKS) and the use of Gaia improved parallaxes (Johnson et al. 2017; Van Eylen et al. 2018; Petigura et al. 2022). These studies showed that the size frequency distribution of planets between ∼1 and ∼4R ⊕ is bimodal with peaks at ∼1.4 R ⊕ and ∼2.4 R ⊕, and a valley at ∼1.8 R ⊕ (Fulton et al. 2017; Fulton & Petigura 2018; Petigura 2020). The best-characterized planets with sizes of about ∼1.4 R ⊕ are consistent with rocky composition, as constrained by their estimated bulk densities (Fortney et al. 2007; Adams et al. 2008; Lopez & Fortney 2014; Weiss & Marcy 2014; Dorn et al. 2015; Wolfgang et al. 2016; Zeng et al. 2016, 2019; Bashi et al. 2017; Chen & Kipping 2017; Otegi et al. 2020). These planets are usually referred to as “super-Earths.” Planets with sizes of about ∼2.4R ⊕ are consistent with the presence of volatiles—which could reflect either rocky cores with H-He-rich atmospheres or ice/water-rich planets (Kuchner 2003; Rogers & Seager 2010; Lopez & Fortney 2014; Zeng et al. 2019; Mousis et al. 2020; Otegi et al. 2020). These planets are commonly referred to as “mini-Neptunes.” For a detailed discussion, see reviews by Bean et al. (2021) and Weiss et al. (2022), and references therein.

2015AandA...584A.103S__Lattimer_&_Swesty_1991_Instance_2

The energy in the inner crust is largely influenced by the properties of the neutron gas and, therefore, the EoS of neutron matter of the different calculations plays an essential role. The NV calculation (Negele & Vautherin 1973) is based on a local energy density functional that closely reproduces the Siemens-Pandharipande EoS of neutron matter (Siemens & Pandharipande 1971) in the low-density regime. The Moskow calculation (Baldo et al. 2007) employs a semi-microscopic energy density functional obtained by combining the phenomenological functional of Fayans et al. (2000) inside the nuclear cluster with a microscopic part calculated in the Brueckner theory with the Argonne v18 potential (Wiringa et al. 1995) to describe the neutron environment in the low-density regime (Baldo et al. 2004). The BBP calculation (Baym et al. 1971a,b) gives the EoS based on the Brueckner calculations for pure neutron matter of Siemens (Siemens & Pandharipande 1971). The LS-Ska (Lattimer & Swesty 1991; Lattimer 2015) and DH-SLy4 (Douchin & Haensel 2001) EoSs were constructed using the Skyrme effective nuclear forces Ska and SLy4, respectively. The SLy4 Skyrme force (Chabanat et al. 1998) was parametrized, among other constraints, to be consistent with the microscopic variational calculation of neutron matter of Wiringa et al. (1988) above the nuclear saturation density. The Shen-TM1 EoS (Shen et al. 1998b,a; Sumiyoshi 2015) was computed using the relativistic mean field parameter set TM1 for the nuclear interaction. The calculations of LS (Lattimer & Swesty 1991; Lattimer 2015) and Shen et al. (Shen et al. 1998b,a; Sumiyoshi 2015) are the two EoS tables in more widespread use for astrophysical simulations. The BSk21 EoS (Pearson et al. 2012; Fantina et al. 2013; Potekhin et al. 2013; Goriely et al. 2010) is based on a Skyrme force with the parameters accurately fitted to the known nuclear masses and constrained, among various physical conditions, to the neutron matter EoS derived within modern many-body approaches which include the contribution of three-body forces.

2019ApJ...885..168O__Thomas_et_al._2004_Instance_2

Tidal heating of Io has been shown to be responsible for its widespread volcanism. The tidal heating rate of Jupiter’s tidally locked moon, , driven by forced eccentricities, e, locked by Europa and Ganymede’s Laplace resonance with Io, is the dominant interior heating source. Similarly, the tidal heating of an exomoon will likely dominate the interior energy budget due to the additional stellar tide. Consequently, the tidal heating rate is orders of magnitude higher than at Io, which for an exo-Io of similar rheological properties ( , Rs = RIo, ρs = ρIo) can be written as (Cassidy et al. 2009; Equations (19) and (20)) 3 where υ = 3 × 10−7 cm3 erg−1, and τs = τp/5 based on the tidal stability criterion discussed in Section 2. For utility, we describe the exo-Io’s tidal efficiency as , which can readily be computed for any three-body system as tabulated in Table 4. The enhanced tidal heating described in Equation (3) will also contribute to the surface temperature T0 = Teq + ΔT0, which is very roughly approximated as 4 where σsb is the Stefan–Boltzmann constant and Teq. At Io, the total neutral volcanic content (SO2, SO, NaCl, KCl, Cl, and dissociation products) ejected to space (Section 4.2.1) by the incident plasma is estimated to be, on average, ∼1000 kg s−1 (e.g., Thomas et al. 2004), varying within an order of magnitude over decades of observations (Burger et al. 2001; Wilson et al. 2002; Thomas et al. 2004). While the source of the dominant gas SO2 is ultimately tidally driven volcanism, the near-surface atmosphere is mostly dominated by the sublimation of SO2 frost (Tsang et al. 2016). By observing the atmospheric evolution of the SO2 column density with heliocentric distance, Tsang et al. (2013) estimated the direct volcanic component to be Nvolc ∼ 6.5 × 1016 cm−2, typically of the total observed SO2 column density. Ingersoll (1989) demonstrated the relative contributions due to both sublimation and volcanic sources in maintaining Io’s atmosphere and established a relationship relating the volcanic source rate to the volcanically supplied atmospheric pressure: 5 This expression also gives the volcanic column density , where g is the acceleration due to gravity. Adopting an observed atmospheric temperature of Tatm = 170 K by Lellouch et al. (2015) corresponding to an atmospheric scale height of H = 12 km, a thermal velocity equal to 150 m s−1, and a sticking coefficient α = 0.5 for the SO2 mass of 64 amu yields a volcanic source rate of ∼ 6.9 × 106 kg s−1 of SO2 integrated over Io’s mass MIo. The average volumetric mixing ratio for NaCl to SO2 at Io is observed to be XNaCl ∼ 3 × 10−3 (Lellouch et al. 2003). This leads to a source rate of ∼ 7.4 × 103 kg s−1 of NaCl, somewhat larger than but reasonably consistent with the direct measurement of the NaCl volcanic source rate of (0.8–3.1) × 103 kg s−1 (Lellouch et al. 2003). From these estimates, we will adopt ∼3 × 103 kg s−1 of Na i as the volcanic source rate for Io.

2019ApJ...887...40W__Willott_et_al._2017_Instance_1

In recent years, the Atacama Large Millimeter/submillimeter Array (ALMA) has carried out comprehensive surveys of the [C II] 158 μm fine structure line in high-z quasars. For example, Decarli et al. (2018) detected [C II] in 85% of 27 optically selected quasars at z > 5.94. This line is an important coolant that traces the ionized and neutral interstellar medium (ISM) and star-forming activities (Herrera-Camus et al. 2015). The detection of [C II] line and dust continuum emission has revealed a wide range of star formation rates, from a few 10 M⊙ yr−1 to ≥1000 M⊙ yr−1 (Decarli et al. 2018; Izumi et al. 2018). The [C II]–FIR luminosity ratios of these quasar hosts range from 10−4 to a few ×10−3 (Maiolino et al. 2005; Wang et al. 2013; Willott et al. 2017; Decarli et al. 2018), following the trend of decreasing [C II]–FIR ratio with increasing FIR luminosities found with the IR luminous star-forming systems (Malhotra et al. 2001; Luhman et al. 2003; Hailey-Dunsheath et al. 2010; Stacey et al. 2010; Graciá-Carpio et al. 2011; Díaz-Santos et al. 2013, 2017; Muñoz & Oh 2016; Smith et al. 2017; Decarli et al. 2018; Gullberg et al. 2018; Rybak et al. 2019). In particular, the quasar hosts with high FIR luminosities of a few 1012 to 1013 L⊙ show low [C II]–FIR ratios similar to that found in the ultraluminous infrared galaxies (ULIRGs) and submillimeter galaxies (SMGs; Luhman et al. 2003; Díaz-Santos et al. 2013, 2017; Wang et al. 2013; Rybak et al. 2019). The [C II]–FIR ratio is suggested to be related to the local conditions of the ISM; e.g., a decrease of [C II]–FIR ratio could be due to a high gas temperature (i.e., T > ΔE/k ∼ 91 K, ΔE is the energy separation of the two levels of the [C II]158 μm transition) where the upper level of the [C II] transition is saturated (Muñoz & Oh 2016), or a high gas surface density (e.g., the typical density in compact ULIRGs and SMGs) where more gas is in the molecular phase (Narayanan & Krumholz 2017). It is interesting to see how the resolved distributions of the line and continuum surface brightnesses and emission ratios in these FIR luminous quasar hosts compare to that in the ULIRGs and SMGs (Smith et al. 2017; Gullberg et al. 2018; Rybak et al. 2019).

2021AandA...650A.172J__Mangum_&_Shirley_2015_Instance_1

In the second, complimentary method, the line profiles were fitted using the least-squares method with the SCIPY routine CURVE_FIT. Toward B335, the fit was allowed to include up to four Gaussian line profiles, one for each potential transition in the blended emission feature. Bounds on the fits were estimated from the HDO data, and other non-blended emission lines in the dataset. The best fit does not include a fourth component, corresponding to either the CH3OCH3 line at 316.7904 GHz or the CH3OD transition at 316.7916 GHz (i.e., only three Gaussian profiles were included in the final fit). The exclusion of a fourth component has limited impact on the Gaussian profile for D2O. The resulting fit is shown the right panel of Fig. 4. The column density is estimated from the fitted line profile by assuming local thermodynamic equilibrium (LTE) (Mangum & Shirley 2015). We adopted an excitation temperature of 220 K, which is derived from the synthetic spectrum; this method results in a D2O column density of 4.0 × 1015 cm−2. The HDO transitions presented in Jensen et al. (2019) give a HDO column density of 1.9 × 1017 cm−2 at the sameexcitation temperature, when a beam filling factor is applied for a source size of 0.′′ 2. This method then results in a D2O/HDO ratio of (2.1 ± 0.6) × 10−2. Evidently, the D2O/HDO ratios toward B335 are somewhat higher when using the Gaussian fits, however both methods yield high D2O/HDO ratios ≳10−2. We note that the HDO column density is different from that presented in Jensen et al. (2019) because a higher excitation temperature of 220 K and a smaller source size are used in this work. Furthermore, the synthetic model includes an optical depth correction and identifies a slight blending of the H $_{2}^{18}$218 O line, increasing the HDO/H2O ratio. Because of this, we consider the column densities presented in this work as more accurate. However, this does not impact the results and discussions of the relative HDO/H2O abundances in Jensen et al. (2019), in which identical excitation temperatures were used for all sources. The differences in the reported HDO/H2O ratios in this work and in Jensen et al. (2019) illustrate the dependence on the methodology and assumptions, however, the observed dichotomy between isolated and clustered protostars reported in that paper is robust. To remove the observed dichotomy requires consistently adopting lower excitation temperatures for the isolated sources, independent of the luminosity of the respective sources.

2016ApJ...821..107G__Schwadron_et_al._2011_Instance_2

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.

2016AandA...588A..44Y__Jones_et_al._2014_Instance_2

The second issue concerns the fact that inside a given region, coreshine is not detected in all the dense clouds observed by Paladini (2014) and Lefèvre et al. (2014) and that the proportion of clouds exhibiting coreshine varies from one region to another. For instance, 75% of the dense clouds detected in Taurus exhibit coreshine, whereas in most other regions the proportion is closer to 50% (such as Cepheus, Chamaeleon, and Musca)5. On the contrary, there are for instance very few detections in the Orion region. In THEMIS, most of the scattering efficiency originates in the accretion of an a-C:H mantle. This leads to three possible explanations for the absence of detectable coreshine. The first explanation is related to the amount of carbon available in the gas phase. The abundance used by Köhler et al. (2015) relies on the highest C depletion measurements made by Parvathi et al. (2012) towards regions with \hbox{$N_{\rm H} \geqslant 2 \times 10^{21}$}NH⩾ 2 × 1021 H/cm2. Parvathi et al. (2012) highlighted the variability in the carbon depletion in dust depending on the line of sight. Thus, there may be clouds were the amount of carbon available for a-C:H mantle formation is smaller or even close to zero: such regions would be populated with aggregates with a thinner H-rich carbon mantle or no second mantle at all and thus exhibit very little or no coreshine emission. A second explanation is related to the stability of H-rich carbon in the ISM, which depends strongly on the radiation field intensity to local density ratio (Godard et al. 2011; Jones et al. 2014). In low-density regions (according to Jones et al. 2014, \hbox{$A_{V} \leqslant 0.7$}AV⩽ 0.7 for the standard ISRF), UV photons are responsible for causing the photo-dissociation of CH bonds, a-C:H → a-C. In transition regions from diffuse ISM to dense clouds (Jones et al. 2014, \hbox{$0.7 \leqslant A_{V} \leqslant 1.2$}0.7 ⩽ AV⩽ 1.2 for the standard ISRF), better shielded from UV photons and where the amount of hydrogen is significantly higher, H-poor carbon can be transformed into H-rich carbon through H atom incorporation, a-C → a-C:H. Similarly, carbon accreted from the gas phase in these transition regions is likely to be and stay H-rich. Then, in the dense molecular clouds, most of the hydrogen is in molecular form and thus not available to produce a-C:H mantles on the grains. However, this approximately matches the density at which ice mantles start to accrete on the grains, which would partly protect a-C:H layers that had formed earlier (Godard et al. 2011, and references therein). The stability and hydrogenation degree of a-C:H, as well as the exact values of AV thresholds, are both dependent on the timescale and UV field intensity. The resulting a-C ↔ a-C:H delicate balance could explain why in a quiet region such as Taurus most of the clouds exhibit coreshine, whereas in Orion, where on average the radiation field intensity and hardness are much higher, most clouds do not. A third explanation is related to the age and/or density of the clouds. In a young cloud, where dust growth is not advanced, or in an intermediate density cloud (ρC ~ a few 103 H/cm3), the dust population may be dominated by CMM grains instead of AMM(I) dust. Such clouds would be as bright in the IRAC 8 μm band as in the two IRAC bands at 3.6 and 4.5 μm, thus not matching the selection criteria defined by Pagani et al. (2010) and Lefèvre et al. (2014) and would be classified as “no coreshine" clouds.

2021AandA...654A..80S__Leclercq_et_al._2017_Instance_1

For this comparison we rely on HST broad-band magnitudes, Lyα line fluxes, Lyα EW0 estimates, spectral UV slopes β, continuum magnitudes, and Lyα FWHM from the catalog that will be presented by Kerutt et al. (2021). This LAE study is based on the same data and source identification (see Sect. 3) as the current study, but focuses on properties of the emanating Lyα emission. The Lyα emission fluxes correspond to the measured flux within 3D apertures of three Kron (1980) radii as measured by LSDCat when detecting sources in the MUSE data cubes. We use these Lyα fluxes as opposed to obtaining them directly from the TDOSE spectra, as the TDOSE extractions are based on the assumption that the morphological extent of the line emission follows the continuum morphology of the modeled HST images (Sect. 4 and Schmidt et al. 2019). However, Lyα emission is known to be extended beyond the continuum (Steidel et al. 2011; Momose et al. 2014; Wisotzki et al. 2016, 2018; Leclercq et al. 2017, 2020) and fluxes based on the TDOSE spectra would therefore be biased. The LSDCat Kron radii fluxes therefore better represents the actual Lyα flux emitted by the LAEs. As for the secondary UV emission lines, the EW0(Lyα) values were calculated by comparing the fluxes to the continuum flux densities estimated from a continuum represented by a power law fλ ∝ λβ. To obtain the spectral slope, Kerutt et al. (2021) first determined the magnitudes from available ancillary broad-band HST photometry, by fitting the rest-frame UV morphology for each of the LAEs using GALFIT (Peng et al. 2010, 2002). This provided morphological parameters for all LAEs including a measure of their effective radius. The estimated absolute UV magnitude at 1500 Å is also based on these GALFIT models. The spectral slope β was then obtained from fitting the continuum power law to the GALFIT-based HST magnitudes. To avoid large scatter in the EW0 measurements presented and analyzed by Kerutt et al. (2021), the EWs are based on the median β for the full LAE sample of β = −1.97 similar to what was done for the secondary UV emission lines presented here. The FWHM of the Lyα emission was measured for each source by fitting a skewed Gaussian profile (Eq. (2) by Shibuya et al. 2014) to the Lyα line profiles in 1D spectral extractions weighted by the MUSE PSF to maximize S/N. These fits also provide a Lyα redshift which is more precise than the lead line redshits provided by the LSDCat source identification. We therefore use these redshifts for the analysis of the Lyα velocity offsets described in Sect. 8. Finally, Kerutt et al. (2021) provide estimates of the systemic redshifts based on the FWHM and peak separation between any double-peaked LAEs (identified through visual inspection of the 1D spectra) in the sample based on the empirical relations presented by Verhamme et al. (2018). We note that a handful of the z > 2.9 objects studied here are not included in the Kerutt et al. (2021) catalog, as their selection was based only on non-AGN objects with leading Lyα emission based on the LSDCat selections (Sect. 3). Hence, for z > 2.9, the objects with IDs 121033078, 601381485, 720470421, 722551008, 722731033, and 723311101 are not included in the LAE parameter comparisons in the following. For further details and for the full value-added catalog of Lyα-related quantities we refer to Kerutt et al. (2021).

2022AandA...661A.129S__Rodríguez-Almeida_et_al._2021_Instance_1

Radio astronomy is recognized as one of the most effective techniques to search for interstellar molecules. By comparing the spectra of candidate molecules in the laboratory with the spectra observed in astronomical surveys, we can determine whether these molecules exist in interstellar space. Therefore, it is necessary to provide rotational spectra of candidates for astronomical detection. Radio astronomy has helped to detect several sulfur-containing molecules in the ISM in recent years: in particular, thiols, the sulfur analogs of alcohols. Methanethiol (or methyl mercaptan, CH3SH) was detected in the Sagittarius B2 (Sgr B2) region of the center of our Galaxy (Linke et al. 1979; Gibb et al. 2000; Müller et al. 2016; Rodríguez-Almeida et al. 2021) and in the protostar IRAS 16293-2422 (Majumdar et al. 2016). Two groups reported to have detected several signs of ethanethiol (C2H5SH) in Sgr B2 (Müller et al. 2016) and Orion (Kolesniková et al. 2014). Most recently, Rodriguez-Almeida reported the first unambiguous detection of ethanethiol in the ISM, toward the G+0.603-0.027 molecular cloud (Rodríguez-Almeida et al. 2021). Moreover, several sulfur-containing species have been observed in comets (Altwegg et al. 2017). Some recent efforts, both from spectroscopy and astronomical searches, to detect S-stitutes of other classes of compounds have also been reported. For instance, thioformic acid (HC(O)SH) was very recently detected in G+0.693–0.027. Its trans-isomer has an abundance of ~1 × 10–10 (Rodríguez-Almeida et al. 2021). Conversely, thioformamide (NH2CHS), the counterpart of for-mamide (NH2CHO), was characterized in the laboratory up to 660 GHz, and its transitions were searched for toward the hot cores Sgr B2(N1S) and Sgr B2(N2), but it was not detected (Motiyenko et al. 2020). The rotational spectrum of thioac-etamide was recently analyzed in the 59.6–110.0 GHz frequency region (5.03–2.72 mm). Its emission was searched for in regions associated with star formation using the IRAM 30 m ASAI observations toward the prestellar core L1544 and the outflow shock L1157–B1. The molecule was not detected, but the study allowed placing constraints on the thioacetamide abundances (Maris et al. 2019; Remijan et al. 2022).

2022ApJ...928....3A__Wedemeyer_&_Steiner_2014_Instance_1

Solar vortex tubes can be spontaneously generated by turbulent convection. In simulations of quiet Sun regions, vortices are found along intergranular lanes (Shelyag et al. 2011a; Kitiashvili et al. 2012; Moll et al. 2012; Silva et al. 2020). These structures have an average lifetime of around 80 s (Silva et al. 2021) and a radius between 40 and 80 km (Shelyag et al. 2013; Silva et al. 2020). Solar kinetic vortex tubes (Silva et al. 2021) act as a sink for magnetic field, creating magnetic flux tubes that expand with height (Kitiashvili et al. 2012; Moll et al. 2012; Silva et al. 2020). The concentration of magnetic flux leads to a high magnetic field tension, which can prevent the magnetic field lines from being twisted by the rotational motion (Shelyag et al. 2011b; Moll et al. 2012; Nelson et al. 2013; Silva et al. 2021). In some cases, twisted magnetic flux tubes appear close enough to flow vortices, leading to magnetic and kinetic vortex structures closely coexisting in regions with high plasma-β (Wedemeyer & Steiner 2014; Rappazzo et al. 2019; Silva et al. 2021). The vortical motions can still trigger perturbations along magnetic lines that could lead to wave excitation, e.g., Battaglia et al. (2021). The vorticity evolution in the magnetized solar atmosphere is mainly ruled by the magnetic field, which also influences the general shape of vortices (Shelyag et al. 2011a). Based on the analysis of swirling strength, the part of the vorticity only linked to swirling motion (Shelyag et al. 2011b; Canivete Cuissa & Steiner 2020) showed that the magnetic terms in the swirling equation evolution tend to cancel the hydrodynamic terms close to the solar surface, whereas the magnetic terms dominate alone the production of swirling motion in the chromosphere. The magnetic field also tends to play an important role in the plasma dynamics along the whole vortex tube, as the Lorentz force has a magnitude comparable to the pressure gradient (Silva et al. 2020; Kitiashvili et al. 2013). High-speed flow jets have also been linked to simulated vortex tubes, driven by high-pressure gradients close to the photosphere and by Lorentz force in the weakly magnetized upper solar photosphere (Kitiashvili et al. 2013). In general, the averaged radial profile of magnetic field, angular velocity, pressure gradient inside of the vortex tube at the lower chromosphere and photosphere levels show similar behavior (Silva et al. 2020).

2021AandA...649A..58L__Bemporad_et_al._(2018)_Instance_2

The leading edges of the transients normally leave bright traces in the images of visible light, inspiring many methods that were developed to derive their locations and velocities, such as the icecream cone model (Fisher & Munro 1984), the graduated cylindrical shell (GCS) model (Thernisien 2011), geometric triangulation methods (Liu et al. 2010), mask-fitting methods (Feng et al. 2012), and trace-fitting methods including the point-p, fixed-Φ, harmonic mean, and self-similar expansion fitting methods (e.g., Sheeley et al. 1999; Howard et al. 2006; Davies et al. 2012; Möstl & Davies 2013). To derive the velocity distribution inside one transient rather than only at its leading edge, some other techniques have been proposed. Colaninno & Vourlidas (2006) applied an optical flow tool to extract the velocity vector of a coronal mass ejection (CME) in digital images. Feng et al. (2015) derived the radial velocity profiles of the whole CME from the spatial distribution of its density given by the mass continuum equation. A cross-correlation method was applied to derive continuous 2D speed maps of a CME from coronagraphic images by Bemporad et al. (2018). In their work, the radial shift pixel by pixel is determined by maximizing the cross correlation between the signal in a radial window at one frame and the signal in a radial shifted window at the previous frame, and the radial speed just equals the radial shift over the time interval between the two frames. Ying et al. (2019) improved this cross-correlation method by analyzing data in three steps: forward step (FS), backward step (BS), and average step (AS). In the FS (BS), the 2D velocity map between the current and the previous (next) frame is constructed with almost the same method as Bemporad et al. (2018). In the AS the average, velocity is obtained from the FS and BS. The velocities derived by all these methods are the component of the flow velocity vector projected onto the POS. This may underestimate the velocity especially for transients that do not propagate in the POS. Methods such as the polarizaition ratio technique (Moran & Davila 2004; DeForest et al. 2017) or the local correlation tracking (LCT) method (Mierla et al. 2009) can derive the 3D geometric information of the whole transients, but not the velocity distribution. Bemporad et al. (2018) chose the propagating direction averaged over the whole CME derived by the polarization ratio technique to correct the radial speed in the 2D maps, but the key information along the LOS is still lacking.

2021ApJ...909..172Z__Read_&_Lebonnois_2018_Instance_1

Atmospheric superrotation is characterized by eastward wind at the equator, which means the atmosphere there has a higher angular momentum than the solid surface. Atmospheric superrotation is a common phenomenon across the universe. In the solar system, superrotation exists in the atmospheres of Venus, Titan, Saturn, and Jupiter, as well as the stratospheric atmosphere of Earth during the westerly phase of the quasi-biennial oscillation (e.g., Kraucunas & Hartmann 2005; Schneider & Liu 2009; Lutsko 2018; Read & Lebonnois 2018). In order to maintain atmospheric superrotation, there must be momentum transports from higher latitudes to the equator against friction or other processes, according to angular momentum conservation (Hide 1969; Held 1999; Showman et al. 2013). This up-gradient transport into the jet can result from Rossby waves, coupled Rossby–Kelvin waves, mixed Rossby–gravity waves, wave–jet resonance, barotropic instability, or baroclinic instability (Suarez & Duffy 1992; Del Genio & Zhou 1996; Joshi et al. 1997; Lee 1999; Williams 2003; Kraucunas & Hartmann 2005; Schneider & Liu 2009; Caballero & Huber 2010; Mitchell & Vallis 2010; Showman & Polvani 2010, 2011; Showman et al. 2010; Liu & Schneider 2011; Arnold et al. 2012; Pinto & Mitchell 2014; Tsai et al. 2014; Wang & Mitchell 2014; Laraia & Schneider 2015; Lutsko 2018; Read & Lebonnois 2018; Pierrehumbert & Hammond 2019). For example, Kraucunas & Hartmann (2005) suggested that in an Earth-like atmosphere, equatorial superrotation can be generated by equatorward stationary eddy momentum convergence, which is associated with zonal variations in the diabatic heating at low latitudes. Mitchell & Vallis (2010) studied the transition from current Earth-like atmospheric circulation to an equatorial superrotation state. They found that during the spin-up period, superrotation is generated by equatorward momentum convergence associated with both barotropic and baroclinic instabilities.

2019ApJ...885...81S__Dekel_et_al._2009b_Instance_1

This scenario could also explain the reason why star-forming galaxies with a relatively thin disk appeared around z ∼ 1, because the hot-mode accretion is expected not to dominate even in massive halos at z ≳ 2 owing to gas supply through the cold gas stream (e.g., Dekel et al. 2009a; Kereš et al. 2009). Some of the gas accreting onto a dark matter halo is expected to penetrate surrounding hot gas in a form of filaments of dense and cold infalling gas and directly accrete onto the central galaxy at such high redshift, where the mass accretion rate and matter density tend to be high. Such direct gas supply through the filaments of cold gas could make the gas disk of the central galaxy more turbulent and gravitationally unstable, which leads to thick and clumpy stellar disk and bulge formation/growth in some cases (e.g., Dekel et al. 2009b; Ceverino et al. 2010; Dekel & Burkert 2014). Thus, it seems to be difficult to form the thin stellar disks even in massive halos at z ≳ 2. After the hot-mode accretion starts to dominate at z ∼ 2 in relatively massive halos, the thin stellar disks may be gradually formed from thinner gas disks and appear around z ∼ 1. If some of these star-forming galaxies with a thin stellar disk quench and evolve into passively evolving galaxies without a violent morphological change as discussed in the previous section, it is understood that the fraction of passively evolving galaxies with a thin shape increases with time at z 1 rather than higher redshifts. Since more massive star-forming galaxies have a sufficient time to form a thin disk through the hot-mode accretion in earlier epochs in this scenario (Noguchi 2019), passively evolving galaxies with a thin shape may also be provided preferentially in more massive galaxy populations at z ∼ 1. This could explain our result that passively evolving galaxies with Mstar = 1010.5–1011 M⊙ already show a thinner shape than those with 1010–1010.5 M⊙ at 0.6 z 1.0. In fact, Bezanson et al. (2018) reported that ∼64% of quiescent galaxies with Mstar ∼ 1010.5–1011 M⊙ at 0.6 z 1.0 show a significant rotation, while those massive galaxies with Mstar > 1011 M⊙ show no or little rotation in the LEGA-C survey. Such quiescent galaxies with a significant rotation might be recently quenched galaxies with a relatively thin disk that has grown through the hot-mode gas accretion since z ≲ 2.

2022MNRAS.514.2974M__the_2003_Instance_1

NGC 2992 has been observed at different flux levels (e.g. Fig. 14) and in Marinucci et al. (2018) a detailed analysis of XMM–Newton exposures of this object is presented. In order to provide an holistic view of the spectral properties of the central engine in NGC 2992, we tested our 2019 best-fitting model (model 1 presented in Section 4.3) on the exposures where the source was found in its lowest and highest states. In particular, we considered the XMM–Newton archival observations taken on 2003-05-19 and 2010-11-28 (Obs.IDs. 01479203014 and 0654910901, respectively). We thus reproduced them directly adopting the best-fitting model found for orbit 2 and show it in Fig. 12. We accounted for the different flux states computing the normalization of the primary emission, its associated reflected component, and the one of the apec and Cloudy tables. All the other parameters have been kept fixed to their corresponding best-fitting values already quoted in Table 5. Moreover, to account for the broad emission line found required by the 2003 data, we added a Gaussian component whose width was kept fixed to σ = 400 eV, in accordance with what literature papers (e.g. Nandra et al. 1997; Shu et al. 2010). This basic procedure led us to the fits shown in Fig. 15, with statistics of χ2/d.o.f. = 218/170 and χ2/d.o.f. = 181/140. In the high flux level, (F2–10 keV = (9.5 ± 0.1) × 10−11 erg cm−2 s−1), the normalization of the power law is Normpo = (3.00 ± 0.01) × 10−2 ph. keV−1 cm2 s−1, about twice of what found in 2019 while the amount of reflected flux is fully consistent with what found in 2019 as we obtained NormMyTorus = (9.7 ± 3.7) × 10−2 ph. keV−1 cm2 s−1. On the other hand, in the 2010 low flux level exposure (F2–10 keV = (2.9 ± 0.2) × 10−12 erg cm−2 s−1), we found the power-law normalization to be Normpo = (4.7 ± 0.2) × 10−4 ph. keV−1 cm2 s−1 about 20–30 times lower than in 2019. The normalization of the reflected component modelled using MyTorus is NormMyTorus = (9.5 ± 1.2) × 10−3 ph. keV−1 $\rm cm^{2}$ s−1, a factor of ∼10 less than in 2019. Therefore, on very long time-scales and during a prolonged low state of the source in 2010, the strength of the reflector appears to respond to the continuum. However, the smaller value of the reflected component found in 2010 can be explained by the torus reflecting the primary continuum of NGC 2992 during a low flux state. Observing the light curves in Fig. 1, before 2010 NGC 2992 was observed in a very low flux state, even lower than the one in 2021. Such a long-term adjustment suggests that reflected spectrum emerges far from the central engine. Finally, in accordance with previous studies, the Fe Kα emission line of NGC 2992 has an unresolved component correlating with the primary flux and emerging from the broad line region. However, the current MyTorus-based model accounts for whole Fe Kα flux (see residuals in Fig. 15) and data do not require any additional Gaussian component. Below 1 keV, the non-variable behaviour of the distant scattering off the NLR can be witnessed in the top panels of Fig. 7 or in the first bin of the excess variance spectra in both Figs 6 and 8.

2019MNRAS.484.3356G__Häring-Neumayer_et_al._2006_Instance_1

Optical-NIR Spectral Energy Distribution (SED) modelling of diffuse light (e.g. Carson et al. 2015; Dale et al. 2016) at high spatial resolution can provide a map of the spatial variation and composition of the main stellar population components of the NSC, as well as their host galaxy. Such analysis can also unveil (an additional) contribution to the optical-NIR light from accretion on to an obscured nuclear MBH and/or nuclear star formation activity (e.g. Noll et al. 2009; Drouart et al. 2016). To understand NSC formation, it is therefore critical to be able to disentangle such degeneracies. For example, follow-up spectroscopic observations aiming at decomposing the main stellar populations and the dynamical imprint of a MBH rely on a good mass model to predict the stellar population velocity profile (e.g. Häring-Neumayer et al. 2006; Seth et al. 2010; Neumayer et al. 2011; Neumayer & Walcher 2012; Nguyen et al. 2018). Observations of NSCs have shown that although they contain young stellar populations and extended star formation histories (SFHs), the most dominant one by mass is the oldest (≳3 Gyr), where more than 50 per cent of the mass of the cluster has formed (e.g. Walcher et al. 2006; Kacharov et al. 2018, from spectral modelling in the optical). Therefore, characterizing the spatial structure of NSCs in the NIR is of particular importance, because that is where most of the stellar light of the old stellar population is emitted that allows us to trace most of the gravitating mass. Therefore, characterizing NSCs in the NIR can provide additional constraints as to which of the leading NSC formation scenarios had a major role in the formation of particular NSC. However, to be able to achieve this for a larger sample and of more distant galaxies, efficient high spatial resolution observations are required, such as with the presented here wide-field ground-layer AO NIR observations in the NIR with ARGOS at the LBT.

2021MNRAS.508.3111M__Garton,_Gallagher_&_Murray_2018_Instance_1

The initially used automated methods for detection of solar activity in the solar images are based on the predefined rules inferred from the appearance and usual characteristics of the structures in the solar atmosphere (Henney & Harvey 2005; Krista & Gallagher 2009; Pérez-Suárez et al. 2011). However, it is not possible to capture all the unique solar structures using generally defined rules. Therefore, methods with more advanced mathematical algorithms were introduced. The Spatial Possibilistic Clustering Algorithm (SPoCA) (Barra et al. 2009; Verbeeck et al. 2014) or Coronal Hole Identification via Multi-thermal Emission Recognition Algorithm (CHIMERA) (Garton, Gallagher & Murray 2018) have been found to be very effective and are widely used in online solar data visualization tools1$^,$ .2 The SPoCA also provides entries for catalogues of coronal holes and active regions within the Heliophysics Events Knowledgebase (HEK) (Hurlburt et al. 2012) that is commonly used in the SolarSoft (Freeland & Handy 1998) and SunPy (The SunPy Community et al. 2020) frameworks. As will be presented later, these algorithms still have limitations for the precise segmentation of structures in the solar corona. Due to the advances in computer vision in recent years, approaches based on machine-learning techniques are able to extend the standard methods (Aschwanden 2010). Conventional machine-learning techniques as support vector machine (SVM), Decision Tree, or Random Forest could improve the detection of coronal holes as they provide automated distinguishing from filaments in EUV solar images (Reiss et al. 2015; Delouille et al. 2018). On the assumption that the techniques based on convolutional neural networks (CNN) are the most convenient techniques for image segmentation tasks (Lecun, Bengio & Hinton 2015), a new era in automated processing of solar images has begun. Illarionov & Tlatov (2018) demonstrated that for the segmentation of coronal holes, CNN provides quantitatively comparable results as the SPoCA and CHIMERA algorithms. Jarolim et al. (2021) provided a method called CHRONOS based on CNN with progressively growing network approach for robust segmentation of coronal holes.

2021MNRAS.506.5129B__Momose_et_al._2014_Instance_1

More recently, Lyman α haloes (LAHs) have been discovered around star-forming galaxies that show Ly α emission far beyond the galaxies’ optical bodies, tracing the circumgalactic rather than interstellar gas (e.g. Hayes et al. 2013). While LAHs are fainter and smaller than LABs in their Ly α extent, they might be a generic feature around Ly α emitting galaxies. Narrow-band imaging can efficiently detect LAHs at targeted redshifts through stacking (Hayashino et al. 2004; Steidel et al. 2011; Matsuda et al. 2012; Feldmeier et al. 2013), and narrow-band surveys enable ultradeep, blind samples of LAHs around distant galaxies (Momose et al. 2014, 2016; Kakuma et al. 2019). In the last years modern surveys performed with integral field unit (IFU) spectrographs on 10 m-class telescopes, such as the Multi Unit Spectroscopic Explorer (MUSE) and the Keck Cosmic Web Imager (KCWI), take place. These new instruments allow the study of individual, faint LAHs opposed to previous narrow-band stacks. Along with the IFUs’ spectral resolution these recent surveys largely increase the information available from LAH observations. Hundreds of individually extended Lyman α haloes at z ≳ 2 have been revealed since (Wisotzki et al. 2016). Many of these are specifically targeted samples which focus on bright quasars (Borisova et al. 2016; Cai et al. 2019; Guo et al. 2020; O’Sullivan et al. 2020), based on strong earlier evidence of enhanced Ly α emission around active galactic nuclei (AGNs Cantalupo et al. 2014; Arrigoni Battaia et al. 2016; Arrigoni Battaia et al. 2019; Farina et al. 2019). Others exploit the large field of view of MUSE, in particular, to conduct blind surveys for LAHs around more typical, generally star-forming galaxies (Leclercq et al. 2017; Wisotzki et al. 2018; Leclercq et al. 2020). At the same time, follow-up with other instruments such as ALMA reveals complementary views on other gas phases within LAHs including CO (Emonts et al. 2019).

2021MNRAS.508.3499V__Takahashi,_Witti_&_Janka_1994_Instance_1

The astrophysical environments of r-process events in the cosmos usually involve explosive physical conditions, because very high neutron densities are required. The neutron-rich isotopes produced along the r-process path have a very short half-life, causing the neutron-rich nuclei to β-decay over time-scales of the order of milliseconds, if they did not undergo further rapid neutron capture. Examples of r-process sites that have been proposed and investigated by theoretical studies include (i) neutrino-driven winds from proto-neutron stars (NSs; Takahashi, Witti & Janka 1994; Woosley et al. 1994; Qian & Woosley 1996; Hoffman, Woosley & Qian 1997; Otsuki et al. 2000; Thompson, Burrows & Meyer 2001; Wanajo et al. 2009; Hansen et al. 2013; Wanajo 2013); (ii) neutrino-driven winds from highly magnetic and potentially rapidly rotating proto-magnetars (Thompson 2003; Thompson, Chang & Quataert 2004; Metzger, Thompson & Quataert 2007, 2008; Vlasov, Metzger & Thompson 2014; Vlasov et al. 2017; Thompson & ud-Doula 2018); (iii) neutrino-driven winds around the accretion disc of a black hole (Pruet, Thompson & Hoffman 2004; Metzger et al. 2008; Wanajo & Janka 2012; Siegel, Barnes & Metzger 2019); (iv) electron-capture SNe (see e.g. Wanajo et al. 2009; Cescutti et al. 2013; Kobayashi, Karakas & Lugaro 2020); (v) magneto-rotationally driven SNe (Burrows et al. 2007; Winteler et al. 2012; Cescutti & Chiappini 2014; Mösta et al. 2014, 2015, 2018; Nishimura, Takiwaki & Thielemann 2015; Nishimura et al. 2017; Halevi & Mösta 2018; Reichert et al. 2021); and (vi) neutron-star mergers (Lattimer et al. 1977; Freiburghaus, Rosswog & Thielemann 1999; Argast et al. 2004; Goriely, Bauswein & Janka 2011; Rosswog 2013; Matteucci et al. 2014; Cescutti et al. 2015; Vincenzo et al. 2015; Kobayashi et al. 2020). Since it is likely that all these mechanisms have contributed to r-process nucleosynthesis at some level, the theoretical studies have focused on exploring the frequency of each event and the predicted template of the corresponding r-process ejecta.

2018ApJ...861...77M__Pillai_2017_Instance_1

Previous Herschel studies confirmed numerical calculations (e.g., Nagai et al. 1998) showing that a parallel orientation between filaments and the local magnetic field is to be expected for filaments having low column densities (e.g., Peretto et al. 2012; Busquet et al. 2013; Palmeirim et al. 2013; André et al. 2014; Cox et al. 2016; Panopoulou et al. 2016). In fact, a variety of orientations have previously been observed, and the alignment of filaments and apparent field direction appear to vary between regions and for different gas densities (cf., Pillai 2017, for a review). For example, there is observational evidence that low-column density filaments, or “striations,” are aligned parallel to the magnetic field lines in Taurus, as they are more susceptible to the magnetic influence than higher column density structures (Palmeirim et al. 2013). These striations may therefore promote mass accretion onto larger filaments, in which stars are finally formed. Such studies, however, were mostly performed using lower-resolution dust column density maps obtained with Herschel (e.g., Li et al. 2013; Palmeirim et al. 2013; Soler et al. 2017) or polarization data tracing relatively low AV environments (e.g., Busquet et al. 2013; Fissel et al. 2016; Planck Collaboration Int. XXXV 2016; Soler et al. 2016; Jow et al. 2018). Nevertheless, as discussed in Section 4.3, the filament F2 studied in this work has molecular hydrogen column densities roughly 2 to 3 orders of magnitude larger than the Herschel filaments studied in Cygnus or Taurus, and may therefore possibly be self-gravitating. At face value, these results are at odds with the widely accepted paradigm of magnetically regulated star formation in filaments, in which a perpendicular orientation of dense filaments with respect to the magnetic field is expected (e.g., Peretto et al. 2012; Busquet et al. 2013; Palmeirim et al. 2013; André et al. 2014; Cox et al. 2016; Panopoulou et al. 2016).

2022ApJ...927...61K__SN_2011f_Instance_1

Statistical investigations of a large sample of photometric data are useful in exploring the bulk properties of various types of events. Nevertheless, such studies are generally limited to LCs in selected passbands, and also the follow-up covers a short duration of the SN evolution. Further, the spectroscopic follow-up of most objects is restricted to early phases. Nonuniformity in the data sample may also be present, as these are collected at different observing facilities and detectors. Studying individual events with proper monitoring at different phases (both photospheric and nebular) is hence extremely important. The very early phase observations (hours to days after explosion) of these events are useful to constrain the progenitor radius at its end stage. This needs a very early detection and quick follow-up, which is not always possible considering their random occurrence in the sky. The large-area surveys with high cadence have contributed significantly in this regard. SN 1993J (Type IIb) is the first event among SE-SNe that shows evidence of a prominent cooling tail just after explosion (Richmond et al. 1994; Barbon et al. 1995). During the past two decades, several Type IIb SNe with such interesting features have been monitored and studied well, e.g., SN 2008ax (Pastorello et al. 2008; Roming et al. 2009), SN 2011dh (Arcavi et al. 2011), SN 2011fu (Kumar et al. 2013; Morales-Garoffolo et al. 2015), SN 2013df (Fremling et al. 2014), and SN 2016gkg (Arcavi et al. 2017; Tartaglia et al. 2017). A handful of Type Ib events have also been discovered at very early phases, such as SN 1999ex (Hamuy et al. 2002; Stritzinger et al. 2002), SN 2008D (Mazzali et al. 2008; Soderberg et al. 2008; Malesani et al. 2009; Modjaz et al. 2009; Bersten et al. 2013), iPTF13bvn (Bersten et al. 2014; Fremling et al. 2016), and LSQ13abf (Stritzinger et al. 2020), and their observational properties are studied in detail. Recently, Prentice et al. (2019) analyzed the properties of 18 SE-SNe and discussed the implications for their progenitors. Direct detection of SE-SNe progenitor candidates has been possible only for a few objects, e.g., SN 1993J (Maund et al. 2004), SN 2011dh (Maund et al. 2011; Van Dyk et al. 2013), SN 2001ig (Ryder et al. 2018), iPTF13bvn (Cao et al. 2013; Eldridge et al. 2015; Kuncarayakti et al. 2015; Folatelli et al. 2016), SN 2016gkg (Kilpatrick et al. 2017; Tartaglia et al. 2017), SN 2017ein (Kilpatrick et al. 2018; Van Dyk et al. 2018; Xiang et al. 2019), and SN 2019yvr (Kilpatrick et al. 2021). Along with very early phases, the temporal observations during maximum to nebular phases are equally important to estimate various explosion parameters and progenitor properties. Detailed investigation of more events can provide an alternative way to understand various progenitor channels.

2017MNRAS.465..213B__David_et_al._1993_Instance_1

As a baseline for understanding how the scaling relations evolve as a function of mass and redshift, we adopt the following self-similar scalings: (2) \begin{equation} M_{\rm {gas},\Delta }\propto M_{\Delta }, \end{equation} (3) \begin{equation} T_{\Delta }\propto M^{2/3}_{\Delta }E^{2/3}(z), \end{equation} (4) \begin{equation} Y_{\rm {X},\Delta }\propto M_{\Delta }^{5/3}E^{2/3}(z), \end{equation} (5) \begin{equation} Y_{\rm {SZ},\Delta }\propto M_{\Delta }^{5/3}E^{2/3}(z), \end{equation} (6) \begin{equation} L_{\Delta }^{\rm {X,bol}}\propto M^{4/3}_{\Delta }E^{7/3}(z), \end{equation} (7) \begin{equation} L_{\Delta }^{\rm {X,bol}}\propto T^{2}E(z), \end{equation} where $E(z)\equiv H(z)/H_0=\sqrt{\Omega _{\text{m}}(1+z)^3+\Omega _{\Lambda }}$, Δ is the chosen overdensity relative to the critical density and YX is the X-ray analogue of the integrated SZ effect. These are derived in Appendix B. Although shown to be too simplistic by the first X-ray studies of clusters (Mushotzky 1984; Edge & Stewart 1991; David et al. 1993), the self-similar relations allow us to investigate if astrophysical processes are less significant in more massive clusters or at higher redshift. To enable a comparison with the self-similar predictions, and previous work, we fit the scaling relations of our samples at each redshift. We derive a median relation by first binning the clusters into bins of log mass (width: 0.1 dex) or log temperature (width: 0.07 dex) and then computing the median in each bin with more than 10 clusters. We also remove the evolution in normalization predicted by self-similar relations. The medians of the bins are then fitted with a power law of the form (8) \begin{equation} E^{\beta }(z)Y=10^A\left(\frac{X}{X_0}\right)^{\alpha }\!\!, \end{equation} where A and α describe the normalization and slope of the best fit, respectively, β removes the expected self-similar evolution with redshift, X is either the total mass or temperature and Y is the observable quantity (Mgas, LX, bol, etc.). X0 is the pivot point, which we set to 4 × 1014 M⊙ for observable–mass relations and to 6 keV for observable–temperature relations. We note that we fix the pivot for all samples and all redshifts. Fitting to the medians of bins, rather than individual clusters, prevents the fit from being dominated by low-mass objects, which are significantly more abundant due to the shape of the mass function. For the hot sample and its relaxed subset, there are too few bins with 10 or more clusters to reliably derive a best-fitting relation at z ≥ 1. By limiting our sample to systems with M500 ≥ 1014 M⊙, we avoid any breaks in the power-law relations that have been seen both observationally and in previous simulation work (Le Brun et al. 2016).

2019MNRAS.487...24G__Rogers_2015_Instance_1

NASA’s Kepler mission has unveiled a wealth of new planetary systems (e.g. Borucki et al. 2010). These systems offer new insights into the process of planet formation and evolution. One of Kepler’s key findings is that the most common planets in our Galaxy, observed to date, are between 1 and 4 R⊕, i.e. larger than Earth but smaller than Neptune (Fressin et al. 2013; Petigura, Marcy & Howard 2013). Further observations revealed a transition in average densities at planet sizes ∼1.5 R⊕ (Marcy et al. 2014; Rogers 2015), with smaller planets having densities consistent with rocky compositions while larger planets having lower densities indicating significant H/He envelopes. In addition, Owen & Wu (2013) noticed a bimodal distribution of observed planet radii. Since then, refined measurements have provided strong observational evidence for the sparseness of short-period planets in the size range of ∼1.5–2.0 R⊕ relative to the smaller and larger planets, yielding a valley in the small exoplanet radius distribution (e.g. Fulton et al. 2017; Fulton & Petigura 2018). For example, the California-Kepler Survey reported measurements from a large sample of 2025 planets, detecting a factor of ∼2 deficit in the relative occurrence of planets with sizes ∼1.5–2.0 R⊕ (Fulton et al. 2017). Studies suggest that this valley likely marks the transition from the smaller rocky planets: ‘super-Earths’, to planets with significant H/He envelopes typically containing a few per cent of the planet’s total mass: ‘sub-Neptunes’ (e.g. Lopez & Fortney 2013, 2014; Owen & Wu 2013; Rogers 2015; Ginzburg, Schlichting & Sari 2016). Furthermore, the location of this valley is observed to decrease to smaller planet radii, Rp, with increasing orbital period, P. In a recent study involving asteroseismology-based high precision stellar parameter measurements for a sample of 117 planets, a slope $\text{d log} R_\mathrm{ p}/ \text{d log} P = -0.09^{+0.02}_{-0.04}$ was reported for the radius valley by Van Eylen et al. (2018). A similar value for the slope of $-0.11^{+0.03}_{-0.03}$ was reported by Martinez et al. (2019).

2022MNRAS.515.5121B__Spiniello_et_al._2018_Instance_1

There are multiple sources of spurious detections in strong lensing analysis, and some of them are just image artefacts. None the less, others are images that might look like a lens, as in the case of edge-on galaxies and some spiral galaxies. One popular approach to reduce this issue is to use colours and look for red galaxies with a blue object close. The colour information is considered an important feature to find lenses (see e.g. Ostrovski et al. 2018; Spiniello et al. 2018). The use of colour queries is considered at least an interesting way to make a pre-selection and improve the final deep learning result purity. In fact, even in the I SGLC results the single-band scenario found lower performance in terms of the considered metric, the AUC of ROC. The II SGLC in a Euclid-like scenario allowed to make deep learning classifications using images with colour information in multiple bands but also higher resolution images. Interestingly the networks using only HJY bands did not find a competitive fit. On the other hand, using the VIS band only with a higher resolution found better results than HJY, which is close to a random guess. Still, the VIS band only has inferior results if compared to the runs using all information. This result suggests that the use of high-resolution images might play an important role and was in fact, more relevant than the multiple bands in the cases tested in this contribution. It is worth mentioning that the threshold defined by the maximum Fβ, with β = 0.001, privileged a pure sample instead of a complete one, obtaining in our best network, HJY + VIS with alternative pre-processing, around ${\sim} 99{{\ \rm per\ cent}}$ purity with completeness of around $45{{\ \rm per\ cent}}$. This choice is justified for the same reason a pre-selection of targets is implemented by colour queries and other methods to reduce the number of non-lenses in a survey where we have billions of non-lenses for thousands of lenses.