adsabs/FOCAL huggingface dataset:
Recently, quasars observed with multiple measurements, another potential cosmological probe with a higher redshift range that reaches to z ∼ 5, is becoming popular to constrain cosmological models in the largely unexplored portion of redshift range from z ∼ 2 to z ∼ 5. A sample that contains 120 angular size measurements in intermediate-luminosity quasars from the very long baseline interferometry (VLBI) observations (Cao et al. 2017a,b) has become an effective standard ruler, which have been extensively applied to test cosmological models (Li et al. 2017; Melia et al. 2017; Qi et al. 2017; Zheng et al. 2017; Xu et al. 2018; Ryan, Chen & Ratra 2019), measuring the speed of light (Cao et al. 2017a, 2020a), exploring cosmic curvature at different redshifts (Cao et al. 2019; Qi et al. 2019), and the validity of cosmic distance duality relation (Zheng et al. 2020). Then, Risaliti & Lusso (2019) put forward a new compilation of quasars containing 1598 quasi-stellar object (QSO) X-ray and ultraviolet (UV) flux measurements in the redshift range of 0.036 ≤ z ≤ 5.1003, which have been used to constrain cosmological models (Khadka & Ratra 2020b) and cosmic curvature at high redshifts (Liu et al. 2020a,c), as well as test the cosmic opacity (Geng et al. 2020; Liu et al. 2020b). Making use of this data to explore cosmological researches mainly depends on the empirical relationship between the X-ray and UV luminosity of these high-redshift quasars proposed by Avni & Tananbaum (1986), which leads to the Hubble diagram constructed by quasars (Risaliti & Lusso 2015, 2017; Lusso & Risaliti 2016; Bisogni, Risaliti & Lusso 2017). In general, the advantage of these two QSO measurements over other traditional cosmological probes is that QSO has a larger redshift range, which may be rewarding in exploring the behaviour of the non-standard cosmological models at high redshifts, providing an important supplement to other astrophysical observations and also demonstrating the ability of QSO as an additional cosmological probe (Zheng et al. 2021).
Citation: Xu et al. 2018
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Citation: Melia et al. 2017
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At scales comparable with dp, a variety of observations in the solar wind have suggested that fluctuations may consist primarily of kinetic Alfvén waves (KAWs; Bale et al. 2005; Sahraoui et al. 2009). In the linear fluctuation terminology, KAWs are waves belonging to the Alfvén branch, at wavevectors k almost perpendicular to the ambient magnetic field B0, with \hbox{$k \sim d_{\rm p}^{-1}$}k~dp-1. A detailed discussion of the properties of KAWs can be found in Hollweg (1999; see also references therein for a more complete view of the subject), for example. In the last decades, KAWs have received considerable attention due to their possible role in a normal mode description of turbulence. Indeed, theoretical studies (e.g., Shebalin et al. 1983; Carbone & Veltri 1990; Oughton et al. 1994) have shown that the turbulent cascade in magnetized plasma tends to develop mainly in directions perpendicular to B0. Anisotropic spectra have been commonly observed in space plasmas, showing the presence of a significant population of quasi-perpendicular wavevectors (Matthaeus et al. 1986, 1990). The above considerations suggest that fluctuations with characteristics similar to KAWs are naturally generated by a turbulent cascade at scales close to dp. Many solar wind observational studies (Bale et al. 2005; Sahraoui et al. 2009; Podesta & TenBarge 2012; Salem et al. 2012; Chen et al. 2013; Kiyani et al. 2013), theoretical works (Howes et al. 2008a; Schekochihin et al. 2009; Sahraoui et al. 2012) as well as numerical simulations (Gary & Nishimura 2004; Howes et al. 2008b; TenBarge & Howes 2012) have suggested that fluctuations near the end of the magnetohydrodynamics inertial cascade range may consist primarily of KAWs, and that such fluctuations can play an important role in the dissipation of turbulent energy. Due to a non-vanishing parallel component of the electric field associated with KAWs, these waves have also been considered in the problem of particle acceleration (Voitenko & Goossens 2004; Décamp & Malara 2006). Particle acceleration in Alfvén waves in a dispersive regime has been studied both in 2D (Tsiklauri et al. 2005; Tsiklauri 2011) and 3D (Tsiklauri 2012) configurations. Recently, Vasconez et al. (2014) have studied collisionless Landau damping and wave-particle resonant interactions in KAWs.
Citation: Carbone & Veltri 1990
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There is growing observational and numerical evidence that star-forming regions may be in a state of global gravitational contraction; see Vázquez-Semadeni et al. (2019). The supersonic collisions of flows of warm diffuse atomic gas simulated with both self-gravity and cooling exhibit the hierarchical collapse of the turbulent medium, as was shown by Vázquez-Semadeni et al. (2007) and Naranjo-Romero, Vázquez-Semadeni & Loughnane (2015) for example. This implies that gravitational instability (GI hereafter) manifests itself in a wide range of sufficiently large scales during the evolution of molecular clouds. Theoretical work has revealed that flattened dense structures form as a result of large collisions of diffuse matter. Later on, they give birth to filaments, which then fragment into multiple cores. This scenario is provided by the dynamical instability of self-gravitating layers, cylinders, and spheres, respectively. The linear stability analysis of these idealized configurations (e.g. Ledoux 1951; Chandrasekhar & Fermi 1953; Bonnor 1956; Elmegreen & Elmegreen 1978; Nagasawa 1987; Fiege & Pudritz 2000) as well as the corresponding non-linear solutions (e.g. Larson 1969; Penston 1969; Miyama, Narita & Hayashi 1987; Inutsuka & Miyama 1997; Masunaga & Inutsuka 2000, and many others) confirms this view. At the same time, as was noted by Larson (1985), the specific geometry of self-gravitating objects is not crucial for the instability condition, which does not differ much from the basic one derived for the unbounded uniform medium. In the latter case, the study of GI goes back to Jeans (1902), who established that plane-wave perturbations on such a background having finite pressure are heavy sound waves propagating at the subsonic velocity, which vanishes as the wavelength approaches the value now referred to as the Jeans length. Perturbations with scale larger than the Jeans length are the growing and damping static waves. Thus, the critical scales for GI of realistic configurations mentioned above are always similar to the Jeans scale, which includes typical speed of sound and density chosen appropriately for the corresponding configuration. However, the most unstable scale for realistic configurations has a finite value in contrast to the Jeans result, when the largest growth rate (corresponding to the inverse free-fall time) manifests at the infinitely large scale. The largest growth rates for GI of realistic configurations are commonly the fractions of the inverse free-fall time.
Citation: Miyama, Narita & Hayashi 1987
Citation function: Similarities; Similarities; Similarities
We find a similar dichotomy of bars in previous studies (Elmegreen & Elmegreen 1985, 1989; Baumgart & Peterson 1986; Elmegreen et al. 1996; Regan & Elmegreen 1997; Kim et al. 2015). They investigated two types of bars: flat and exponential profiles in surface brightness. Flat bars have nearly constant light distributions along the bar, whereas those of exponential bars decrease exponentially. They also differ in their structures and the intensity contrast between the bar and the disk: flat bars are longer, wider, and stronger than exponential bars and have a higher contrast than exponential bars. Besides, Athanassoula (1992) explained that flat bars could have roughly rectangular orbits around the end of the bar through the stellar orbit calculation. These properties can also be explained by the locations of resonances (Lynden-Bell 1979; Contopoulos & Papayannopoulos 1980; Sellwood 1981; Contopoulos et al. 1989; Athanassoula 1992; Skokos et al. 2002). A flat density profile develops in crowding stellar orbits between the inner 4:1 resonance and corotation radius (Combes & Elmegreen 1993; Elmegreen & Elmegreen 1985; Elmegreen et al. 1996). Exponential bars end near the inner Lindblad resonance and do not have such crowding orbits (Lynden-Bell 1979; Elmegreen & Elmegreen 1985; Elmegreen et al. 1996). Therefore, flat bars and exponential bars may be expected to have different pattern speeds based on the value of 





, where Rcr and Rbar are the radius of the corotation resonance and the bar, respectively (Debattista & Sellwood 2000; Valenzuela & Klypin 2003). Although some studies reported the observational lack of slow bars (Debattista & Sellwood 2000; Aguerri et al. 2015), others showed that the pattern speed of bars roughly depends on the Hubble type: fast bars in early-type spirals and slow bars in late-type spirals (Aguerri et al. 1998; Rautiainen et al. 2008). More observational data will help us understand the relation between the density profile and the pattern speed of bars.
Citation: Skokos et al. 2002
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In Sects. 4.3 and 4.4 we showed that our image simulations match the actual data very well and that the re-weighting factors are close to unity for subsets of galaxies split by size and S/N, with fluctuations of the order of 10−4 or less. Consequently, the estimates of the shear biases presented in Table 2 should be accurate. Nonetheless it is worthwhile to explore the robustness of our results and attempt to quantify the potential systematic uncertainties that may still be present. For instance, we simplified the galaxy morphologies by representing them with Sérsic profiles. Our input catalogue is incomplete at the faintest magnitudes, but the missing galaxies may still affect the estimate of the multiplicative bias. Varying star densities can affect the results (e.g. Hoekstra et al. 2015, 2017). In this section we therefore explore the sensitivity of the shear measurement bias to various assumptions and simplifications made in the simulations. These results help to assess the robustness of the calibration presented in the previous section. In the language described in Sect. 2.2, these tests correspond to various evaluations of the Δ terms from different simulations. As these tests can become computationally expensive very quickly, we use only 5 of the 13 PSF sets. This results in only minor changes in the mean residual bias values ( 0.005; see lower right panel of Fig. 13), and hence they remain a good representation of the data. The smaller volume of simulations naturally results in a larger statistical uncertainty, but we note that we are interested in determining the change in the mean values of the multiplicative biases of the different tomographic samples when we vary the inputs. The errors are tightly correlated among the different simulations within a tomographic bin, as there is a significant overlap among the input samples of galaxies in the different simulations and because the noise realisations in the images are identical in many of these simulations, unless explicitly mentioned otherwise. Hence, the shifts are not driven by noise. Our main objective is to ensure that uncertainties in the input quantities do not change the bias by more than 0.02. The results in this section indicate that they are indeed controlled under 0.02, but it appears that we cannot impose a tighter limit on the overall uncertainty at this time.
Citation: Hoekstra et al.; 2017
Citation function: Uses; Uses
Most DSFGs are discovered in wide-area observations using single-dish far-IR (FIR)/millimeter instruments such as the Submillimeter Common-User Bolometer Array (SCUBA/SCUBA-2) on the James Clerk Maxwell Telescope (JCMT; e.g., Smail et al. 1997; Hughes et al. 1998; Chapman et al. 2005; Koprowski et al. 2017; Simpson et al. 2019), the Herschel Space Observatory
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Herschel is an ESA space observatory with science instruments provided by European-led Principal Investigator consortia and with important participation from NASA. (Pilbratt et al. 2010; Eales et al. 2010; Oliver et al. 2012), and the AzTEC instrument (Scott et al. 2008; Aretxaga et al. 2011). Multi-wavelength follow-up of hundreds of survey-identified DSFGs reveals that most sit between 1  z  3 (Casey et al. 2012a; Magnelli et al. 2011, 2013; Gruppioni et al. 2013; Le Floc’h et al. 2005). While there exists a handful of individually studied DSFGs at redshifts as high as z ∼ 5–7 (e.g., Cooray et al. 2014; Strandet et al. 2017; Marrone et al. 2018; Zavala et al. 2018b; Casey et al. 2019; Reuter et al. 2020), such high-z systems have proven difficult to both identify and spectroscopically confirm. This is because (i) DSFGs at z > 4 are outnumbered by the dominant DSFG population at z ≈ 1–3 and (ii) there are serious physical and evolutionary degeneracies that make DSFG photometric redshifts highly uncertain (with precision often σ
Δz/1+z
 ≳ 1; Casey 2020). This latter point is often seen as a benefit: their strongly negative k-correction means that the flux density of DSFGs at z > 1 remains constant with increasing z for λ
obs ≳ 850 μm, meaning a DSFG at z ∼ 10 can be observed as readily as a DSFG at z ∼ 1 (Blain et al. 2002). However, when searching for high-z DSFGs, this negative k-correction is also a hindrance as it becomes difficult to identify redshifts for galaxies with only long-wavelength emission. This effect is further exacerbated by the (sub)millimeter color degeneracy between dust temperature and redshift. Thus, barring clear identification at other wavelengths, it is easy to confuse z ∼ 2 and z ∼ 6 DSFGs with solely submillimeter observations.
Citation: Pilbratt et al. 2010
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High-redshift z ≳ 2 galaxies with prominent Lyman-α emission (Partridge & Peebles 1967), referred to as Lyman-α emitters (LAEs), have become the subject of intense research over the last two decades. Since LAEs are believed to be powered at least partially by ongoing star formation, they are expected to belong to a relatively low-mass and young class of actively star-forming galaxies. This suggests that the number density of LAEs is large enough to map out the large-scale structure of the high-redshift universe. Increasing numbers of LAEs are being observed by surveys including narrow-band imaging (e.g., Nakajima et al. 2012) and integral field unit spectroscopy such as MUSE (e.g., Bacon et al. 2006), amounting to about 104 emitters known to date since the first detections (e.g., Cowie & Hu 1998; Hu & McMahon 1996). These observations enable us to study galaxy clustering at somewhat small scales (e.g., Diener et al. 2017; Ouchi et al. 2010, 2018) as well as evolution of the Lyman-α luminosity function (e.g., Konno et al. 2016, 2018; Ouchi et al. 2008). A remarkable example for such surveys is the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX; Adams et al. 2011; Hill et al. 2008). HETDEX will map out the three-dimensional distribution of nearly one million LAEs (Leung et al. 2017) at 1.9  z  3.5 over 400 deg2. This allows us to precisely measure the large-scale (≳10 Mpc) clustering of LAEs (Agrawal et al. 2017; Chiang et al. 2013) and of the Lyman-α intensity map (Saito et al., in prep.) with the primary scientific goal being to determine the cosmic expansion history via baryon acoustic oscillations (BAOs) and the growth of structure via redshift-space distortions (RSD). We refer the reader to Alam et al. (2017) and references therein for recent efforts of BAO and RSD measurements. In addition, these surveys offer exciting opportunities to study the connection between LAEs and their environment, including the cross-correlation between LAEs and the Lyman-α forest or other galaxy populations.
Citation: Nakajima et al. 2012
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Several mechanisms have been suggested to explain the formation of moving groups. A common explanation is that these velocity structures are the remnants of open clusters, or formed by interactions with a bar (Eggen 1965; Dehnen 2000). One problem with the cluster formation idea is that stars in moving groups can have a variety of different ages and compositions, so it is unlikely that they all came from the same cluster (Eggen 1965; Kushniruk et al. 2020). Analysis of GALAH data (Quillen et al. 2018) indicates that some moving groups, such as the Hercules moving group, may be due to a resonant bar. It has also been suggested that moving groups could have been formed from perturbations due to the Magellanic Clouds via gravitational interactions (Dehnen 1998). Recent work also finds that transient spiral structure (Hunt et al. 2018) may lead to the formation of moving groups, as well as perturbations due to spiral arms in the MW (Michtchenko et al. 2018). Moving groups in Gaia data have also been identified and analysed in action space. In the (JR, Jz) plane there are at least seven overdensities that follow lines of constant slope in this plane, which correspond to known moving groups in the solar neighbourhood (Trick, Coronado & Rix 2019). It is likely that there may be multiple causal mechanisms at play in the formation of moving groups in the Milky Way. The analysis of Gaia DR2 data has revealed many facets of a Galaxy that are clearly out of equilibrium, including the so-called phase-space spiral (Antoja et al. 2018), and the Enceladus merger (Helmi et al. 2018), that have been interpreted as arising from interactions with dwarf galaxies. Analysis of Gaia DR2 data also led to the discovery of a new dwarf galaxy (Torrealba et al. 2019) that likely interacted with the Milky Way (Chakrabarti et al. 2019). However, the formation of moving groups due to dwarf galaxy interactions has not yet been studied with full N-body simulations. Motivated by these earlier works that indicate that the MW may has been perturbed by dwarf galaxies, we focus our study here in trying to understand if some of the moving groups in the Galaxy may have arisen from dwarf galaxy interactions.
Citation: Hunt et al. 2018
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Citation: Michtchenko et al. 2018
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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.
Citation: Jones et al. 2021
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Citation: Jones et al. 2021
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The atmosphere above the Canary Islands, and especially the western islands Tenerife and La Palma, has been extensively characterized over the past 50 yr (Kiepenheuer 1972; McInnes & Walker 1974; Brandt & Righini 1985; Murdin 1985; Stickland et al. 1987; Whittet, Bode & Murdin 1987; Menéndez-Valdés & Blanco 1992; Jímenez, González Jorge & Rabello-Soares 1998; Mahoney, Muñoz-Tuñón & Varela 1998; Maring et al. 2000; Torres et al. 2002; Rodríguez et al. 2004; Alonso-Pérez et al. 2007; Basart et al. 2009; Lombardi, Zitelli & Ortolani 2009; Delgado et al. 2010; Vernin et al. 2011; Cuevas et al. 2013; Rodriguez-Franco & Cuevas 2013; Varela et al. 2014; Laken et al. 2016; Vogiatzis et al. 2018; Hidalgo et al. 2021). Due to the combination of large-scale atmospheric circulation on the descending branch of the Hadley cell (see e.g. Palmén & Newton 1969; Rodríguez et al. 2004), and the ‘Trade’ or ‘Alisios’ winds coming from the Azores high area, a stable and strong temperature inversion layer (TIL) appears (Font 1956; Huetz-de-Lemps 1969), whose top is typically found at heights around 1200 m a.s.l. in summer and 1800 m a.s.l. in winter (Torres et al. 2002; Carrillo et al. 2016). Whenever the temperature inversion is able to separate two well-defined regimes, the moist marine boundary layer (MBL) and above it, the dry free troposphere (FT), the phenomenon is called an ‘Alisio’ inversion. This happens about 80 per cent of the time (Torres et al. 2002). Under these conditions, the FT can be characterized as ‘ultraclean’, i. e., the concentration of accumulation mode particulate matter is lower than 50 cm−3 (Pennypacker, Diamond & Wood 2020). The clean atmospheric conditions are one of the reasons why the Canarian observatories ‘Observatorio del Teide (OT)’, located at ∼2400 m a.s.l., and ‘Observatorio del Roque de los Muchachos (ORM)’, located between ∼2100 and ∼2400 m a.s.l., are known to belong to the best astronomical sites worldwide.
Citation: Rodriguez-Franco & Cuevas 2013
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