adsabs/FOCAL huggingface dataset:
One of the important findings of the COS-Halos survey is that 





 is omnipresent in the halos of SF galaxies which harbor a major reservoir of galactic metals (i.e., Tumlinson et al. 2011a). Outflows from the SF galaxies are thought to be the origin of highly ionized oxygen in the CGM. However, the outflows need not be active at the time of observations. Active outflows that are detected primarily via low-ions (e.g., 





, 





, 





) absorption in the spectra of host-galaxies (i.e., the “down-the-barrel” outflows) are also found to be ubiquitous at both high and low redshifts (Shapley et al. 2003; Martin 2005; Rupke et al. 2005; Veilleux et al. 2005; Rubin et al. 2010, 2014). Despite the ubiquity of winds, several basic questions that are key to quantifying galaxy feedback remain unanswered. How far do they propagate? What is the baryon mass that are processed through them? What is the mass-flow rate through winds? What are the mass loading factors and kinetic power of the winds? To what degree are metals processed through them? This is essentially because the location of the outflow with respect to host-galaxy is an unknown in “down-the-barrel” outflows. Galaxy outflow probed by a background QSO has the advantage of having the minimum distance between host-galaxy and outflowing material. For example, by analyzing a post-starburst outflow from a galaxy at 





 detected in the spectra of QSO PG 1206+459 at an impact parameter of ∼68 kpc, Tripp et al. (2011) have shown that the entrained gas mass could be as large as ∼ few 





 Numerous strong and large velocity spread (





 km s−1) metal absorption lines along with solar to super-solar metallicities in different absorption components suggest that the absorber is tracing an active outflow. Such wide velocity spread 





 absorbers at low-z are also reported by Tumlinson et al. (2011b) and Muzahid (2014). In both these cases metallicity of the 





 bearing gas is low (e.g., 





) which suggest that 





 is probably tracing an “ancient outflow” (Ford et al. 2014) rather than an active wind. Interestingly, strong 





 is detected in PG 1206+459 which is not present in the latter two systems. We note that the location of the absorbing gas with respect to the host-galaxy’s projected major and minor axes are not known for any of these strong 





 absorbers.
Citation: Veilleux et al. 2005
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Citation: Martin 2005
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Theoretical and observational work argue for a strong correlation between the NIR size and the AGN luminosity (Suganuma et al. 2006; Kishimoto et al. 2011; Koshida et al. 2014; GRAVITY Collaboration 2019). In the standard picture, the NIR emission originates from the dust sublimation region, which is determined by the dust sublimation temperature Tsub. The temperature at which dust sublimates depends on the grain size and composition and ranges from Tsub ≈ 1500 K for silicate (Si) grains to Tsub ≈ 2000 K for graphite (C) grains (Baskin & Laor 2018). By assuming a grain size distribution typical for the interstellar medium (ISM), the mean sublimation distance can be calculated for graphite and silicate dust as 



R
≈

a
X

×

L

46


1
/
2




$ R\approx a_{\mathrm{X}}\times L_{46}^{1/2} $


 [pc] with aC = 0.5 and aSi = 1.3 (e.g. Barvainis 1987; Netzer 2015; for grain size dependence see Baskin & Laor 2018). However, there is evidence both from SED fitting (Mor & Netzer 2012) and from dust reverberation mapping (Kishimoto et al. 2007; Koshida et al. 2014) that, close to the AGN, one may find only larger graphite grains. Since, at the same distance from the AGN, large grains are cooler than small grains, one might naturally expect to find a distribution of large graphite grains at the smallest radii. In addition changes in the brightness of the AGN may lead to changes in the dust sublimation radius (Kishimoto et al. 2013) or instead, depending on the dust distribution, its temperature (Schnülle et al. 2015). As such, the sublimation radius is really rather a sublimation region according to grain size and species (e.g. Hönig & Kishimoto 2017; Baskin & Laor 2018). Empirical studies have found R2.2 μm ≈ 0.4 pc × (LAGN/1046 erg s−1)1/2 (assuming the V-band to Rsub relation from Koshida et al. 2014, and using the conversion LAGN ∼ 8L5500 Å from Netzer 2015), which corresponds to a good approximation to the sublimation radius of graphite. For NGC 1068 the observed relation from Koshida et al. (2014) predicts a dust sublimation radius in the range R2.2 μm ≈ 0.08–0.27 pc, for the luminosity range quoted in Table 1.
Citation: Barvainis 1987
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It is expected that the polarization properties produced by the thermal–nonthermal electrons have wide applications to many kinds of synchrotron sources. Here, we propose some examples. Covino et al. (2017) detected the low-level linear polarization of AT 2017gfo/GW170817 during the kilonova phase. Although the measured polarization seems to be due to the Galactic dust along the line of sight, we think that future synchrotron polarization detections for electromagnetic counterparts of gravitational wave events can be applied to further investigate compact object merger systems and constrain detailed physics of gravitational wave events. Fast radio burst (FRB) 140514 has the linear polarization limit of 





 and the circular polarization degree of 21 ± 7% (Petroff et al. 2015). The development of the radio polarization measurements on FRBs are carried out to reveal the FRB origin. Covino & Götz (2016) summarized the polarization observation of GRB prompt and afterglow emissions, and the GRB jet geometry related to the polarization was also mentioned. Steele et al. (2017) presented the optical polarization results of some GRBs, and the linear polarization degrees are relatively low (see also the recent report of Wiersema & Covino 2018 on GRB 180205A). Moreover, the circular polarization was detected in GRB 121024A (Wiersema et al. 2014). Spectropolarimetric detection of either supernova Ia (SN Ia) or core-collapse supernova was performed (Tanaka et al. 2012; Porter et al. 2016). The polarization properties can be helpful to further explore SN explosion mechanisms. Thermal electrons cannot be ignored in the celestial objects mentioned above, and we should consider the thermal electron contribution to the synchrotron polarization. Moreover, some objects, such as kilonova, GRB, and SN as mentioned above, have dense environments (D’Elia et al. 2007; Kasen et al. 2013; Tanaka & Hotokezaka 2013; Buckey et al. 2018; Yang et al. 2018). Polarization radiative transfer should be taken into account when we consider the polarization measurements in the optical and radio bands. In this paper, we do not attempt to reproduce these different polarization properties with exact modeling parameters. However, these polarization detections of the different populations encourage us to further explore the possibility of applying our scenario, when we assume that all of these kinds of sources are dominated by the synchrotron radiation.
Citation: Petroff et al. 2015
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Recent work by Tseliakhovich & Hirata (2010) showed that, at the time of recombination, baryons were moving in a coherent manner (on scales of ≲3 Mpc comoving) with respect to the dark matter and had an rms velocity of ∼30 km s−1. Since, on small scales, the baryons effectively displayed a streaming motion with respect to the dark matter component, their relative velocity is usually referred to as a ‘stream velocity’ in the literature, and we adopt this convention throughout this work. Tseliakhovich & Hirata (2010) also argued that, since shortly after recombination the temperature of the baryons (and hence the sound speed) drops precipitously, the streaming motion of the baryons becomes supersonic and it can potentially have a dramatic impact on structure formation. Subsequent studies, both theoretical and numerical, showed that the large stream velocity of baryons has profound implications on the total number density of haloes (Tseliakhovich & Hirata 2010; Maio, Koopman & Ciardi 2011; Stacy, Bromm & Loeb 2011; Tseliakhovich, Barkana & Hirata 2011; Fialkov et al. 2012; Naoz, Yoshida & Gnedin 2012; O'Leary & McQuinn 2012; Bovy & Dvorkin 2013; Tanaka, Li & Haiman 2013; Tanaka & Li 2014; Asaba, Ichiki & Tashiro 2016), the size of the haloes that are able to retain gas at a given redshift (Naoz, Yoshida & Gnedin 2013), the overall gas fraction in haloes (Dalal, Pen & Seljak 2010; Greif et al. 2011; Maio et al. 2011; Tseliakhovich et al. 2011; Fialkov et al. 2012; Naoz et al. 2012, 2013; O'Leary & McQuinn 2012; Richardson, Scannapieco & Thacker 2013; Asaba et al. 2016), the gas density and temperature profiles (Greif et al. 2011; Liu & Wang 2011; Fialkov et al. 2012; O'Leary & McQuinn 2012; Richardson et al. 2013), the halo mass threshold in which star formation occurs (Liu & Wang 2011; Maio et al. 2011; Greif et al. 2011; Fialkov et al. 2012; O'Leary & McQuinn 2012; Bovy & Dvorkin 2013), as well as black hole evolution (Tanaka et al. 2013; Tanaka & Li 2014; Latif, Niemeyer & Schleicher 2014) and magnetic fields (Naoz & Narayan 2013). A thorough review of the implications of the stream velocity on structure formation is presented in Fialkov (2014).
Citation: Fialkov et al. 2012
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Citation: Fialkov et al. 2012
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Citation: Fialkov et al. 2012
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Citation: Fialkov (2014)
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Citation: Tseliakhovich & Hirata (2010)
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Citation: Tseliakhovich & Hirata (2010)
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Gohil & Ballantyne (2017) showed that the AGN obscuration phenomenon depends on the gas fraction in NSDs. With a higher gas fraction, a greater amount of gas is available to accrete, which increases a column of gas in annulus Σmp and, in turn, controls expansion of the atmosphere. The overall gas fraction (f0) in galaxies is observed to be increasing with redshift. f0 is related to the depletion time and the specific star-formation rate (sSFR) through
(16)
\begin{eqnarray}
f_0(z)=\frac{1}{1+[t_{\rm dep}(z){\rm sSFR}(z)]^{-1}}.
\end{eqnarray}
The depletion time is estimated to be (Saintonge et al. 2013)
(17)
\begin{eqnarray}
t_{\rm dep}=1.5(1+z)^{\alpha } [{\rm Gyr}],
\end{eqnarray}
with α = −1.0 (Tacconi et al. 2013). Lilly et al. (2013) provide an analytical expression for the evolution of sSFR, given by
(18)
\begin{eqnarray}
{\rm sSFR}(M_*,z)= \left\lbrace \begin{array}{@{}l@{\quad }l@{}}0.07\Big (\frac{M_*}{10^{10.5} \, \mathrm{M}_{\odot }}\Big )^{-0.1} (1+z)^3\ \ &amp; z<2,\\
 0.30\Big (\frac{M_*}{10^{10.5} \, \mathrm{M}_{\odot }}\Big )^{-0.1} (1+z)^{5/3}\ \ &amp; z\ge 2, \end{array}\right.
\end{eqnarray}
which was motivated by observational data (e.g. Daddi et al. 2007; Elbaz et al. 2007; Noeske et al. 2007; Pannella et al. 2009; Stark et al. 2013). Since there is a weak dependence on the galaxy mass M*, we choose M* = 1010.5 M⊙. Then the distribution of the gas fraction (Φf) is assumed to be Gaussian around f0 and is given by
(19)
\begin{eqnarray}
\Phi _{\rm f}(z,f)=\frac{1}{\sqrt{2 {\pi} }\sigma _{\rm f}}\exp \Big [-\frac{(f-f_0)^2}{2\sigma _{\rm f}^2}\Big ].
\end{eqnarray}
The dispersion σf is estimated to be 0.2 by roughly fitting a Gaussian to the results of Tacconi et al. (2013). Finally, we can compute the weights Wf of input parameter f ≡ fg, out from
(20)
\begin{eqnarray}
W_{\rm f}(z,f)=\frac{\Phi _{\rm f}(z,f)}{\sum \limits _{k}\Phi _{\rm f}(z,f_k)},
\end{eqnarray}
where
(21)
\begin{eqnarray}
f_k\in [0.2,0.4,0.6,0.8].
\end{eqnarray}
The right panel of Fig. 1 shows the evolution of 20, 40, 60 and 80 per cent gas fraction with redshift, represented by black, red, blue and green curves, respectively. At low redshift, NSDs with low gas fraction dominate the sample, while NSDs with a 40 per cent gas fraction dominate at 1  z  2. Beyond z = 2, NSDs with a 60 per cent gas fraction dominate. Wf of NSDs with low gas fraction decrease overall with redshift and the reverse is true for NSDs with high gas fraction (60 and 80 per cent). Similarly to the observational evidence, the figure illustrates that the dominant gas fraction increases with z and its evolution flattens out at higher redshift.
Citation: Elbaz et al. 2007
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PHL 1811 itself has an estimated Eddington ratio of 





 (Leighly et al. 2007b). PHL 1811, along with several PHL 1811 analogs and WLQs with rest-frame optical spectra (J1521+5202, 2QZ J2154–3056, and the two high-redshift WLQs in Shemmer et al. 2010), have weak or undetected [O iii] 





 narrow emission lines, suggestive of high Eddington ratios (e.g., Boroson & Green 1992; Shen & Ho 2014). Moreover, several studies have found that as 





 increases, the C iv REW generally decreases and the C iv blueshift also increases (e.g., Bachev et al. 2004; Baskin & Laor 2004; Richards et al. 2011; Shen & Ho 2014; Sulentic et al. 2014; Shemmer & Lieber 2015). In these correlations, there is only limited sampling in the super-Eddington or low (





 Å) C iv REW regime, but the overall trends suggest that the Eddington ratio grows as one moves from typical quasars toward WLQs in the C iv REW versus blueshift space (Figure 3(a)). By the nature of our selection of the PHL 1811 analogs and WLQs, demanding that the BELR does not produce normal emission lines, we may have recovered effectively a population of quasars with (extremely) high Eddington ratios. It is difficult to measure 





 directly for our exceptional objects, as the SMBH masses estimated from the line-based virial method are likely highly uncertain and perhaps systematically in error (Section 5.3). However, based on the empirical Γ





 relations, our joint spectral analysis of the X-ray normal subsample in Sections 5.2 and 5.3 does indicate a high Eddington ratio (





) in general for our quasars.33

33
Narrow-line Seyfert 1 galaxies (NLS1s) also generally have steep X-ray spectra and high Eddington ratios. PHL 1811 itself is considered a NLS1 (Leighly et al. 2001), and NIR spectroscopy of a limited sample of WLQs shows that they have in general narrow Hβ and strong optical Fe ii emission (see Section 5.1 of Plotkin et al. 2015), similar to NLS1s. However, as the line widths and REWs have likely dependences on luminosity, our PHL 1811 analogs and WLQs are not directly comparable to local, less-luminous NLS1s.
Citation: Richards et al. 2011
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Phinney (1989) was the first to recognize that 





 is not exactly the maximum orbital pericenter for a complete tidal disruption, a distance we would like to name the “physical tidal radius” (we assign it the symbol 





). To remedy the neglect of internal stellar structure, he suggested that 





 could be estimated by applying to 





 a correction factor based on the star’s apsidal motion constant and its dimensionless binding energy. For this reason, 





 is sometimes reinterpreted to be 





, but without evaluating how it might differ from 





 (Stone et al. 2013). Several groups have tried to include stellar structure in the calculation of 





, but employing purely Newtonian dynamics on polytropic stars (e.g., Luminet & Carter 1986; Khokhlov et al. 1993; Guillochon & Ramirez-Ruiz 2013; Mainetti et al. 2017). Recently, there have been efforts beginning from genuine main-sequence stellar structures, but still restricted to Newtonian dynamics, and examining a limited range of stellar masses (only 1 





 in Goicovic et al. 2019, 





 and 





 at several ages in Law-Smith et al. 2019, 





, 





, and 





 at three different ages in Golightly et al. 2019). Others have explored the dependence on black hole mass induced by relativistic effects, but without any reference to internal stellar structure or the hydrodynamics of disruption (e.g., Ivanov & Chernyakova 2006; Kesden 2012; Servin & Kesden 2017). Earlier works employed a post-Newtonain approximation (e.g., Ayal et al. 2000) or explored the use of relativistic hydrodynamics simulations for strong encounters of polytropic stars, but without stellar self-gravity (e.g., Laguna et al. 1993). In some cases, relativistic effects were approximated by a “generalized Newtonian potential” (e.g., Gafton et al. 2015; Gafton & Rosswog 2019) or in terms of genuine relativistic dynamics (e.g., Frolov et al. 1994), but assuming a polytropic structure for the star and computing stellar self-gravity in an entirely Newtonian fashion (in the last case, fixing it to its initial stellar surface value). Many of these explorations of 





 also computed the energy distribution dM/dE and explored the relation between the remnant mass and orbital pericenter in partial disruptions; in one case (Manukian et al. 2013), they also examined the remnants’ orbital properties. However, all this work was subject to the limitations already enumerated.
Citation: Guillochon & Ramirez-Ruiz 2013
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A non-vanishing α1 adds to the polarization of the orbit in the same way as a non-vanishing α3, and analogous to equation (13) one finds for the change of the orbital eccentricity vector 
(17)
\begin{eqnarray*}
\dot{\boldsymbol {e}} = \dot{\boldsymbol {e}}_{\hat{\alpha }_1} + \dot{\boldsymbol {e}}_{\hat{\alpha }_3} + \dot{\omega }_{\rm PN}\, \hat{\boldsymbol {k}} \times {\boldsymbol {e}} \, , 
\end{eqnarray*}
where 
(18)
\begin{eqnarray*}
\dot{\boldsymbol {e}}_{\hat{\alpha }_1} \simeq \frac{\hat{\alpha }_1}{4c^2} \, \frac{M_{\rm p}- M_{\rm c}}{M_{\rm p}+ M_{\rm c}} \, n_{\rm b} \mathcal {V}_O \, {\boldsymbol w}_\perp 
\end{eqnarray*}
(Damour & Esposito-Farèse 1992b) and 
(19)
\begin{eqnarray*}
\dot{\boldsymbol {e}}_{\hat{\alpha }_3} \simeq \frac{3}{2\mathcal {V}_O}\, {\boldsymbol {a}}_{\hat{\alpha }_3} \times \hat{\boldsymbol {k}} = -\hat{\alpha }_3 \pi \, \frac{s_{\rm p}\nu }{\mathcal {V}_O} \, {\boldsymbol w}_\perp 
\end{eqnarray*}
(Bell & Damour 1996). The velocity ${\boldsymbol w}$⊥ is the projection of the systemic CMB frame velocity into the orbital plane of the binary pulsar. To independently constrain $\hat{\alpha }_3$ from PSR J1713+0747 timing, we will include the $\hat{\alpha }_1$ limits obtained from PSR J1738+0333 by Shao & Wex (2012) in our analysis. Conversely, owing to its small orbital period, PSR J1738+0333’s possible $\hat{\alpha }_3$ effects would be much smaller than its $\hat{\alpha }_1$ effects and would have insignificant impact on the $\hat{\alpha }_1$ limits derived from this system. There is one assumption, however, that we have to make here. Since PSR J1713+0747 and PSR J1738+0333 have different masses, and therefore different sensitivities sp, we cannot assume that they would lead to identical $\hat{\alpha }_1$. For this reason, our analysis only applies to deviations from GR which exhibit only a moderate mass dependence of the strong-field parameter $\hat{\alpha }_1$, at least for neutron stars in the range of 1.3–1.5 M⊙.
Citation: Damour & Esposito-Farèse 1992b
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In our spectroscopic analysis, we also quantify the reflection strength for each source. It is therefore interesting to compare our results to the typical assumptions made in CXB population-synthesis models. Indeed, they generally implement relatively similar assumptions. The reflection is always assumed to have a constant value 





 within each population (e.g., Ballantyne et al. 2006; Treister et al. 2009; Akylas et al. 2012; Ueda et al. 2014) or possibly a function of the degree of obscuration (e.g., G07; Ueda et al. 2014; Esposito & Walter 2016) with no dispersion around a mean value. On average, we find median reflection values that are: (1) significantly lower (





) than those assumed (





) by CXB models and (2) exhibit a rather broad distribution with a median value relative to the whole sample of ∼0.4 (Table 6). Furthermore, we measure a significant anti-correlation with unabsorbed and intrinsic luminosities (the latter being more pronounced; see Figure 8 (lower panels) and Table 6). This trend is further confirmed by the findings of our companion paper on stacked NuSTAR spectra (DM17). In this context, we find sources with lower unabsorbed luminosities to have a median reflection (





) a factor of two stronger than more luminous ones (





). The broad R distribution reaches 50% percentile values a factor of two larger. When using intrinsic coronal luminosities, the differences exacerbates further by a factor of about two. A similar trend has been largely ignored by models; the one exception being the G07 model, for which QSOs have been assumed to have no reflection. G07 also assumes higher R for Type-1 sources (R = 1.3) compared to Type-2s (R = 0.88) in order to mimic a orientation-dependent disk reflection. Ueda et al. (2014) instead assumes a flat R = 0.5 from the disk and a torus-based contribution in the context of a luminosity- and redshift-dependent unified scenario in order to reproduce a total R = 1 for Seyfert galaxies.
Citation: Esposito & Walter 2016
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Over the past decades, our understanding of the EoR has deepened from advances in the observational frontier of galaxies in the early universe. Dedicated surveys of high-redshift galaxies using the Hubble Space Telescope (HST) have measured a large sample of galaxies out to redshift as high as z ∼ 8 (Bouwens et al. 2015b; Finkelstein et al. 2015), which with the help of gravitational lensing has allowed the rest-frame ultraviolet (UV) galaxy luminosity function to be accurately constrained to a limiting magnitude of 






M


UV


AB


≳
−
15


 (Atek et al. 2015; Bouwens et al. 2017; Yue et al. 2018). It is expected that, by the advent of the James Webb Space Telescope (JWST), not only the currently limited sample size of 9 ≲ z ≲ 12 galaxies and candidates (Ellis et al. 2013; Oesch et al. 2014, 2016, 2018) but also the constraints on the faint-end slope evolution of the UV luminosity function will be considerably enhanced (Mason et al. 2015; Yung et al. 2019). Combined with the Thomson scattering optical depth τes = 0.055 ± 0.009 inferred from the cosmic microwave background (CMB) temperature and polarization power spectra by Planck Collaboration et al. (2016a), the SFH based on a plausible faint-end extrapolation of the luminosity function suggests that the global reionization history could be explained by the “known” high-z galaxy population. If the average escape fraction of their ionizing photons into the intergalactic medium (IGM) is in the range of 10%–20% (e.g., Mason et al. 2015; Robertson et al. 2015; Bouwens et al. 2015a; Sun & Furlanetto 2016; Madau 2017; Naidu et al. 2020), there will be no need to invoke additional ionizing sources such as Population III stars and quasars. Nevertheless, the uncertainty associated with such an extrapolation indicates a fundamental limitation of surveys of individual objects—sources too faint compared with the instrument sensitivity, such as dwarf galaxies, are entirely missed by galaxy surveys, even though a significant fraction, if not the majority, of the ionizing photons are contributed by them (Wise et al. 2014; Trebitsch et al. 2018; but see also Naidu et al. 2020).
Citation: Atek et al. 2015
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