adsabs/FOCAL huggingface dataset:
It is now well established that intrinsic alignments exist in the Universe, and must be accounted for at some level to avoid biasing cosmological analyses based on cosmic shear and galaxy–galaxy lensing. IAs have been included in cosmic shear analyses for as long as shear has been a competitive cosmological probe (Heymans et al. 2013; Dark Energy Survey Collaboration 2016; Jee et al. 2016; Troxel et al. 2018; Chang et al. 2019; Hikage et al. 2019; Hamana et al. 2020; Hildebrandt et al. 2020; Asgari et al. 2021). Only recently, however, have the lensing data been of sufficient volume to potentially incur biases due to model insufficiency (see Krause et al. 2016, and the Stage IV forecasts of Fortuna et al. 2021 and the tests in the context of DES Y3 Secco, Samuroff et al. 2021; see also Joachimi et al. 2020 for an interesting counter discussion). Developing a fuller understanding of intrinsic alignments, then, will be crucial for, arguably, the current generation of cosmological surveys, and certainly the next. This paper is one small step in this direction, providing the first detailed analysis at the level of model constraints on the best available cosmological hydrodynamic simulations. Our results, of course, come with a number of caveats. Most notably, our selection function is not intended to accurately match current or future lensing surveys. This is in part because recreating the complex redshift-dependent selection function in a real lensing sample, which would typically be based on a number of correlated observables, is a difficult task; it is also, however, a function of our aim in this study. We wish to understand the behaviour of IAs at a physical level, in order to feed into understanding IAs and model building efforts, rather than make a detailed prediction or robustness test for a particular survey. The behaviour of intrinsic alignments on small physical scales is an important topic for future investigation, and one that could conceivably be addressed using hydrodynamic simulations; indeed, due to the larger number of measureable modes, the signal to noise on small scales is relatively high. The TATT approach allows some hope of pushing to smaller scales (though not into the regime of ∼1 h−1 Mpc, where one would need an explicit model for 1h alignment contributions). Unfortunately, a number of other poorly understood effects enter on small scales, particularly non-linear galaxy bias and baryonic physics. In order to pursue IA constraints on such scales, it is likely that one would need to consider both higher order bias terms and the interplay with the higher order IA terms.
Citation: Krause et al. 2016
Citation function: Background
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.
Citation: Ford et al. 2014
Citation function: Motivation; Motivation; Motivation
Sulfur-bearing molecules constitute a fundamental branch of prebiotic chemistry due to the recently gained importance of cysteine C3H7NO2S as a catalyst in the assembly of complex peptides (Foden et al. 2020). In cold, interstellar environments, cysteine is far from being detected, but other complex organic molecules (COMs) of prebiotic importance bearing S have recently been detected, highlighting ethyl mercaptan C2H5SH or thioformic acid (HC(O)SH; Kolesniková et al. 2014; Rodríguez-Almeida et al. 2021). In turn, the family of compounds to which HC(O)SH belongs (thioacids) has also been pointed out as possible catalysts in prebiotic processes in a primordial Earth (Chandru et al. 2016). Particularly puzzling is the fact that the presence of these molecules on Earth could have a (at least partial) panspermic origin, owing to both a confirmed presence of sulfur-bearing species in comets (Korth et al. 1986; Krishna Swamy & Wallis 1987; Calmonte et al. 2016; Rubin et al. 2019) and the fact that the simplest thioacid (thioformic acid) has been recently identified in the interstellar medium (ISM; i.e., the giant molecular cloud G+0.693; see Rodríguez-Almeida et al. 2021). The chemical mechanism behind the formation of thioformic acid is unknown, but such a mechanism needs to explain the formation of HC(O)SH from simple, sulfur-bearing precursors. In Rodríguez-Almeida et al. (2021) several possible routes were postulated for the formation of HC(O)SH, namely,
1



CO+SH→HSCO→HHC(O)SH,



2



HCO+SH→HC(O)SH,



3



OCS+2H→HC(O)SH.

In this work, we have focused on reaction (3) on interstellar dust grains, having two main objectives in mind. First, we wish to check whether or not the reaction proceeds effectively, and, second, we wish to determine the stereoisomerism of the reaction. The study of reaction (3) sparked our interest for two main reasons. First, OCS is a moderately abundant molecule in the astronomical source where HC(O)SH was detected (Armijos-Abendaño et al. 2014; Rodríguez-Almeida et al. 2021); second OCS is the only sulfur-bearing species positively detected on interstellar ices (Palumbo et al. 1997).
Citation: Rubin et al. 2019
Citation function: Motivation
One major issue we face with the ALMA dust polarization observations is spatial filtering by the interferometer, which removes the scales of emission that are not included in the uv -coverage of the dataset. In contrast to the statistical analysis of dust polarization performed with single dish instruments such as Planck (Planck Collaboration Int. XIX 2015; Planck Collaboration Int. XX 2015; Planck Collaboration XII 2020), BLASTPOL in Fissel et al. (2016), and SCUPOL (Poidevin et al. 2013), our analysis of interferometric data requires us to characterize how the filtering alters the polarization quantities we use in our statistics. With this aim, we use a set of synthetically observed non-ideal MHD simulations computed with RAMSES (Teyssier 2002; Fromang et al. 2006) that follow the gravitational collapse of cores whose range of initial mass and turbulence reproduce the main characteristics of the sources from our sample. The set consists of six simulations of collapsing cores (with total masses of 30, 60, and 100 M⊙). We perform radiative transfer on these models using the POLARIS code (Reissl et al. 2016), which produces the Stokes I, Q, and U maps and assumeseither that a constant fraction of the dust grains are perfectly aligned everywhere (perfect alignment, known as “PA” hereafter) or that paramagnetic grains are aligned via radiative torques, known as “RATs” hereafter (e.g., Lazarian 2007). We note that the hypothesis of perfect alignment is not physical, and we do not aim to reproduce or interpret the polarized dust emission from Class 0 envelopes as resulting from perfect alignment. However, while we recognize that an hypothesis of perfect dust alignment is not a physical model but a phenomenological one, it has been suggested that the properties of dust polarization at the larger scales of the diffuse ISM (especially the results of 

${{\mathcal{S}}\times{\mathcal{P}_{\rm{frac}}}}$S×Pfrac
) can be explained and reproduced with perfect alignment (Planck Collaboration XII 2020; Seifried et al. 2020). In the first part of our discussion we aim to compare our results with those obtained at larger scales, and thus perfect alignment remains an interesting point of comparison with RATs, and is a useful benchmark to compare how different physical models of grain alignment affect the statistical properties of the polarized emission. In addition, a case where the grains are perfectly aligned is only taking into account the source-specific geometrical effects governing the resulting polarization maps, and thus is useful to understand where alignment drops or is suppressed. We present all the details of the simulations and the radiative transfer calculations in Appendix D. In order to produce realistic synthetic observations to compare with the ALMA datasets, we use the CASA simulator (with the typical ALMA uv -coverage of these observations) to implement the effects of interferometric filtering and atmospheric noise on the POLARIS synthetic emission maps.
Citation: Reissl et al. 2016
Citation function: Uses; Uses
Whistler precursors are a manifestation of dispersive radiation (Tidman & Northrop 1968; Fairfield 1974; Hoppe 1981; Mellott & Greenstadt 1984), one mechanism through which collisionless shocks may transform bulk flow kinetic energy into other forms of energy. Several other mechanisms have been proposed, including wave–particle interactions (Sagdeev 1966; Coroniti 1970a; Gary 1981), particle reflection (Edmiston & Kennel 1984; Kennel et al. 1985; Kennel 1987; Bale et al. 2005; Su et al. 2012), and macroscopic quasi-static field effects (Scudder et al. 1986a, 1986b, 1986c). At low Mf, theory suggests that dispersive radiation and wave–particle interactions dominate (Kennel et al. 1985). Because this study focuses on low Mach number shocks (Mf ≲ 3), particle reflection and macroscopic field effects are not investigated in detail. Whistler precursors (Tidman & Northrop 1968; Fairfield 1974) are often observed and can dissipate energy through several processes, including generation of higher frequency waves by creating electron temperature anisotropies or current-driven instabilities (Gary 1981; Hull et al. 2012), acceleration of halo electrons and thermal ions (Wilson III et al. 2012; Chen et al. 2018), and deflection and modulation of core particles (Goncharov et al. 2014). Wave–particle interactions dissipate energy through anomalous resistivity, shorthand for the energy and momentum exchange between the electromagnetic fields and particles (Sagdeev 1966; Coroniti 1970a; Gary 1981; Papadopoulos 1985; Wilson III et al. 2007, 2010, 2012, 2014a, 2014b; Breneman et al. 2013). Goodrich et al. (2018) showed that ion acoustic waves may be indicators or facilitators of momentum transfer between reflected and incident ion populations, thus linking these waves to the transformation of bulk flow kinetic energy. In addition, work by Wang et al. (2020) shows how reflected ions interacting with incident ions generate Debye-scale electrostatic fluctuations, starting as ion acoustic waves that trap ions and decay into electrostatic solitary waves. Krasnoselskikh et al. (2013) provides a review of the quasi-perpendicular bow shock and the inferred dissipation mechanisms based on Cluster observations. Some of the types of waves observed near shocks include magnetosonic whistler mode waves (Fairfield 1974; Coroniti et al. 1982), solitary waves (Bale et al. 1998), ion acoustic waves (Fredricks et al. 1968; Gary et al. 1975; Rodriguez & Gurnett 1975; Gurnett et al. 1979a, 1979b; Formisano & Torbert 1982; Fuselier & Gurnett 1984; Balikhin et al. 2005; Hull et al. 2006), Langmuir waves (Filbert & Kellogg 1979; Kellogg 2003, and references therein; Pulupa & Bale 2008), and electron cyclotron harmonic waves and waves associated with the electron cyclotron drift instability (ECDI) (Wilson III et al. 2010, 2014a, 2014b; Breneman et al. 2013; Goodrich et al. 2018).
Citation: Goncharov et al. 2014
Citation function: Background; Background; Background
The first direct proof of the existence of magnetic fields in large-scale extragalactic environments, i.e., galaxy clusters, dates back to the 1970s with the discovery of extended, diffuse, central synchrotron sources called radio halos (see, e.g., Feretti et al. 2012 for a review). Later, indirect evidence of the existence of intracluster magnetic fields has been given by several statistical studies on the effect of the Faraday rotation on the radio signal from background galaxies or galaxies embedded in galaxy clusters (Lawler & Dennison 1982; Vallée et al. 1986; Clarke et al. 2001; Johnston-Hollitt 2003; Clarke 2004; Johnston-Hollitt & Ekers 2004). On scales up to a few Mpc from the nearest galaxy cluster, possibly along filaments, only a few diffuse synchrotron sources have been reported (Harris et al. 1993; Bagchi et al. 2002; Kronberg et al. 2007; Giovannini et al. 2013, 2015). Magnetic fields with strengths on the order of 10-15 G in voids might be indicated by γ-ray observations (see Neronov & Vovk 2010; Tavecchio et al. 2010; Takahashi et al. 2012, 2013; but see Broderick et al. 2014a,b for alternative possibilities). Nevertheless, up to now, a robust confirmed detection of magnetic fields on scales that are much larger than clusters is not available. Stasyszyn et al. (2010) and Akahori et al. (2014a) investigated the possibility of statistically measuring Faraday rotation from intergalactic magnetic fields with present observations, showing that only the Square Kilometre Array (SKA) and its pathfinders are likely to succeed in this respect. By comparing the observations with single-scale magnetic field simulations, Pshirkov et al. (2015) infer an upper limit of 1.2 nG for extragalactic large-scale magnetic fields, while the Planck Collaboration XIX (2016) derived a more stringent upper limit for primordial large-scale magnetic fields of B 0.67 nG from the analysis of the Cosmic Microwave Background (CMB) power spectra and the effect on the ionization history (but see also Takahashi et al. 2005; Ichiki et al. 2006).
Citation: Vallée et al. 1986
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However, the determination of the faint side of the QLF at z ∼ 5 is important to understand the overall picture of SMBH growth in the early universe. Previous observations suggest that the number density of luminous quasars increased from the early universe to z ∼ 2 and then decreased to the current universe (e.g., Richards et al. 2006; Croom et al. 2009). It is of particular interest to study possible luminosity dependences of the quasar number-density evolution (luminosity-dependent density evolution; LDDE). Recent optical surveys of high-redshift quasars have reported that low-luminosity quasars show peak number density at lower redshifts than do high-luminosity quasars (e.g., Croom et al. 2009; Ikeda et al. 2011, 2012; Niida et al. 2016). Since the quasar luminosity at a given Eddington ratio corresponds to MBH, the reported LDDE trend is sometimes called the downsizing evolution. The same trend has been found by X-ray surveys for all AGN populations, including obscured (type-2) ones (e.g., Ueda et al. (2003, 2014), Hasinger et al. (2005), Aird et al. (2015), Miyaji et al. (2015); see also Enoki et al. (2014) and Shirakata et al. (2019) and references therein for theoretical works on the AGN downsizing evolution). Note that the downsizing evolution was originally proposed to describe the redshift evolution of the galaxy mass function (e.g., Cowie et al. 1996; Neistein et al. 2006; Fontanot et al. 2009). Therefore, the AGN downsizing evolution, if present, may provide a significant insight into the galaxy–SMBH coevolution. However, the number density of low-luminosity quasars at high redshifts has been quite uncertain, which has prevented us from understanding the whole picture of the quasar LDDE. Ueda et al. (2014) noted a possibility that the number ratio of total (type-1 and type-2) low-luminosity AGNs to high-luminosity ones may increase from z ∼ 3 to z ∼ 5, which is referred to as “up-down sizing” (see also Glikman et al. 2010, 2011; Giallongo et al. 2015). We need large samples of high-z low-luminosity AGNs to examine such scenarios.
Citation: Glikman et al.; 2011
Citation function: Background
C-statistic (e.g. Jang & Miller 1997) is the most commonly used and the one-way analysis of variance (ANOVA; de Diego 2010) the most powerful test for verifying the presence of variability in a DLC. However, we did not employ either of these tests because, de Diego (2010) has questioned the validity of the C-test by arguing that the C-statistics does not have a Gaussian distribution and the commonly used critical value of 2.567 is too conservative. On the other hand, the ANOVA test requires a rather large number of data points in the DLC, so as to have several points within each sub-group used for the analysis. This is not feasible for our DLCs which typically have no more than about 30–45 data points. Therefore, we have instead used the F-test which is based on the ratio of variances, F = variance(observed)/variance(expected) (de Diego 2010; Villforth, Koekemoer & Grogin 2010), with its two versions : (i) the standard F-test (hereafter Fη-test, Goyal et al. 2012) and (ii) scaled F-test (hereafter Fκ-test, Joshi et al. 2011). The Fκ-test is preferred when the magnitude difference between the object and comparison stars is large (Joshi et al. 2011). Onward Paper II, we have only been using the Fη-test because our objects are generally quite comparable in brightness to their available comparison stars. An additional gain from the use of the Fη-test is that we can directly compare our INOV results with those deduced for other major AGN classes (Goyal et al. 2013). An important point to keep in mind while applying the statistical tests is that the photometric errors on individual data points in a given DLC, as returned by the algorithms in the iraf and daophot softwares are normally underestimated by the factor η which ranges between 1.3 and 1.75, as estimated in independent studies (e.g. Gopal-Krishna, Sagar & Wiita 1995; Garcia et al. 1999; Sagar et al. 2004; Stalin et al. 2004a; Bachev, Strigachev & Semkov 2005). Recently, using a large sample, Goyal et al. (2013) estimated the best-fitting value of η to be 1.5, which is adopted here. Thus, the Fη statistics can be expressed as


\begin{equation*}
F_{1}^{\eta } = \frac{\sigma ^{2}_{({\rm q-s1})}}{ \eta ^2 \langle \sigma _{{\rm q-s1}}^2 \rangle }, \hspace{5.69046pt} F_{2}^{\eta } = \frac{\sigma ^{2}_{({\rm q-s2})}}{ \eta ^2 \langle \sigma _{{\rm q-s2}}^2 \rangle }, \hspace{5.69046pt} F_{{\rm s1-s2}}^{\eta } = \frac{\sigma ^{2}_{({\rm s1-s2})}}{ \eta ^2 \langle \sigma _{{\rm s1-s2}}^2 \rangle },\end{equation*}

 where $\sigma ^{2}_{({\rm q-s1})}$, $\sigma ^{2}_{({\rm q-s2})}$ and $\sigma ^{2}_{({\rm s1-s2})}$ are the variances of the ‘quasar–star1’, ‘quasar–star2’ and ‘star1–star2’ DLCs and $\langle \sigma _{{\rm q-s1}}^2 \rangle =\sum _{\boldsymbol {i}=0}^{N}\sigma ^2_{i,{\rm err}}({\rm q-s1})/N$, $\langle \sigma _{{\rm q-s2}}^2 \rangle$ and $\langle \sigma _{{\rm s1-s2}}^2 \rangle$ are the mean square (formal) rms errors of the individual data points in the ‘quasar–star1’, ‘quasar–star2’ and ‘star1–star2’ DLCs, respectively. η is the scaling factor (= 1.5) as mentioned above.
Citation: Joshi et al. 2011
Citation function: Uses; Uses; Uses
One possible, conventional, explanation for an apparently accelerating rotational period may be the light-time effect due to the orbit of a binary companion. However, none of the magnetic B-type stars in which this phenomenon has been detected, including the present star, are known to be in binary systems (Shatsky & Tokovinin (2002) conducted an NIR search for visual companions, and found no evidence of a companion in the case of HD 142990). The change in period ΔP due to the light-time effect should correspond to a change in radial velocity ΔRV = cΔP/P, where c is speed of light (e.g. Pigulski & Boratyn 1992). Fig. 6 shows the least-squares deconvolution (LSD) profiles extracted from the ESPaDOnS data set with a line mask using all metallic lines in the spectrum (for details, see Shultz et al. 2018). No bulk RV variability is apparent. Measuring the RV is complicated by the spectroscopic variability introduced by chemical spots, which in addition to equivalent width changes also introduce RV variations coherent with the rotation phase due to changes in the star’s centre of gravity. As a result, only measurements performed on observations obtained close to the same rotation phase can be compared. The ESPaDOnS data contains two observations obtained close to phase 0.6, separated by about 3 yr (one on 14/06/2014, the second on 14/05/2017, with a difference in phase of 0.02 cycles when phased using the variable ephemeris). Measuring the centres of gravity of the LSD profiles extracted from these observations yields a difference in RV of 1 km s−1, comparable to the measurement uncertainty. The RV change expected over 3 yr if $\dot{P}$ is due to orbital motion is about 3 km s−1, so this test must be considered inconclusive. However, a change of −20 s over the 30 yr of observations should have led to ΔRV = 71 km s−1; it is unlikely that such a large change in RV would have been missed. The Pulkovo Compilation of Radial Velocities (Gontcharov 2006) give RV = −12 ± 3 km s−1, consistent with RVs measured from ESPaDOnS data (which have a mean and standard deviation of −4 and 5 km s−1), suggesting that the RV has been stable over a time span of at least a decade.
Citation: Shultz et al. 2018
Citation function: Uses