adsabs/FOCAL huggingface dataset:
SMBHs and host galaxies share several properties. Both SMBHs and galaxies have exponential cut-offs at the high mass end of their co-moving space densities (e.g., Shankar et al. 2009; Ilbert et al. 2013; Kelly & Shen 2013). The population of both SMBHs and galaxies also exhibit mass downsizing whereby the oldest, in the case of galaxies, and the most massive of SMBHs grew early and rapidly (e.g., Thomas et al. 2005, 2010; Merloni & Heinz 2008). However, there is a mismatch in both the shape and co-moving number density between galaxies and dark matter halos, especially at the low and high mass ends of these functions (Benson et al. 2003). Because powerful AGN can have a mechanical and radiative energy output similar to or exceeding that of the binding energy of a massive galaxy and dark matter halo, AGN are thought to play a key role in regulating the baryonic growth of galaxies. Both observations and simulations have suggested that there may be a positive trend between the mean black hole accretion rate and star-formation rate (SFR; e.g., Delvecchio et al. 2015; McAlpine et al. 2017), while the mean SFR as a function of black hole accretion rate shows no correlation for low luminosity sources (e.g., Stanley et al. 2015; McAlpine et al. 2017). One should be cautious when interpreting both theoretical and observational results in the definition of what exactly AGN feedback is and how AGN affect their host galaxies to explain the properties of an ensemble of galaxies (Scholtz et al. 2018). The strength and nature of AGN feedback – the cycle whereby the SMBH regulates both its own growth and that of its host – depends on galaxy mass and morphology. For example, the most massive elliptical galaxies are generally metal-rich and old, while less massive lenticular galaxies, which make up the bulk of the early-type galaxy population, have star formation histories that lasts significantly longer (Thomas et al. 2005, 2010; Emsellem et al. 2011; Krajnović et al. 2011). Clearly, if AGN feedback plays a crucial role in shaping the ensemble of galaxies, its impact on massive dispersion dominated galaxies must result in somewhat different characteristics in these galaxies compared to rotationally-dominated and predominately less massive lenticular galaxies.
Citation: Delvecchio et al. 2015
Citation function: Compare/Contrast
Current observations of the interstellar neutral helium trajectories by the Interstellar Boundary EXplorer (IBEX) (McComas et al. 2009) determined the inflow longitude of the interstellar wind to be 





 (Leonard et al. 2015), ∼757 (McComas et al. 2015), 





 (Bzowski et al. 2015), and 





 (Schwadron et al. 2015). Recent studies using the Ulysses/GAS instrument made observations of the interstellar helium distribution by detecting the sputtered ions from a lithium fluoride coated surface, finding the inflow direction to be 





 (Bzowski et al. 2014) and 





 (Wood et al. 2015). However, recent observations of the pickup helium focusing cone using the Plasma and Suprathermal Ion Composition (PLASTIC; Galvin et al. 2008) on board the Solar and Terrestrial Relations Observatory Ahead (STEREO A) spacecraft measured the peak density and determined the inflow longitude to be 





 (Drews et al. 2012). In addition, Gershman et al. (2013) measured the peak density of the pickup helium focusing cone using the Fast Imaging Plasma Spectrometer (FIPS; Andrews et al. 2007) instrument on board the MErcury, Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft and the Solar Wind Ion Composition Spectrometer (Gloeckler et al. 1998) instrument on board the Advanced Composition Explorer spacecraft and determined inflow longitudes of 





 and 





, respectively. Although these pickup ion measurements fall within the 1σ uncertainties of the IBEX observations, they appear to show consistently small deviations in the same direction. Refer to Figure 1 for a diagram of the 





 longitudinal difference between the observations focused on in this study of Drews et al. (2012) and Schwadron et al. (2015). The difference between the pickup helium and neutral helium observations suggests that the transport of pickup ions inside 1 au may play a role—a concept that was addressed but not factored into the derived peak longitudes in the previously mentioned pickup ion studies and has recently been up for debate (Chalov 2014; Lallement & Bertaux 2014; Frisch et al. 2015).
Citation: Galvin et al. 2008
Citation function: Uses; Uses
The connection between long-duration GRBs (LGRBs) and SNe was predicted theoretically (Colgate 1974; Woosley 1993) and has been verified observationally (e.g., Galama et al. 1998; Hjorth et al. 2003; see a review in Woosley & Bloom 2006). They usually happen in the star formation regions of the galaxies (e.g., Paczyński 1998; see the reviews in Woosley & Bloom 2006 and Kumar & Zhang 2015). The immensely bright afterglows illuminate the gas and dust within the star-forming regions of the host galaxy and intervening intergalactic medium along the GRB line of sight. Their spectra are usually featureless power-laws or broken power-laws, which can be well described by the synchrotron radiations of relativistic electrons. Therefore, GRB afterglows are good probes of burst environment and the interstellar dust and gas in distant, star-forming galaxies (Metzger et al. 1997; Jensen et al. 2001; Savaglio et al. 2003; Vreeswijk et al. 2004; Chen et al. 2005; Prochaska et al. 2007a; Schady et al. 2007, 2010; Watson et al. 2007; Fox et al. 2008; Starling et al. 2008; Jang et al. 2011; Xin et al. 2011). GRB afterglow spectra with Lyα absorption features indicate the presence of large column densities of cold neutral gas within GRB host galaxies, and their hydrogen column densities (





) are usually larger than 





 cm−2 (e.g., Prochaska et al. 2007b; Schady 2012). The damped Lyα systems (DLA) may represent the ISM near the GRBs in a few kiloparsecs (Kpc), but not gas directly local to the GRB (Prochaska et al. 2007b). Thus GRB optical afterglows may be used as probes of the ISM in their host galaxies, as the ISM observed is less affected by the GRB or its progenitor (Watson et al. 2007). The visual dust extinctions (





) along the GRB lines of sight of many GRBs are low. As shown in Greiner et al. (2011), about 50% of GRBs observed with GROND after the launch of Swift mission have 





. In addition, the early optical light curves of about one-third of GRBs show a smooth onset bump (Li et al. 2012). It may be due to the deceleration of the GRB fireball by the ambient medium (Sari & Piran 1999; Kobayashi & Zhang 2007). In this scenario, the rising slope of the bump is determined by the medium density profile (





) and the spectrum index of the accelerated electrons 





], says, 





 (Liang et al. 2013). Hence, The afterglow onset bumps would be also an ideal probe to study the properties of the fireball and the profile of the circumburst medium. Liang et al. (2013) found that 





 (see also Watson et al. 2007; Jin et al. 2012).
Citation: Schady et al. 2007
Citation function: Motivation
Citation: Vreeswijk et al. 2004
Citation function: Motivation
In this paper, we performed statistical analyses on the chirality and the magnetic configurations (inverse-polarity versus normal polarity) of the solar filaments that erupted on the solar disk from 2010 May 13 to 2015 December 31 covering both the rising phase and the beginning of the declining phases of solar cycle 24. The chirality is determined by an indirect method proposed by Chen et al. (2014), i.e., left-/right-skewed drainage corresponds to dextral/sinistral chirality. The determination of the magnetic configuration is also based on a method proposed by Chen et al. (2014), i.e., those filaments that follow Martin's Rule (Martin et al. 1994) are of the inverse-polarity type, and those that disobey Martin's Rule are of the normal-polarity type. By studying a sample of 571 filaments, we obtained the following results.

(1)
About 94.8% of the filaments in the northern hemisphere have negative helicity, and 87.4% of the filaments in the southern hemisphere have positive helicity, indicating a significant hemispheric preference of helicity. As a whole, 91.6% of our sample of erupting filaments follows the hemispheric rule of helicity sign. With the improved method for determining the filament chirality, the strength of the hemispheric rule is higher than that in previous studies. It should be noted that the statistical result is based on the erupting filaments. Those filaments that do not erupt during the disk passage are not included in our sample.


(2)
Following convention, we divided the filaments into three types, the quiescent type, the intermediate type, and the active-region type. It is shown that the strength of the hemispheric rule is 93% for the quiescent filaments, 95% for the intermediate filaments, and 83% for the active-region filaments.


(3)
Regarding the cyclic behavior of the hemispheric preference, it is found that the strength of the quiescent filaments decreases slightly from ∼97% in the rising phase to ∼85% in the declining phase, whereas the strength of the intermediate filaments keeps a high value around 96 ± 4%. Only the active-region filaments show significant variations. Their strength of the hemispheric rule rises from ∼63% to ∼95% in the rising phase, and keeps a high value of 82% ± 5% during the declining phase. However, during a half-year period around the solar maximum, the hemispheric preference totally vanishes.


(4)
It is found that in our sample of erupting filaments, 89% are inverse-polarity filaments that are magnetically supported by a flux rope, whereas 11% are normal-polarity filaments that are magnetically supported by a sheared arcade.
Citation: Martin et al. 1994
Citation function: Uses; Uses
One of the major endeavors of modern observational cosmology is to paint a coherent picture of the history of the universe. To this end, the final frontier remains the identification and characterization of the first sources that appeared in the universe, those which played a significant role in reionizing the intergalactic medium from a neutral state to a fully ionized one over the first billion years (corresponding to redshifts of 6 ≲ z ≲ 12). Extragalactic surveys (of deep fields as well as lensing clusters; Grogin et al. 2011; Koekemoer et al. 2011; Bradley et al. 2012; Ellis et al. 2013; Bradley et al. 2014; Schmidt et al. 2014; Treu et al. 2015; Lotz et al. 2017; Salmon et al. 2018; Coe et al. 2019) with the Hubble Space Telescope (HST) have yielded impressive gains in the number of galaxy candidates at redshifts z = 7–10, with samples reaching over 1000 objects, and revolutionized our understanding of galaxy evolution therein. Complementing these observations, the spectroscopic confirmation (e.g., Finkelstein et al. 2013; Oesch et al. 2015; Zitrin et al. 2015; Roberts-Borsani et al. 2016; Hoag et al. 2017; Stark et al. 2017; Hashimoto et al. 2018) and characterization (e.g., Laporte et al. 2017; Mainali et al. 2018; Endsley et al. 2021) of over a dozen sources has seen impressive advances with ground-based spectroscopy (e.g., probing the rest-frame UV and FIR with Keck/MOSFIRE, VLT/X-Shooter, and ALMA), particularly for the brightest and rarest objects. For the rest-frame optical, however, the Spitzer Space Telescope has, until now, afforded the only realistic means for statistical analyses. However, the Infrared Array Camera’s (IRAC) coarse spatial resolution and the limited depth probed by many surveys make robust and uncontaminated constraints on galaxy properties a challenging feat. Further advances with current facilities are challenging owing to the limited wavelength coverage of HST and the observed faintness of star-forming galaxies as one approaches redshifts of z > 10. The imminent arrival of the James Webb Space Telescope (JWST) has the potential to detect galaxies well beyond the current frontier of z ∼ 12 (e.g., Behroozi et al. 2020) thanks to the unprecedented resolution and sensitivity of its near-IR (NIR) imaging and spectroscopic capabilities, and revolutionize our current understanding of galaxy evolution.
Citation: Stark et al. 2017
Citation function: Background; Background
Citation: Zitrin et al. 2015
Citation function: Background; Background
The SFG population bias factor, bSFG may be calculated from the ratio of galaxy to dark matter correlation functions, i.e:
(27)$$\begin{eqnarray*}
b_{\rm SFG}^2(z) &amp;=&amp; \frac{\xi _{\rm g}(r, z)}{\xi _{\rm DM}(r, z)} \nonumber \\
&amp;=&amp; \left(\frac{r_0(z)}{8} \right)^{\iota } \, \frac{J_2}{\sigma _8^2 \,\, \mathcal {G}^2(z)}
\end{eqnarray*}$$(e.g. Kaiser 1984; Bardeen et al. 1986; Lindsay et al. 2014), where the matter fluctuation amplitude σ8 = 0.811 ± 0.006 (Planck Collaboration 2020), and $\mathcal {G}(z) = g(z)/g_0$, with g(z) as the growth factor at redshift z and g0 = g(z = 0) (e.g. Carroll, Press & Turner 1992).7 Additionally, J2 = 72/([3 − ι][4 − ι][6 − ι]2ι), and $r_0(z) = r^{\rm c}_0 (1+z)^{p}$ with p = 1 − (3 + w)/ι (Lindsay et al. 2014). Here, the choice of the parameter w reflects the clustering model adopted. In this demonstrative model, we consider only linear clustering (Overzier et al. 2003) where clustering growth is set by linear perturbation theory and w = ι − 1. We leave the investigation of alternative clustering growth models to future work – for example, stable clustering (where clusters have a fixed physical size and w = 0), co-moving clustering (where clusters have fixed co-moving size and w = ι − 3) and decaying clustering (which implies a rapid clustering decay) are also considered in the literature (Overzier et al. 2003; Kim et al. 2011; Elyiv et al. 2012). The remaining parameters in equation (27) are the power-law slope of the two-point correlation function of galaxies, ι, and the galaxy clustering length $r_0^{\rm c}$. Both of these may be estimated empirically for SFGs, and we adopt the best-fitting values of Hale et al. (2018): ι = 1.8 and $r_0^{\rm c} = 6.1\, \text{Mpc}\, h^{-1}$. These were computed from radio-selected SFGs in the COSMOS field using deep Karl G. Jansky Very Large Array (VLA) data at 3 GHz, reaching redshifts as high as z ∼ 5, thus covering our range of interest (z ≤ 3).8 The resulting bias factor from these parameter choices is higher than those computed for SFGs at other wavelengths (e.g. Gilli et al. 2007; Starikova et al. 2012; Magliocchetti et al. 2013), but this is attributed to the greater extent of the redshift distribution of the sources.
Citation: Carroll, Press & Turner 1992
Citation function: Uses
In Fig. B.1 we plot the soft X-ray light curves for our candidate highly variable AGN using available X-ray data taken by the satellite missions Einstein, ROSAT, XMM, Suzaku and Swift. The count rates were obtained from different archives including HEASARC, the XMM Science Archive, the Swift UKSSDC and from our own Swift XRT data analysis, and for upper limits the 1SXPS catalogue (Evans et al. 2014) and the XMM upper limit server2 were queried. The count rates of the different satellites were converted to fluxes between 0.2–2.0 keV using PIMMS3 assuming a power law with a photon index of 1.7 as a spectral shape taking into account Galactic extinction as given by Willingale et al. (2013). Sobolewska & Papadakis (2009) found a positive correlation between flux and spectral slope for a sample of bright RXTE AGN in the 2–10 keV band. This could affect the relative fluxes seen in our sample which are plotted in Fig. B.1. We have attempted to quantify this for the different detectors used in the creation of our light curves. The sample of Sobolewska & Papadakis (2009) showed spectral changes with observed power-law slope varying between 1.0 and 2.0 (see their Fig. 7). For a typical Galactic absorption of 3 × 1020 cm-2 the change from slope of 1.0 to 2.0 would alter our estimated fluxes by −14% (ROSAT), −13% (XMM-Newton), +7% (Swift-XRT), +76% (Suzaku), +25% (Einstein-IPC). The change is large for Suzaku observations since in this case we use the count rate between 2.0–10.0 keV and extrapolate it to the soft band. All other satellites are sensitive in the soft band and hence the fluxes are less dependent upon the assumed spectral index. Six sources within our sample (XMMSL1 J024916.6-041244, J034555.1-355959, J045740.0-503053, J051935.5-323928, J070841.3-493305, and J193439.3+490922) display variation in flux of a factor of ten or greater between at least one pair of XMM and Swift observations, on timescales of months to years. The ratio between the soft X-ray flux observed with Swift and that observed with XMM for the remaining sources is typically a factor of a few. We observed the two TDE candidates with XRT, and found that both had faded significantly, following expectations from previous and later fluxes and upper limits (Figs. 1p and h).
Citation: Sobolewska & Papadakis (2009)
Citation function: Uses; Uses
Citation: Sobolewska & Papadakis (2009)
Citation function: Uses
Thereafter, we proceeded to fit the broad-band spectrum with the analytical model ‘COMPMAG’ and its updated version. The ‘COMPMAG’ model has many spectral parameters that were impossible to constrain simultaneously. Especially, β if left free preferred a value >0.9 in the fit, which is greater than the maximum possible terminal velocity at the neutron star surface. Therefore, we tested the two-component ‘COMPMAG’ model, assuming reasonable parameter values applicable to low-luminosity accretion-powered pulsars, i.e. free-fall velocity profile (η = 0.5), and β = 0.5 that corresponds to the maximum terminal velocity at the neutron star surface, and a pencil beam emission pattern. The radius of the accretion column (r0) was set to 0.25 corresponding to a radius of ∼1 km for a neutron star of mass 1.4 M⊙. The Albedo at the neutron star surface (A) was set to 1. This provided an acceptable fit to the data with acceptable and physically viable parameter values. Addition of a CRSF feature was not required for this model. The high β preferred by the fit indicated that bulk motion Comptonization (BMC) dominates the Comptonization process in X Persei. The fit also provided a low value of electron temperature (kT) consistent with the high value of β obtained (Farinelli et al. 2012). The dominance of BMC in low-luminosity pulsars, especially X-Persei has been predicted before and is consistent with our results (Becker & Wolff 2005, 2007). In Becker & Wolff (2007), the authors qualitatively described the observed spectrum of X-Persei with a pure BMC model. The obtained reduced χ2 value with ‘COMPMAG’ was higher than that obtained with the ‘newhcut’ model (1.84 for 569 dof), although there were no systematic pattern in the residuals, and the higher χ2 was mainly contributed by a higher variance in a few energy bins of the XIS spectra. Fit with the updated ‘COMPMAG2’ model was difficult with the given statistical quality of the data, as it required fitting many more additional parameters. Moreover as discussed in Farinelli et al. (2016), the ‘COMPMAG’ model provides an adequate description of the spectrum for low-luminosity pulsars, where the blackbody emission provides the major source of photons for Comptonization, appropriate for the case of X Persei. Considering the above, and as ‘COMPMAG’ is a more physical model to understand the continuum spectra of the source, we used this as our best-fitting model for the rest of the paper. As a CRSF feature is not required for this model, we cannot claim the presence of a CRSF in the average spectrum of X Persei. Table 1 summarizes the best-fitting broad-band spectral parameters obtained using the ‘newhcut’ (without addition of the CRSF) and ‘COMPMAG’ models. Fig. 3 shows the best-fitting unfolded spectrum for the best-fitting model, showing the model components.
Citation: Farinelli et al. 2012
Citation function: Similarities
The GRMHD model library is limited in that it has only a few discrete parameter values for the magnetization, black hole spin, and electron temperature distributions. Apart from increasing the number of discrete values, one could think about ways to interpolate between these using, for example, machine learning techniques (van der Gucht et al. 2020; Yao-Yu Lin et al. 2020) or fit to semi-analytic models (e.g., Broderick et al. 2016). The variability within the GRMHD models was found to be an important limitation for constraining black hole parameters, as attested by, for example, the small difference in recovered parameters between the EHT2021 and EHT2021+ arrays. The analysis pipeline may be extended to include a characterization of the source variability as part of the model selection process (e.g., Kim et al. 2016; Roelofs et al. 2017; Medeiros et al. 2017, 2018; Johnson et al. 2017; Bouman et al. 2018; Wielgus et al. 2020), which could improve the constraining power beyond the averaging method introduced here. EHT expansions are expected to make the large-scale jet visible in reconstructed images of the black hole shadow due to an increased dynamic range (Doeleman et al. 2019; Roelofs et al. 2020; Raymond et al. 2021). This connection between event-horizon scales and the extended jet has not been taken into account in the parameter estimation framework used here, as the GRMHD library images have a small field of view (160 μas). With the development of GRMHD simulations that have the ability to connect large (e.g., Fromm et al. 2017, 2018, 2019; Liska et al. 2018; Chatterjee et al. 2019) and small scales at different wavelengths and of an extended fitting framework, the constraining power is expected to improve especially for EHT extensions and space arrays. For a mass measurement, feature extraction techniques such as a ring fit (Event Horizon Telescope Collaboration 2019d,f) may be used, potentially in combination with fitting the more extended (variable) structure (Broderick et al. 2020b). Models and analysis techniques for Sgr A* and polarization could be considered as well. These possible avenues for further simulation and fitting framework development mean that the parameter constraints presented in this paper should not be interpreted as set limits on the constraining power of the considered arrays. Rather, they show what is achievable with the current state of the art.
Citation: Johnson et al. 2017
Citation function: Future Work; Future Work