adsabs/FOCAL huggingface dataset:
The exploration of the Universe out to times earlier than the point of complete reionization is rapidly advancing. One of the most informative probes of these epochs is the 21-cm line produced by hydrogen atoms in the neutral intergalactic medium (IGM) at redshifts z > 6. This line redshifts to frequencies below 200 MHz and can be detected by low-frequency radio telescopes. Global 21-cm experiments measure the spectrum of this line averaged over the sky. The first tentative detection of the Cosmic Dawn signal was recently made by the Low-Band implementation of the Experiment to Detect the Global EoR Signature (EDGES, Bowman et al. 2018). Other global 21-cm experiments, including the Large-Aperture Experiment to Detect the Dark Ages (Bernardi et al. 2016; Price et al. 2018), the EDGES High-Band (Bowman & Rogers 2010; Monsalve et al. 2017, 2018, 2019), and the Shaped Antenna measurement of the background RAdio Spectrum (Singh et al. 2017, 2018), provide upper limits on the signal from Cosmic Dawn and the Epoch of Reionization (EoR), ruling out some extreme astrophysical scenarios. A parallel effort is being made by interferometric radio arrays that are placing upper limits on the fluctuations of the 21-cm signal, including the Donald C. Backer Precision Array for Probing the Epoch of Reionization (Kolopanis et al. 2019), the Low Frequency Array (LOFAR, Patil et al. 2017; Gehlot et al. 2019; Mertens et al. 2020), the Giant Metrewave Radio Telescope (Paciga et al. 2013), the Murchison Widefield Array (Beardsley et al. 2016; Barry et al. 2019; Li et al. 2019; Trott et al. 2020), and the Owens Valley Radio Observatory Long Wavelength Array (Eastwood et al. 2019). The most recent upper limit reported by LOFAR (Mertens et al. 2020) made it possible to place (weak) upper limits on the temperature of the neutral gas and ionization state of the Universe at z = 9.1 (Ghara et al. 2020; Mondal et al. 2020). Upcoming arrays, including the Hydrogen Epoch of Reionization Array (DeBoer et al. 2017), the Square Kilometer Array (Koopmans et al. 2015), and the New Extension in Nancay Upgrading LOFAR (Zarka et al. 2012), will provide measurements of the power spectrum over a wide range of scales and redshifts.
Citation: Bernardi et al. 2016
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It was soon realized that these frequencies are probably related to oscillations of the neutron star, and several groups tried to identify them as elastic oscillations of the crust (Duncan 1998; Messios et al. 2001; Strohmayer & Watts 2005; Piro 2005; Sotani et al. 2007; 2013, 2016; Samuelsson & Andersson 2007, 2009; Steiner & Watts 2009; Deibel et al. 2014), Alfvén oscillations (Cerdá-Durán et al. 2009; Sotani et al. 2008; Colaiuda et al. 2009), or coupled magneto-elastic oscillations (Levin 2006; 2007; Glampedakis et al. 2006; Gabler et al. 2011; 2012; Colaiuda & Kokkotas 2011; van Hoven & Levin 2011, 2012). The theoretical models based on the observed frequencies are very elaborate and may be able to constrain properties of high-density matter as found in the interior of neutron stars. Some of the models, for instance, require a superfluid component in the core of the star (van Hoven & Levin 2011, 2012; Glampedakis et al. 2011; Passamonti & Lander 2013, 2014; Gabler et al. 2013; 2016, and in prep.). Different models depend sensitively on the identification of the fundamental oscillation frequency, and may not explain all of the observed frequencies. Even when the fundamental frequency is identified, the interpretation and parameter estimation is not yet straightforward because of degeneracies in the parameter space. However, keeping other stellar parameters fixed, some general trends of the fundamental oscillation frequency can be summarized as follows (see Gabler et al. 2016, and in prep. for a detailed discussion): i) The frequency scales linearly with the magnetic field strength. ii) It decreases with increasing compactness (Sotani et al. 2008). The compactness is related to the hardness of the equation of state (EOS): Material with a stiff equation of state is harder to compress, leading to larger radii and hence lower compactnesses. iii) It can only reach the surface for significantly strong magnetic fields 
$\bar B\gtrsim\bar
B_\text{outbreak}(\sqrt{c_s})$

B̄≳B̄outbreak(cs)


, whose thresholds depend on the square root of the shear velocity (Gabler et al., in prep.).
Citation: Glampedakis et al. 2006
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The polarimetric observations with ALMA at 0.87 mm toward B335 were conducted during 2016 to 2018, consisting of 13 successful executions (project code: 2015.1.01018.S). In the observations, 40–47 antennae were used in the configurations with baseline lengths ranging from 15 to 1400 m. The pointing center was α(J2000) = 19h37d00.s89, δ(J2000) = +7°34′096. The on-source integration time was 7.4 hr. The observations were conducted with the full polarization mode and at the frequency ranges of 335.5–339.5 GHz and 347.5–351.5 GHz with a total bandwidth of 8 GHz. In these observations, J1751−0939 was observed as the bandpass calibrator, J1938 + 0448 or J1935 + 2021 as the gain calibrators, and J1924−2914 or J2000−1748 for polarization calibration. The flux calibration was performed with the observations of quasars or the asteroid, Pallas. The data were manually calibrated by the EA ARC node using the Common Astronomy Software Applications (CASA) of the version 5.1.1 (McMullin et al. 2007). We additionally performed self-calibration of the phase using the Stokes I data. Then the calibrated visibility data were Fourier-transformed with the Briggs weighting with a robust parameter of +0.5 to generate Stokes IQU images, and the images were cleaned using the CASA task tclean. The achieved synthesized beam is 019 × 017. The noise level in the Stokes I image is 40 μJy beam−1, and it is 9 μJy beam−1 in the Stokes Q and U images. When we generated the polarized intensity (Ip) map, we debiased the polarized intensity (Ip) with 





, where σQ, U is the noise level in Stokes Q and U (Wardle & Kronberg 1974; Simmons & Stewart 1985). To extract polarization detections, we first binned up the Stokes IQU and Ip maps to have a pixel size of 01, which is approximately half of the beam size, and computed polarization orientations and fractions. Thus, the minimal separation between two polarization detections is 01. The Stokes I and Ip maps with their original pixel size of 002 are presented below. The polarization detections are extracted when the signal-to-noise ratios (S/N) of both Stokes I and Ip are larger than three, and thus the expected uncertainties in the polarization orientations are ≲9°.
Citation: McMullin et al. 2007
Citation function: Uses
We fit the total foreground absorption column by applying two-component multiplicative absorption models, tbabs and tbnew.
8


8

https://pulsar.sternwarte.uni-erlangen.de/wilms/research/tbabs/
 The absorption component tbabs accounts for the Galactic absorption column N
H,Gal, and it is fixed at N
H,Gal = 6 × 1020 cm−2 (Dickey & Lockman 1990). The second absorption component, tbnew, accounts for the LMC absorption column, N
H,LMC and is varied in the fits. We use tbnew as the absorption column for LMC as it allows us to set individual elemental abundances associated with N
H,LMC at their respective LMC values. Recent measurements of the LMC abundances based on the X-ray spectral analysis of SNRs (Maggi et al. 2016; Schenck et al. 2016) suggest ∼50% lower LMC abundance values for O, Ne, Mg, and Fe than the previously estimated values (Hughes et al. 1998). Our derived shock parameters are consistent (within statistical uncertainties), assuming either set of these LMC abundances in tbnew. We tie N
H,LMC among all the fitted spectra, assuming no temporal variation in the LMC column for SNR 1987A. We obtain a best-fit total absorption column density, 



NH=NH,Gal+NH,LMC=2.17−0.22+0.22×1021cm−2

. This value is comparable to estimates of N
H obtained by other X-ray analyses: 



2.35−0.08+0.09×1021cm−2

 (Park et al. 2006), 



1.44−0.12+0.16×1021cm−2

(Zhekov et al. 2009), and 



2.60−0.05+0.05×1021cm−2

 (Alp et al. 2021). We note that more recent H i surveys have suggested much higher N
H,Gal values toward the LMC, i.e., ∼(2.5–4) × 1021 cm−2 (Kalberla et al. 2005; Willingale et al. 2013; HI4PI Collaboration et al. 2016). By adopting these high Galactic columns, the overall fits are equally good, but this results in a negligible LMC column. The implied negligible LMC column does not appear to be reasonable because optical extinction estimates show that the LMC contribution is greater than the Milky-Way contribution (Fitzpatrick & Walborn 1990; France et al. 2011). A detailed analysis of the contribution of the Galactic N
H toward total absorption column density is beyond the scope of our work. While this issue was similarly outlined by Alp et al. (2021) in relation to their analysis of the XMM-Newton data of SNR 1987A, we note that the total columns (Galactic + LMC) are generally consistent between either values of the Galactic column, and thus that the best-fit parameters in our spectral model fits (i.e., electron temperatures, ionization age, abundances, and volume-emission measures) are not significantly affected (within statistical uncertainties).
Citation: Hughes et al. 1998
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The B/T ratio of a galaxy is the fraction of total luminosity contributed by the bulge component of the galaxy. Bulges are the central component of disc galaxies which appear as central bright cores in galaxy images or as excess-light over the disc light in the inner region of galaxy light profiles. The B/T ratio is an important indicator of galaxy structure and is well correlated with several other physical quantities of interest in studies of galaxy evolution, such as galaxy morphology (Graham & Worley 2008), kinematics (Cappellari et al. 2013), stellar mass, and star formation rate (Bluck et al. 2014). The B/T ratio also directly determines the bulge luminosity which in turn correlates with the mass of the central supermassive black hole of the galaxy (Kormendy & Richstone 1995; Marconi & Hunt 2003; Kormendy & Ho 2013). Galaxies having different B/T ratios are thought to have undergone evolution along different evolutionary paths. A high value of B/T of a galaxy generally indicates its evolutionary history dominated by galaxy mergers (Hopkins et al. 2010). On the other hand, majority of low B/T systems are often pseudobulge hosting galaxies (Fisher & Drory 2008; Gadotti 2009) which are thought to undergo slow evolution through internal, secular evolution processes (Kormendy & Kennicutt 2004). It must be noted that unlike visual morphology which is a qualitative measure, the B/T ratio is a quantitative measure of the galaxy morphology. It is the only reliable way to separate ellipticals and disc galaxies when morphological features such as spiral arms cannot be resolved by the telescope (due to resolution or sensitivity limitations, particularly at high redshifts). The quantitative nature of the B/T ratio also allows direct comparison of galaxies with their counterparts in cosmological simulations. One can study these simulated galaxies to understand the origin of various properties of real galaxies. All these factors make the B/T ratio an important parameter to measure in studies of galaxy formation and evolution.
Citation: Bluck et al. 2014
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Supermassive black holes (SMBH) with masses M• ∼ 109–1010





 are recognized to be present at redshifts as high as z ≃ 6–7.5, when the universe was 650–800 Myr young (Fan et al. 2003; Willott et al. 2010; Mortlock et al. 2011; Bañados et al. 2014, 2018; Wu et al. 2015; Decarli et al. 2018; Izumi et al. 2019)—in total more than 150 such SMBHs are already known (see, e.g., Fan et al. 2019). Their origin remains elusive—it is unclear how massive their seeds were, how efficient their growth rate was, and what was the mass reservoir for their growth. To fit the existence of the quasars J0100 + 2802 (z = 6.33), J1120 + 0641 (z = 7.09), and J1342 + 0928 (z = 7.54) with SMBH masses M• = 1.2 × 1010,  2 × 109, and 7.8 × 108





 respectively, one has to assume that their masses grow as M• = M•,0 exp [t/(47 Myr)], corresponding to the standard Eddington limit with a 10% radiative efficiency , the Salpeter growth time tS = cσT/(4πGmp) = 47 Myr, and the seed mass M•,0 ≥ 103





 at z ≥ 40 (Bañados et al. 2018). In this scenario SMBHs have to begin growing even earlier than the very first stars are assumed to have appeared (see discussion in Barkana & Loeb 2001). Moreover, it suggests that the accretion is tightly tuned to the Eddington rate, which seems physically unlikely (see discussion in Haiman & Loeb 2001; Volonteri & Rees 2005; Haiman 2013; Alexander & Natarajan 2014; Madau et al. 2014). Lower-mass black holes with M• ∼ 100 





, originated from Population III stars, are apparently unlikely to serve as seeds for growing SMBHs, because photoionization and photoheating from their massive progenitors strongly suppress further supply of cold mass onto the BH (Johnson & Bromm 2007), and would require even more time for the black hole to grow. Note, however, that this channel for SMBH seeds is currently widely discussed (see references in Natarajan et al. 2019). Also note that this problem of the presence of such enormously massive BHs in a younger than 1 Gyr universe can be to a certain extent eased when possible magnification of z > 6 SMBHs due to gravitational lensing is considered (Fan et al. 2019; Pacucci & Loeb 2019a, 2019b). Recent millimeter observations of the quasar J0100 + 2802 (z = 6.33) with the most massive BH, M• ∼ 1.2 × 1010





, known at z > 6, indicate strong lensing with a magnification factor of ∼450 (Fujimoto et al. 2019). As a result, the estimate of the SMBH mass may be reduced by more than an order of magnitude, though this still remains exceedingly large for a 1 Gyr universe ∼109





 (Fujimoto et al. 2019). The fraction of so strongly magnified quasars is fairly low considering the very small typical angular size of such lenses, as a rule ≪1″ (see, e.g., Pei 1995; Bolton et al. 2008; Pacucci & Loeb 2019b).
Citation: Barkana & Loeb 2001
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Galaxy interactions represent a fundamental component of our current view of hierarchical galaxy evolution. Studies based on both observations and simulations have shown that galaxy collisions and mergers can dramatically affect the galaxies undergoing the interaction, by, e.g. triggering nuclear activity (e.g. Kennicutt 1984; Kennicutt et al. 1987; Ellison et al. 2011, 2013a; Silvermann et al. 2011; Satyapal et al. 2014), producing colour changes (e.g. Larson & Tinsley 1978; Darg et al. 2010; Patton et al. 2011), disrupting morphologies (e.g. Kaviraj et al. 2011; Patton et al. 2016; Lofthouse et al. 2017), and altering the metallicities (e.g. Rupke et al. 2010; Perez, Michel-Dansac & Tissera 2011; Scudder et al. 2012; Torrey et al. 2012). The most evident effect driven by galaxy encounters is probably the triggering of new episodes of star formation, which can occur both in the pre-merger regime between first pericentre and coalescence (e.g. Nikolic, Cullen & Alexander 2004; Ellison et al. 2008, 2013b; Patton et al. 2011; Scudder et al. 2012), and in the post-merger phase, where the two nuclei of the interacting galaxies have merged together (e.g. Kaviraj et al. 2012; Kaviraj 2014; Ellison et al. 2013a). The idea that galaxy mergers have a strong impact on the star formation activity is supported by studies of Ultra-Luminous InfraRed Galaxies (ULIRGs), i.e. galaxies with IR luminosities exceeding 1012 L⊙ and characterized by star formation rates (SFRs) up to ∼1000 M⊙ yr−1 (e.g. Barnes & Hernquist 1991; Mihos & Hernquist 1994; Daddi et al. 2010; Scoville et al. 2015). Observations have revealed that the majority of ULIRGs reside in interacting systems (e.g. Sanders & Mirabel 1996; Veilleux, Kim & Sanders 2002; Kartaltepe et al. 2010, 2012; Haan et al. 2011). Nevertheless, ULIRGs are rare and extreme examples of highly star-forming galaxies. Most galaxy–galaxy interactions result in SFR increases of at most a factor of a few, as shown in both numerical simulations (e.g. Di Matteo et al. 2008) and observations of galaxy pairs and post-mergers (Ellison et al. 2008; Martig & Bournaud 2008; Jogee et al. 2009; Robaina et al. 2009; Scudder et al. 2012).
Citation: Nikolic, Cullen & Alexander 2004
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Citation: Patton et al. 2016
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Detections of ionic, atomic, and molecular species in exoplanetary atmospheres serve as a unique and strong diagnostic of those chemical and dynamical processes driving their formation and evolution. Their detection and abundance measurements could act as indicators of planetary formation scenarios and reveal connections to the primordial protoplanetary disc and the host star (Williams & Cieza 2011; Mordasini et al. 2016; Madhusudhan et al. 2017). Furthermore, discoveries of atmospheric chemical species allow us to better understand various thermodynamical processes and chemistry, winds in the upper atmosphere (Goodman 2009; Snellen et al. 2010; Brogi et al. 2016; Madhusudhan et al. 2016; Wyttenbach et al. 2020), and to probe planetary interiors and various bulk properties through their abundances (Kite et al. 2016; Thorngren & Fortney 2019; Madhusudhan et al. 2020). A whole host of ions, atoms, and molecules have been detected through a variety of, often complementary, techniques, such as differential spectrophotometry using low-to-mid resolution spectroscopy (e.g. Gibson et al. 2012, 2017; Deming et al. 2013; Kreidberg et al. 2014; Kirk et al. 2016; Nortmann et al. 2016), and high resolution spectroscopic techniques (e.g. Redfield et al. 2008; Snellen et al. 2008; Rodler, Lopez-Morales & Ribas 2012; Birkby et al. 2013; Hoeijmakers et al. 2015, 2018, 2020; Brogi et al. 2016; Birkby et al. 2017; Žák et al. 2019; Ehrenreich et al. 2020). To date, ionic species such as Fe ii and Ti ii (Hoeijmakers et al. 2019), atomic absorption from Na, K, H α, and He (e.g. Redfield et al. 2008; Sedaghati et al. 2016; Casasayas-Barris et al. 2017; Spake et al. 2018; Chen et al. 2020; Seidel et al. 2020), and molecules such as H2O, CH4, and CO (e.g. Konopacky et al. 2013; Brogi et al. 2014; Fraine et al. 2014; Barman et al. 2015; Sing et al. 2016) have been detected through the aforementioned techniques. Needless to say that this list of detected constituents is by no means exhaustive, nor that of methods employed to detect exoplanetary atmospheres. For instance, high-resolution imaging instruments such as SPHERE (Beuzit et al. 2019) and GRAVITY (Gravity Collaboration et al. 2017), both at the VLT (ESO’s Very Large Telescope), through combination with low-dispersion spectroscopy, have facilitated direct measurements of exoplanetary atmospheres (Samland et al. 2017; Gravity Collaboration et al. 2020).
Citation: Deming et al. 2013
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Citation: Goodman 2009
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Citation: Hoeijmakers et al.; 2020
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Galactic BHXBs offer excellent laboratories in which to study jet interaction zones, as their jets evolve on day to month time-scales, they are located at nearby distances, and they are thought to be good analogues for AGNs. Given their incredible diagnostic potential, over the last couple of decades there have been many observational campaigns searching for these highly sought after interaction sites near BHXBs. To date, several candidate jet–ISM interaction sites have been identified; SS 433 (Dubner et al. 1998), Cygnus X–1 (Gallo et al. 2005; Russell et al. 2007), 1E 1740–2942 (Mirabel et al. 1992), GRS 1758−258 (Martí et al. 2002), GRS 1915+105 (Rodríguez & Mirabel 1998; Chaty et al. 2001; Kaiser et al. 2004), H1743–322 (Corbel et al. 2005), XTE J1550–564 (Corbel et al. 2002; Kaaret et al. 2003; Migliori et al. 2017), XTE J1748–288 (Brocksopp et al. 2007), GRO J1655–40 (Hjellming & Rupen 1995; Hannikainen et al. 2000), GX 339-4 (Gallo et al. 2004), 4U 1755–33 (Kaaret et al. 2006), XTE J1752–223 (Yang et al. 2010; Miller-Jones et al. 2011; Yang et al. 2011; Ratti et al. 2012), XTE J1650–500 (Corbel et al. 2004), XTE J1908+094 (Rushton et al. 2017), 4U 1630–47 (Neilsen et al. 2014; Kalemci, Maccarone & Tomsick 2018), LMC X–1 (Russell et al. 2006; Cooke et al. 2007; Hyde et al. 2017), and GRS 1009–45 (Russell et al. 2006). From these past works, it is clear that finding and confirming interaction sites can be incredibly difficult and often observationally expensive (e.g. requiring deep, wide-field radio continuum observations). This difficulty mainly results from the fact that interaction sites can manifest with a wide variety of morphologies and emission properties, likely dependent on the properties of the BHXB (e.g. space velocity; Miller-Jones et al. 2008; Heinz et al. 2008; Wiersema et al. 2009) and/or local ISM properties (e.g. density; Heinz 2002; Kaiser et al. 2004). Once identified, detailed calorimetric calculations of interaction sites are highly sensitive to the properties of the ISM (i.e. density, kinetic temperature, shock velocity; Russell et al. 2007; Sell et al. 2015), and thus require accurate constraints on the physical conditions in the interacting gas, which cannot be derived with continuum observations alone. Therefore, developing and implementing new methods that allow us to identify and place improved observational constraints on these parameters at multiple interaction sites, is crucial for taking full advantage of the diagnostic potential of these regions.
Citation: Migliori et al. 2017
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