adsabs/FOCAL huggingface dataset:
In 2018, a deep spectral feature centred at 78 MHz was reported by the EDGES collaboration (Bowman et al. 2018). The feature was presented as the long sought-after 21-cm absorption feature seen against the CMB during the CD at z ∼ 17. The location of this putative absorption trough is consistent with redshift predictions from theoretical models and simulations of the CD (Furlanetto et al. 2006; Pritchard & Loeb 2010; Mesinger, Ferrara & Spiegel 2013; Cohen et al. 2017). However, the depth of the feature is ΔT21 ∼ 0.5 K ($99{{\ \rm per\ cent}}$ confidence level), which is two to three times stronger and considerably wider (Δν ∼ 19 MHz) than that predicted by the most optimistic astrophysical models (e.g. Pritchard & Loeb 2010; Fialkov, Barkana & Visbal 2014; Fialkov & Loeb 2016; Cohen et al. 2017). Moreover, the observed feature is flat-bottomed instead of a smooth Gaussian-like shape. Several ‘exotic’ theoretical models have already been proposed which might explain the depth of the feature, such as a considerably colder IGM due to interaction between baryons and dark matter particles causing a lower spin-temperature and therefore a deeper absorption feature (e.g. Barkana 2018; Fialkov, Barkana & Cohen 2018), or a stronger radiation background against which the absorption is taking place (e.g. Dowell & Taylor 2018; Ewall-Wice et al. 2018; Feng & Holder 2018; Fialkov & Barkana 2019). Although the 21-cm signal is expected to be stronger at these redshifts, the foreground emission is several times brighter at these frequencies compared to EoR 21-cm signal observations at 150 MHz (Bernardi et al. 2009, 2010). Moreover, ionospheric effects are amplified at lower frequencies (de Gasperin et al. 2018; Gehlot et al. 2018), rendering the measurement of the signal equally (or even more) challenging than in EoR experiments. As of now, Ewall-Wice et al. (2016) reported a systematics-limited power spectrum upper limit of $\Delta _{21}^2 \lt (10^4~\text{mK})^2$ on co-moving scales $k\lesssim 0.5~h\, \text{cMpc}^{-1}$ (in 3 h of integration) on the 21-cm signal brightness temperature in the redshift range 12 ≲ z ≲ 18 using MWA. This overlaps with the low-redshift edge of the 21-cm absorption feature (Bowman et al. 2018). Gehlot et al. (2019) provided a 2σ upper limit of $\Delta _{21}^2 \lt (1.4\times 10^4~\text{mK})^2$ on the 21-cm signal power spectrum at $k = 0.038~h\, \text{cMpc}^{-1}$ (in 14 h of integration) using the LOFAR-Low Band Antenna (LBA) system in the redshift range 19.8 ≲ z ≲ 25.2, which corresponds to the high-redshift edge of the absorption feature. More recently, Eastwood et al. (2019) used OVRO-LWA observations to report a 2σ upper limit of $\Delta _{21}^2 \lt (10^4~\text{mK})^2$ at $k \approx 0.1~h\, \text{cMpc}^{-1}$ (in 28 h of integration) at redshift z ≈ 18.4.
Citation: Fialkov & Loeb 2016
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The lifetime of molecular clouds (MCs) remains an active research topic in the study of the interstellar medium and star formation, and most recent studies, both observational and theoretical, place this lifetime at a few times 107 yr for clouds in the 105–106M⊙ mass range (e.g. Blitz & Shu 1980; Kawamura et al. 2009; Zamora-Avilés, Vázquez-Semadeni & Colín 2012; Zamora-Avilés & Vázquez-Semadeni 2014; Lee, Miville-Deschênes & Murray 2016). In addition, several observational studies have suggested that the star formation rate (SFR) of the clouds appears to increase over their lifetimes. For example, studies of young clusters embedded in moderate-mass MCs (∼104M⊙) (e.g. Palla & Stahler 1999, 2000; Da Rio et al. 2010) have shown that their age histograms contain a large majority of young (1–2 Myr) objects, but also a tail of older (up to several Myr) ones suggesting an accelerating star formation activity, sometimes followed by a subsequent decline (see also Povich et al. 2016; Schneider et al. 2018). In addition, Kawamura et al. (2009) reported a clear evolutionary process over the lifetime of giant molecular clouds (GMCs; of masses ∼105–106M⊙) in the Large Magellanic Cloud, evidenced by the increasing number of massive stars across the sequence of GMC ‘classes’ proposed by those authors. Finally, on the basis of the large scatter in the observed star formation efficiency (SFE) in Milky Way GMCs, Lee et al. (2016) have concluded that the SFR in those clouds must also be time variable. Numerical simulations of MC formation and evolution also exhibit time varying, increasing SFRs during their early stages (e.g. Vázquez-Semadeni et al. 2007; Hartmann, Ballesteros-Paredes & Heitsch 2012). Also, in the presence of stellar feedback, at late times the SFRs reach a maximum and begin to decrease again (e.g. Vázquez-Semadeni et al. 2010; Colín, Vázquez-Semadeni & Gómez 2013). Vázquez-Semadeni, González-Samaniego & Colín (2017) have recently shown that the simulations of Colín et al. (2013) in fact produce stellar age histograms highly resemblant of the observed ones (Palla & Stahler 1999, 2000; Da Rio et al. 2010), and reproduce observed radial age gradients in clusters (Getman et al. 2014) as well as bottom-heavy stellar initial mass functions (IMFs) in scattered regions of massive star formation (Povich et al. 2016).
Citation: Schneider et al. 2018
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Citation: Palla & Stahler; 2000
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Citation: Palla & Stahler; 2000
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Citation: Palla & Stahler; 2000
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Citation: Palla & Stahler; 2000
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Still unconstrained are the mechanisms that are able to modify both SF and morphology and their corresponding time-scales. Several hypotheses have been proposed to explain the quenching of the SF in blue galaxies, such as gas stripping (e.g. Gunn & Gott 1972), morphological or gravitational quenching (Martig et al. 2009; Genzel et al. 2014), shock heating of infalling cold gas by the hot halo (Dekel & Birnboim 2006) or an exhaustion of the gas supply (e.g. Larson, Tinsley & Caldwell 1980). Moreover, in massive galaxies, the role of AGNs in influencing galaxy evolution and quenching SF is supported by several observations (Hopkins et al. 2005; Kaviraj et al. 2007; Diamond-Stanic et al. 2012; Fabian 2012, and references therein, Cimatti et al. 2013; Cicone et al. 2014; Förster Schreiber et al. 2014) and corroborated by the theoretical results obtained combining N-body simulations of dark matter haloes evolution (Springel et al. 2005; Boylan-Kolchin et al. 2009) with semi-analytic models for galaxy formation (White & Frenk 1991; Springel et al. 2005; Lu et al. 2011; Benson 2012). However, other models are capable of forming rapidly quiescent galaxies without invoking the AGN feedback (e.g. Khochfar & Silk 2006; Naab, Khochfar & Burkert 2006, 2009; Johansson, Thomas & Maraston 2012). Stellar or supernova (SN) feedback is most likely channel for the SF quenching in low-mass galaxies (e.g. 1010 M⊙; Kaviraj et al. 2007). Several mechanisms have also been invoked to explain the morphological transformation. Numerical simulations have shown that major merging can give rise to elliptical and S0 galaxies (Bekki 1998) and that also minor merging can play an important role in spheroid and bulge growth (Bournaud, Jog & Combes 2007; Naab et al. 2007). From an observational point of view, evidence that the morphological transformation can also be induced by environmental mechanisms (Larson et al. 1980; Farouki & Shapiro 1981; Moore et al. 1999; Quilis, Moore & Bower 2000; ; Bekki, Couch & Shioya 2002) or by the secular growth of pseudo-bulges (Courteau, de Jong & Broeils 1996; Norman, Sellwood & Hasan 1996; MacArthur, Courteau & Holtzman 2003; Kormendy & Kennicutt 2004; Debattista et al. 2006) has been found.
Citation: Diamond-Stanic et al. 2012
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Several consortia have been actively approaching the question based on large photometric and spectroscopic surveys such as 2dF and SDSS, covering large sky areas and redshift ranges (Lewis et al. 2002; Balogh et al. 2004; Kauffmann et al. 2004; Poggianti et al. 2017). All these efforts have found indisputable evidence for the impact of environment in galaxy evolution (and references therein; Boselli & Gavazzi 2006, 2014; Cortese et al. 2021). However, understanding the role played by different physical mechanisms exerted under diverse environment conditions constitutes the matter of a very active debate. The involved physical mechanisms are classified in two types: hydrodynamic and gravitational. Hydrodynamic effects concern the stripping of cold/warm interstellar gas (H i and H2) by the hot intracluster medium (ICM). The ram-pressure stripping (RPS, Gunn & Gott 1972) and the viscous stripping (Nulsen 1982) are the most studied cases. On the other side, we have the tidal (gravitational) mechanisms occurring between a galaxy and the cluster potential (Byrd & Valtonen 1990) or among neighbour galaxies (Merritt 1983; Barnes & Hernquist 1996; Walker, Mihos & Hernquist 1996). These include major mergers, accretion of low-mass satellites, and the accumulation of fast speed encounters between galaxies (the galaxy harassment, Moore, Katz & Lake 1996). The removal of the galaxy halo gas, known as galaxy starvation (e.g. Larson, Tinsley & Caldwell 1980), is predicted to occur either by hydrodynamic or gravitational interactions. Most of these mechanisms are predicted to transform a spiral galaxy into an S0, and it is known that more than one process might act simultaneously on a single galaxy. The pre-processing of galaxies occuring within groups infalling towards clusters and seems to be particularly important (Donnari et al. 2021). Groups of galaxies are known to have lower velocity dispersions than clusters, allowing slower and deeper tidal interactions among their members. Several authors (Fadda et al. 2008; Poggianti et al. 2009b) provided substantial evidence that strong galaxy evolution is occurring in low-mass systems at a large distance from the cluster core. However, the debate pre-processingversuscluster effects remains open because many variables are involved, such as the infalling orbits, initial gas/stellar masses, the group/cluster properties, and even the surrounding large-scale structure (Rhee et al. 2020; Salerno et al. 2020).
Citation: Barnes & Hernquist 1996
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But if, as our studies in this Letter indicate, microstreams might be the result of accumulated and persistent velocity enhancements resulting from a series of switchbacks, then it could be that individual switchbacks result from coronal jets, and the microstreams are a consequence of a series of such jet-driven switchbacks occurring in close succession. Thus, this would be a modification of the idea put forth by Neugebauer (2012) whereby a series of minifilament eruptions capable of producing coronal jets could accumulate and generate a microstream. In fact, homologous jets, continuing for hours at a time, have been commonly observed (e.g., Chifor et al. 2008; Cheung et al. 2015; Panesar et al. 2016a, 2016b; Sterling et al. 2017; Joshi et al. 2017; Paraschiv & Donea 2019; Moore et al. 2021). Under the minifilament-eruption scenario, the multiple minifilament/flux ropes would be ejected as long as the cancellation continues (Panesar et al. 2016a; Sterling et al. 2017). A swarm of such homologous jets, produced over a several-hour time period, conceivably could account for a microstream peak. Additionally, there is some recent evidence (Bale et al. 2021; Fargette et al. 2021) that switchbacks have an extent similar to the scale size of supergranules (∼30,000 km). Measurements of the lengths of the erupting minifilaments that produce jets range from ∼8000 km (Sterling et al. 2015) to ∼18,000 (Panesar et al. 2016a), and thus of similar order to (albeit somewhat smaller than) a typical supergranule diameter. Fargette et al. (2021) also found switchbacks to occur on another size scale, one that approximately corresponds to the size of photospheric granules, ∼1000 km. Chromospheric spicules have widths of some fraction of this size, and thus their observation could be consistent with some spicules resulting from the minifilament-eruption-jet mechanism as suggested in Sterling & Moore (2016), and then those spicules making smaller-scale switchbacks as suggested in Sterling & Moore (2020).
Citation: Sterling et al. 2017
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Mid-infrared and millimeter polarimetric observation has so far been considered as the best method to probe the magnetic field. This is because if aspherical grains in disks become aligned with the magnetic field as is the case in the interstellar medium (ISM), the polarization vector arising from thermal emission of the aligned grains becomes perpendicular to the local magnetic field line (Cho & Lazarian 2007, henceforth CL07, Matsakos et al. 2016; Yang et al. 2016b; Bertrang et al. 2017). At mid-infrared wavelengths, Li et al. (2016) performed a polarimetric imaging observation of AB Aur using CanariCam. As a result, they detected a centrosymmetric polarization pattern, and the degree of polarization was as high as 1.5% at large radii. At millimeter wavelengths, polarimetric observations of disks have been performed (e.g., Hughes et al. 2009, 2013; Rao et al. 2014; Stephens et al. 2014; Cox et al. 2015; Kataoka et al. 2016b). Polarized emission from a circumstellar disk has been detected in the Class 0 phase (Rao et al. 2014; Cox et al. 2015). More evolved disks do not show a degree of linear polarization larger than 0.5% (Hughes et al. 2009, 2013). It should be mentioned that Stephens et al. (2014) detected polarized flux from HL Tau, which is classified as a Class I-II, with an average degree of linear polarization of 0.9%. More recently, Kataoka et al. (2016b) reported the first submillimeter polarization observation of a disk obtained with ALMA, and they clearly detected polarized flux from HD 142527. The polarization fraction at a peak position of polarized intensity was 3.26%, and the maximum polarization fraction was as high as 13.9%. The disk reveals radial polarization vectors; however, they flip by 90° in its northeast and northwest regions. In addition, the detected polarization fraction is much larger than the stringent limit set by Hughes et al. (2009, 2013), and further polarimetric observations by ALMA will reconcile this discrepancy.
Citation: Hughes et al.; 2013
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We attempted to find a correlation between the mean magnetic field and the outflow and minor axis of the cloud CB 17. Relative orientations between various quantities of CB 17 are presented in the first row of Table 6, along with a comparative study of the same for some dark clouds. The first column gives the cloud ID and columns 2–6 give the position angles of the mean magnetic field at the envelope ($\lt \theta ^{\rm env}_B\gt $), mean magnetic field at the core ($\lt \theta ^{\rm core}_B\gt $), outflow axis (θout), minor axis (θmin) of the core of the cloud and Galactic plane (θGP), respectively. $\lt \theta ^{\rm env}_B\gt $ of CB 17 is found to be almost aligned along the Galactic plane over that region of the sky (column 7), which indicates the dominance of the Galactic magnetic field over the envelope magnetic field of the cloud and thus we cannot infer much about the magnetic field structure from the optical study. A similar feature has also been observed in the cases of CB 34 (Das et al. 2016), L328, L673-7 (Soam et al. 2015), CB 26 (Halder et al. 2019), CB 3 and CB 246 (Ward-Thompson et al. 2009). However, $\lt \theta ^{\rm core}_B\gt $ of CB 17 (obtained by submm polarimetry) turned out to be perpendicular to $\lt \theta ^{\rm env}_B\gt $ (column 8); a similar phenomenon has been observed in the case of CB 34-C1 (Das et al. 2016), IRAM 04191 (Soam et al. 2015) and CB 54 (Wolf et al. 2003). Since, in the case of CB 17, $\lt \theta ^{\rm env}_B\gt $ is along the Galactic plane orientation, this implies that only $\lt \theta ^{\rm core}_B\gt $ (denser region) is linked with the ongoing physical phenomena in the cloud. $\lt \theta ^{\rm core}_B\gt $ is oriented perpendicular to θGP as well (column 9) and a similar orientation has been observed in the case of CB 34-C1 (Das et al. 2016), IRAM 04191 (Soam et al. 2015), CB 230 and CB 244 (Wolf et al. 2003) as well. Moreover, $\lt \theta ^{\rm core}_B\gt $ is found to be almost aligned along the minor axis of the core of the cloud; the angular offset is nearly 5.9° (column 10). The alignment of $\lt \theta ^{\rm core}_B\gt $ with the minor axis of the cloud fits the magnetically regulated star formation model, in which the magnetic field should lie along the minor axis of the cloud (Mouschovias & Morton 1991; Li 1998), and the same feature has also been observed for the clouds CB 34-C1 (Das et al. 2016) and IRAM 04191 (Soam et al. 2015). The angular offset between $\lt \theta ^{\rm core}_B\gt $ and the outflow axis is found to be 80.9° (column 11), that is, the core-scale magnetic field is oriented almost perpendicular to the outflow direction and a similar phenomenon has also been observed in the case of CB 34 (Das et al. 2016), CB 68 (Bertrang et al. 2014), B335, CB 230, CB 244 (Wolf et al. 2003) and CB 3 (Ward-Thompson et al. 2009). The angular offset between θout and θmin is found to be 75° and the same feature has been observed for CB 34-C1 (Das et al. 2016).
Citation: Ward-Thompson et al. 2009
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Citation: Ward-Thompson et al. 2009
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On larger scales, the clusters in which BCGs reside can generally be divided into two categories: cool core clusters, which exhibit very peaked surface brightness distributions at X-ray wavelengths, and non cool core clusters, with similar overall X-ray luminosities but with smoother, less peaked X-ray surface brightness distributions. Some authors (e.g. Hudson et al. 2010; Santos et al. 2010) define an intermediate category called moderate or weak cool core clusters. Since cool core clusters have short radiative cooling time-scales on the order of 108 yr in their centres (e.g. Voigt & Fabian 2004; McNamara & Nulsen 2007, 2012; Hlavacek-Larrondo et al. 2012), starbursts are expected to be common at the centre of such clusters. Indeed, the central cool gas in these clusters should condense onto the BCG, forming stars at rates of hundreds of solar masses per year (e.g. Fabian 1994). However, most BCGs are relatively quiescent and those that do show evidence of star formation generally tend to have star formation rates 1 order of magnitude smaller, on the order of $1-150 \, \mathrm{M_{\odot }\, {yr}^{-1}}$ (e.g. Donahue et al. 2007; Bildfell et al. 2008; O’Dea et al. 2008, 2010; Rawle et al. 2012). This mismatch between expected and observed star-forming rates, known as the cooling flow problem, is thought to be caused by active galactic nuclei (AGNs) feedback processes from the BCG. AGNs can release copious amounts of energy into the intracluster medium (ICM) through many ways, including: jetted outflows that inflate cavities, weak shocks, sound waves, or turbulence in the ICM (e.g. Markevitch & Vikhlinin 2007; McNamara & Nulsen 2007, 2012; Zhuravleva et al. 2014; Fabian et al. 2017). Alone, the energy released by jetted outflows appears to be on the same order as the energy needed to offset cooling (e.g. Rafferty et al. 2006; McNamara & Nulsen 2007; Hlavacek-Larrondo et al. 2012), therefore suggesting that AGN feedback is a good candidate for solving the cooling flow problem.
Citation: McNamara & Nulsen 2007
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Citation: McNamara & Nulsen 2007
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Citation: McNamara & Nulsen 2007
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Comparison of 1D marginal posterior distributions over the parameters S8 ≡ σ8(Ωm/0.3)0.5, σ8 and Ωm, from DES Y3 data as well as other experiments, and consistency tests for this work (in blue). (a) Constraints obtained from the harmonic (this work) and real (Amon et al. 2022; Secco et al. 2022) space analyses of DES Y3 data are shown in blue and green (see also Fig. 14), both with and without shear ratio information (SR; Sánchez et al. 2021). (b) Constraints from other weak lensing surveys, namely HSC Y1 (Hikage et al. 2019; Hamana et al. 2020, 2022b), KiDS-1000 (Asgari et al. 2021), and KiDS-450 (Hildebrandt et al. 2017; Köhlinger et al. 2017) are shown in grey, and constraints from cosmic microwave background observations from Planck 2018 are shown in yellow (Planck Collaboration VI 2020). (c) Constraints from four weak lensing analyses of DES Y3 data are compared, including the analysis of mass map moments (Gatti et al. 2021b) and peaks (Zürcher et al. 2022), and illustrating a high level of consistency (see also Fig. 15). (d) Consistency tests where redshift bins are removed one at a time (first four) and where the data vector is split into its large- and small-scale data points (last two) (see also Appendix C). (e) Various other consistency tests: removing autopower spectra, swapping the covariance matrix, and marginalizing over redshift distribution uncertainties with HyperRank and MultiRank(see also Appendix C). (f) Modelling robustness test for intrinsic alignment (IA), including B-mode power spectra, or replacing TATT by NLA, or removing IA contributions altogether (see also Section 6.2, Fig. 12). (g) Other robustness test, freeing the dark energy equation-of-state w or fixing the neutrino mass to 0.06 eV. (h) Baryonic feedback tests where the matter power spectrum is computed with HMCode instead of HaloFit, and fiducial scale cuts are replaced with kmax = 1, 3, and 5 $h\, {\rm Mpc}^{-1}$ scale cuts (see also Section 6.3 and Fig. 13).
Citation: Hamana et al.; 2022b
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