adsabs/FOCAL huggingface dataset:
Let us first define some terminology before describing our results. “Outflow” means the flow with a positive radial velocity vr, including both “turbulent outflow” and “real outflow.” In the former case, the test particle will first move outward but will eventually return after moving outward for some distance. In the latter case, the test particle continues to flow outward and eventually escapes the outer boundary of the simulation domain. “Real outflow” consists of two components, i.e., jet and wind. The jet region is defined as the region occupied by the time-averaged magnetic field lines connected to the ergosphere (described by 






r


erg


≡


r


g


+


1
−


a


2




cos


2


θ





r


g


;


 Visser 2007) of the black hole. We note that this definition coincides with other notions of the jet based on velocity and magnetization, as seen even in early two-dimensional simulations (McKinney & Gammie 2004). Moreover, turbulence tends not to have a strong effect on these field lines, so that in both individual snapshots and a time-averaged sense, the field lines connecting to the ergosphere coincide with β  2 and σ > 1 (McKinney et al. 2012, see Figures 3 and 6). Thus, the jet region is bounded by the magnetic field line whose foot point is rooted at the black hole ergosphere with θ = 90°, i.e., the boundary between the black hole ergosphere and the accretion flow (refer to the red lines in Figure 5). In this case, all magnetic field lines in the jet region are anchored to the black hole ergosphere and thus can extract the spin energy of the black hole via the Blandford & Znajek mechanism (Blandford & Znajek 1977; hereafter BZ77) to power the jet (“BZ-jet”). Real outflows outside of this boundary are powered by the rotation energy of the accretion flow, and we call them wind. Note that our definition of wind adopted here is different from that adopted in some literature, where they require that the Bernoulli parameter of wind must satisfy Be > 0. We do not add this requirement because we find that for a nonsteady accretion flow, Be is not constant along the trajectories but usually increases outward, at least to the radius within which turbulence is well developed (Yuan et al. 2015). We find that even though Be is negative at a small radius, it can become positive when it propagates outward.
Citation: Blandford & Znajek 1977
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Citation: Yuan et al. 2015
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The H i mass fraction of every gas particle in the baryonic runs is calculated based on the particle’s temperature and density and the cosmic UV background radiation flux while including a prescription for self-shielding of H2 and dust shielding in both H i and H2 (Christensen et al. 2012). This allows for the straightforward calculation of the total H i mass of each simulated galaxy. We create mock H i data cubes only for the 42 halos that contain 





. Specifically, we create mock data cubes that mimic ALFALFA observations (Haynes et al. 2011). After specifying a viewing angle (see below), our code considers the line-of-sight velocity of each gas particle. The velocity of each particle is tracked in the simulation by solving Newton’s equations of motion, but any turbulent velocity of the gas is not taken into account. Velocity dispersions in dwarf galaxies can be on the order of the rotational velocity, ∼10–15 km s−1 (e.g., Stanimirović et al. 2004; Tamburro et al. 2009; Oh et al. 2015). Dispersions are thought to be driven at least partially by thermal velocities or supernovae (Tamburro et al. 2009; Stilp et al. 2013a, 2013b). In our simulations, supernovae inject thermal energy, and the thermal state of the H i gas needs to be considered in the mock H i linewidth for a realistic comparison to observations. To account for the thermal velocity, the H i mass of each gas particle is assumed to be distributed along the line-of-sight in a Gaussian distribution with a standard deviation given by the thermal velocity dispersion, 





, where T is the temperature of the gas particle. After this thermal broadening is calculated, a mock H i data cube can be generated by specifying the spatial and velocity resolution. For all of our galaxies, we adopt a spatial resolution of 54 pixels across 2Rvir. In practice, this corresponds to a range of ∼1 kpc resolution in our lowest-mass galaxies up to ∼9 kpc resolution in our most massive galaxies. However, the spatial resolution plays no role in our study, since measurements of the VF are based on spatially unresolved H i data. For the velocity resolution, we match the ALFALFA specification of 11.2 km s−1 (two-channel boxcar-smoothed).
Citation: Christensen et al. 2012
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Citation: Stilp et al. 2013a
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In this work, we present well-calibrated optical photometric (−0.2 to +413 d), polarimetric (−2 to +31 d) and optical (−5 to +391 d), NIR (−5 to +22 d) spectroscopic studies of SN 2012au, based on data obtained using many observational facilities around the globe. Analysis based on our photometric observations suggests that SN 2012au appears to be one of the most luminous SNe Ib (MB, peak = −18.06 ± 0.12 mag), though fainter than the threshold limit of SLSNe I (M$_\mathrm{ g}\, \lt -$19.8 mag; Quimby et al. 2018). The MR, peak (∼ –18.67 ± 0.11 mag) of SN 2012au is brighter than the average values of SNe Ib and Ic, but closer to those reported for SNe Ic-BL (Drout et al. 2011). Similarly, the peak bolometric luminosity of SN 2012au (∼ [6.56 ± 0.70] × 1042 erg s−1) is higher than the mean peak luminosities of SNe Ib and Ic, but still lower than those of SNe Ic-BL (Lyman et al. 2016). Using the early bolometric light curve of SN 2012au, the estimated values of Mej, Ek, MNi, and T0 are ∼5.1 ± 0.7 M⊙, ∼ (4.8 ± 0.6) × 1051 erg, ∼0.27−0.30 M⊙, and ∼66.0 ± 9.4 d, respectively. These physical parameters of SN 2012au are close to those inferred for SN 2009jf (a bright SN Ib: Sahu et al. 2011) and – on average – larger than for classical SNe Ib/c but smaller than for some SNe Ic-BL. SN 2012au manifests larger Mej and MNi in comparison with most of the SNe IIb, Ib, and Ic, which may be the prime reason behind the luminous peak of SN 2012au, as seen in the case of SLSNe I (Nicholl et al. 2015). On the other hand, light-curve decline rates of SN 2012au (at phases ≥+40 d) in all the optical bands are shallower than typically observed in the case of SNe Ib and slow-decaying SLSNe I, and theoretically predicated for 56Co $\rightarrow \, ^{56}$fe decay. As SN 2012au exhibits comparatively larger Mej, a larger optical depth resulting in a larger diffusion time-scale (for the trapped energy to cross the outer envelope) could broaden the light curve. Therefore, high trapping of gamma-rays at late phases or higher opacity of massive ejecta are among the plausible interpretations for the modest luminosity decline rate of SN 2012au in comparison with other SNe Ib (Clocchiatti & Wheeler 1997). However, smoothly distributed circumstellar media up to a larger radius could be another possibility behind the late-time shallower decay rate for SN 2012au, but an absence of the CSMI in the late-time spectra ruled out this scenario (Milisavljevic et al. 2018). The late-time bolometric light curve of SN 2012au is better constrained by $L~\varpropto\, t^{-2}$, a conventional magnetic dipole equation. Hence for SN 2012au the shallower decay of the late-time light curve might be a potential indicator of a central engine powering source that is accelerating the inner ejecta.
Citation: Clocchiatti & Wheeler 1997
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Let ui be the evolved quantity at the coordinate position xi. Then, THC_M1 approximates the derivative of the flux f(u) at the location xi as
(31)$$\begin{eqnarray}
\partial _x f (u) \simeq \frac{F_{i - 1 / 2} - F_{i + 1 / 2}}{\Delta x},
\end{eqnarray}$$where Fi − 1/2 and Fi + 1/2 are numerical fluxes defined at $x_i \mp \frac{\Delta x}{2}$, respectively. The fluxes are constructed as linear combination of a non-diffusive second order flux $F^{\operatorname{HO}}$ and a diffusive first order correction $F^{\operatorname{LO}}$:
(32)$$\begin{eqnarray}
F_{i + 1 / 2} = F_{i + 1 / 2}^{\operatorname{HO}} - A_{i + 1 / 2} \varphi _{i + 1 / 2} \left(F_{i + 1 / 2}^{\operatorname{HO}} - F_{i + 1 / 2}^{\operatorname{LO}}\right) .
\end{eqnarray}$$The term φi + 1/2 is the so-called flux limiter (LeVeque 1992), while Ai + 1/2 is a coefficient introduced to switch off the diffusive correction at high optical depth (more below). The role of the flux limiter is to introduce numerical dissipation in the presence of unresolved features in the solution u and ensure the non-linear stability of the scheme. In particular, if Ai + 1/2φi + 1/2 = 0 the second-order flux is used, while if Ai + 1/2φi + 1/2 = 1, then the low order flux is used. A standard second order non-diffusive flux is used for $F^{\operatorname{HO}}$, while the Lax–Friedrichs flux is used for $F^{\operatorname{LO}}$:
(33)$$\begin{eqnarray}
F^{\operatorname{HO}}_{i + 1 / 2} = \frac{f (u_i) + f (u_{i + 1})}{2},
\end{eqnarray}$$(34)$$\begin{eqnarray}
F^{\operatorname{LO}}_{i + 1 / 2} = \frac{1}{2} [f (u_i) + f (u_{i + 1})] - \frac{c_{i + 1 / 2}}{2} [u_{i + 1} - u_i] .
\end{eqnarray}$$The characteristic speed in the Lax–Friedrichs flux ci is taken to be the maximum value of the speed of light between the right and left cells
(35)$$\begin{eqnarray}
c_{i + 1 / 2} = \max _{a \in \lbrace i, i + 1 \rbrace } \left\lbrace \left| \alpha _a \sqrt{\gamma _a^{x x}} \pm \beta _a^x \right| \right\rbrace .
\end{eqnarray}$$We remark that it is known that the M1 system can, in some circumstances, lead to acausal (faster than light) propagation of neutrinos in GR (Shibata et al. 2011). For this reason, one might argue that a better choice of the characteristic velocity for the Lax–Friedrichs formula would have been given by the eigenvalue of the Jacobian of $\boldsymbol{F}$. These values are known analytically (Shibata et al. 2011), however in our preliminary tests we found that the use of the full eigenvalues resulted did not improve on the stability or accuracy of the M1 solver.
Citation: Shibata et al. 2011
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Citation: Shibata et al. 2011
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To distinguish the emission from AGNs and star formation we use the fact that star-forming galaxies (SFGs) are known to exhibit radio–IR correlation across a wide range of luminosities and redshifts (see Condon 1992; Appleton et al. 2004; Basu et al. 2015). The correlation between 1.4 GHz luminosity and IR luminosity (monochromatic IR luminosity as well as bolometric IR luminosity between 8.0 and 1000 $\mu$m) in SFGs is attributed to the fact that both radio and IR emission are closely related to star formation (Ivison et al. 2010). AGNs with predominant radio emission from jet deviate from radio–IR correlation by showing radio-excess (Morić et al. 2010; Del Moro et al. 2013). Therefore, we examine if our NLS1s show radio-excess in the radio–IR correlation. We note that radio–IR correlation can be represented as the ratio of IR flux to 1.4 GHz radio flux density i.e. q=log(SIR/$S_{\rm 1.4 \, GHz}$) (see Appleton et al. 2004). For our radio-detected NLS1s we estimate q$_{\rm 22\, {\mu }m}$=log($S_{\rm 22 {\mu }m}$/$S_{\rm 1.4 \, GHz}$), where IR flux at 22 $\mu$m is taken from Wide-field Infrared Survey Explorer (WISE),2 and 1.4 GHz flux density is taken from FIRST whenever available, otherwise from NVSS. WISE is an all sky survey carried out at four MIR photometric bands namely W1 [3.6 $\mu$m], W2 [4.6 $\mu$m], W3 [12 $\mu$m], and W4 [22 $\mu$m], with 5σ sensitivity of 0.08, 0.11, 1.0, and 6.0 mJy, and angular resolution of 6.1, 6.4, 6.5, and 12.0, respectively (Wright et al. 2010). Using the most recent version of WISE source catalogue (AllWISE3 data release) we obtain MIR counterparts for 481, 480, 440, and 354 of our 498 radio-detected NLS1s with SNR ≥ 5.0 in W1, W2, W3, and W4 bands, respectively. The WISE counterparts of our radio-detected NLS1s are searched within a circle of 2.0 arcsec radius centred at SDSS optical positions. q$_{\rm 22 {\mu }m}$ is estimated using k-corrected fluxes, where IR spectral index is derived using W3 and W4 band fluxes, and radio spectral index is derived using 1.4 GHz and 150 MHz flux densities, whenever available, otherwise an average radio spectral index of −0.7 is considered.
Citation: Wright et al. 2010
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Efforts have been made to model the signals caused by different sources of stellar variability within the RV time series (e.g., Tuomi et al. 2013; Rajpaul et al. 2015; Davis et al. 2017; Simola et al. 2019). Several solutions have been successfully proposed in order to deal with stellar oscillations and granulation phenomena, such as: calculating stellar evolution sequences (e.g., Christensen-Dalsgaard et al. 1995); fitting a two-level structure tracking (TST) algorithm based on a two-level representation of granulation (Del Moro 2004); using daytime spectra of the Sun in order to measure the solar oscillations (e.g., Kjeldsen et al. 2008; Lefebvre et al. 2008); and characterizing the statistical properties of magnetic activity cycles focusing on HARPS observations (e.g., Pepe et al. 2011; Dumusque et al. 2011b). However, properly modeling the other sources of stellar activity remains extremely challenging (e.g., Nava et al. 2019). In the present work, we deal with the cross correlation function (CCF) that is derived from the stellar spectrum (e.g., Hatzes 1996; Hatzes & Cochran 2000; Fiorenzano et al. 2005). As it is well known, the CCF barycenter estimates the RV of the star. The asymmetry and the full width at half maximum (FWHM) of the CCF give a strong indication for stellar activity, meaning that variations in RV are caused by active regions rather than by an exoplanet (e.g., Hatzes 1996; Queloz et al. 2001; Boisse et al. 2011; Figueira et al. 2013; Simola et al. 2019). Several solutions have been successfully proposed for mitigating stellar activity perturbations when working with RV measurements, including: decorrelating RV data against activity indicators such as log ${{R'}_{{\rm{HK}}}}$R′HK (e.g., Wilson 1968; Noyes et al. 1984) or Hα (e.g., Robertson et al. 2014); modeling stellar activity by fitting Gaussian processes (GPs, Rasmussen & Williams 2005; Haywood et al. 2014; Rajpaul et al. 2015); or moving averages (e.g., Tuomi et al. 2013) to the RV data. A common statistic employed for identifying changes in the shape of the CCF is the bisector span (e.g., Hatzes 1996; Queloz et al. 2001).
Citation: Noyes et al. 1984
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There are still several sources of systematics we do not consider in this paper. For instance, whether the use of a different mass distribution models for these lenses could significantly affect the final result. Therefore, we performed a sensitivity analysis and repeated the above calculation using the extend power law (EPL) lens model, in which the luminosity density profile [v(r) ∼ r−δ] is different from the total mass (luminous plus dark matter) density profile ρ(r) ∼ r−α. Such lens model has found widespread astrophysical applications in the literature (Cao et al. 2016a; Xia et al. 2017; Qi et al. 2019), considering the anisotropic distribution of stellar velocity dispersion β (Koopmans 2005; Cao et al. 2017b; Chen et al. 2019). With the EPL lens parameters (α, β, δ) modelled by Gaussian distributions α = 2.00 ± 0.08, δ = 2.40 ± 0.11, and β = 0.18 ± 0.13 (Gerhard et al. 2001; Bolton, Rappaport & Burles 2006; Graur et al. 2014; Schwab, Bolton & Rappaport 2010), the scatter plot of the deviation Tzs in EPL model is shown in Fig. 4. Our results provide the deviation Tzs = 0.974 ± 0.017 [corresponding to c(zs) = 2.922(± 0.051) × 105 km s−1], and the median value Med(Tzs) = 0.983 with the median absolute deviation MAD(Tzs) = 0.259 for the full lens sample. Therefore, our results show that the assumed lens model has a slight impact on the SOL constraint, which highlights the importance of auxiliary data (such as more high quality integral field unit) in improving constraints on the density profile of gravitational lenses. More detailed models of mass distribution, such as Navarro–Frenk–White density profile (suitable for dark matter distribution; Navarro, Frenk & White 1997), Sersic-like profile (suitable for stellar light distribution) (Sérsic 1968), pseudo-isothermal elliptical mass distribution, could also be considered in this context (Kassiola & Kovner 1993). However, strong lensing observables we used are determined by the total mass inside the Einstein radius. Hence, they are not so sensitive to the details of the very central distribution like cusps (besides the extremal cases). Moreover, the Einstein rings of the lenses we used corresponded to less than 10 kpc hence the NFW profile of the dark halo would not likely be manifested.
Citation: Gerhard et al. 2001
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In this paper, we report on spectroscopic CH3CN, CH3OH (methanol), and dust continuum observations with the Atacama Large Millimeter/submillimeter Array (ALMA) at 349 GHz with an angular resolution of 0′′.1. We exploit the CH3CN (19K–18K) K-ladder, with excitation energies ranging from 168 K (for K = 0) to 881 K (for K = 10), to probe, at different radii, the physical conditions in the accretion disk of an early-type young star. We targeted the star-forming region G023.01−00.41, at a trigonometric distance of 4.59

$^{+0.38}_{-0.33}$





−0.33

+0.38



 kpc from the Sun (Brunthaler et al. 2009), where we recently revealed the accretion disk around a young star of 104.6 L⊙, corresponding to a ZAMS star of 20 M⊙ (Sanna et al. 2019, their Fig. 1); the disk was imaged by means of spectroscopic ALMA observations of both CH3CN and CH3OH lines at 0′′.2 resolution inthe 230 GHz band. The disk extends up to radii of 3000 au from the central star where it warps above the midplane; here, we resolve the outer disk regions in two apparent spirals projected onto the plane of the sky. We showed that molecular gas is falling in and slowly rotating with sub-Keplerian velocities down to radii of 500 au from the central star, where we measured a mass infall rate of 6 × 10−4 M⊙ yr−1 (Sanna et al. 2019, their Fig. 5). The disk and star system drives a radio continuum jet and a molecular outflow aligned along a position angle of 57°, measured east of north (Sanna et al. 2016, their Fig. 2); their projected axis is oriented perpendicular to the disk midplane whose inclination with respect to the line-of-sight was estimated to be less than 30° (namely, the disk is seen approximately edge-on; Sanna et al. 2014, 2019). Previously, we also measured the average gas conditions over the same extent of the whole disk, by means of Submillimeter Array (SMA) observations of the CH3CN (12K–11K) emission, and we estimated a kinetic temperature of 195 K and CH3CN column density of 5.1 × 1016 cm−2 (Sanna et al. 2014, their Fig. 2 and Table 4).
Citation: Sanna et al. 2016
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Citation: Sanna et al. 2014
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The initialization of solar flares remains an unsolved problem. Early ideas on how the initialization might occur were described by Norman & Smith (1978). They argued that flare process cannot start in the entire flare volume at one instant, and proposed that the flare onset was localized in a small part of an active region, from which the energy release extends as dissipation spreading process throughout the flare volume. Two types of agents that may lead to this kind of a dissipation process were addressed: electron beams and shock waves. These agents can trigger flares at large distances from their initial locations, causing sympathetic (simultaneous) flares or leading to a sequential flare energy release in one active region (Liu et al. 2009; Zuccarello et al. 2009). These triggering processes were numerically studied by Karlický & Jungwirth (1989) and Odstrčil & Karlický (1997). Karlický & Jungwirth (1989) assumed that electron beams, penetrating into the current sheet in the magnetic reconnection region, generate Langmuir waves. Then, using the particle-in-cell model, the authors studied the effects of these electrostatic waves on the plasma system. Sufficiently strong Langmuir waves were found to be able to generate ion-sound waves through the three-wave decay process (Bárta & Karlický 2000). These ion-sound waves increase electrical resistivity in the current sheet system, which results in the onset of the energy dissipation. Thus, the electron beams are able to cause magnetic reconnection. Odstrčil & Karlický (1997) studied the mechanism for the flare trigger by shock waves. They used a 2D magnetohydrodynamic model with the MHD shock wave propagating towards the current sheet. A portion of the shock wave passed through the sheet, and the rest was reflected. Nothing occurred at the very beginning of the wave-current sheet interaction. However, after some time, specific plasma flows around the current sheet were formed, which led to the start of magnetic reconnection. This shows that for reconnection to be triggered, the enhanced electrical resistivity as well as the plasma flows are important.
Citation: Bárta & Karlický 2000
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Given their very large column densities, the most obscured sources can effectively be detected in the X-rays at rest-frame energies 





 because their primary continuum is strongly suppressed at softer energies. This can be currently done: (i) locally (





) by targeting bright sources (e.g., 





; Baumgartner et al. 2013) Seyfert-type (





 ) with hard X-ray (





 keV) surveys, such as those performed by Swift/BAT and INTEGRAL (Krivonos et al. 2007; Ajello et al. 2008); and (ii) at high redshifts (





) with the most sensitive Chandra/XMM-Newton observations of the deep/medium survey fields (e.g., Civano et al. 2016; Luo et al. 2017). Through either spectral or hardness ratio analysis, they allow one to quantify and characterize the obscured Compton-thin (





) AGN population and further shed light on the known decreasing trend between the numerical relevance of this population compared to all AGN (absorbed fraction) and the source luminosity (Lawrence & Elvis 1982; Gilli et al. 2007; Burlon et al. 2011; Buchner et al. 2015) and its redshift evolution (La Franca et al. 2005; Ballantyne et al. 2006; Treister & Urry 2006; Aird et al. 2015a; Buchner et al. 2015; Liu et al. 2017). They also allow exploration of the importance of the CT population, although with different constraining power and different non-negligible degrees of bias—especially at the highest column densities and lowest luminosities (e.g., Burlon et al. 2011; Brightman et al. 2014; Buchner et al. 2015; Lanzuisi et al. 2015; Ricci et al. 2015). Indeed, the large diversity in the spectral shapes, as well as poorly explored observational parameters in low counting regimes26

26
I.e., at the highest column densities or at the high/low-energy spectral boundaries where the instruments are less sensitive.
 such as the high energy cut-off and the reflection strength at high energies (Treister et al. 2009; Ballantyne et al. 2011, hereafter BA11), the scattered fractions at low energies (Brightman & Ueda 2012; Lanzuisi et al. 2015), or physical parameters such as the Eddington ratio (Draper & Ballantyne 2010), may further introduce uncertainty or biases, enlarging the possible range of the fraction of CT sources to an order of magnitude (Akylas et al. 2012) or even significantly reducing their importance (Gandhi et al. 2007). Indeed, given the paucity of CT sources effectively contributing to the CXB missing flux, the most recent population-synthesis models have tried to explain the CXB missing component as mainly a pronounced reflection contribution from less obscured sources with a reduced contribution by CT AGN (Treister et al. 2009; Ballantyne et al. 2011; Akylas et al. 2012).
Citation: Ballantyne et al. 2006
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