adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021ApJ...910..163D (fulltext)
while gradually decreases with increasing , as can be seen from Equation ( 57 ). Figure 14. Normalized local effective sound speed () and normalized effective local vertical half-thickness () as functions of the normalized external pressure . The value of is maximum (=1.061) at (as shown by the dashed horizontal and vertical lines). Appendix E Notes on the Generalized Toomre Criterion In the limit of flux-freezing, the dispersion relation isTo minimize ω 2 , the criteria are d ( ω 2 )/ dk = 0 and d 2 ( ω 2 )/ dk 2 > 0 at , yieldingNow, to obtain the instability criterion, we set ω 2 0 at , which givesyieldingIn the dimensionless form, it becomeswhereSee Section 2.6 for further discussion. Appendix F Notes on the Stationary Field Limit In the limit of stationary magnetic fields, , the resulting dispersion relation can be obtained using Equations ( 42 )–( 45 ). It follows that where A 1 , A 2 , θ , and γ are explicitly written in Section 2.4 . Recall that θ and γ represent the case of AD and OD, respectively. Simplifying each individual term of Equation ( F2 ) yields For the case of only OD (set θ = 0), combining Equations ( F3 ), ( F5 ), and ( F6 ), we obtain the resulting dispersion relation and corresponding growth timescale as shown in Equations ( 91 ) and ( 92 ). For the case of only AD (set γ = 0), combining Equations ( F3 ), ( F4 ), and ( F6 ) we obtain the resulting dispersion relation and corresponding growth timescale as shown in Equations ( 95 ) and ( 96 ). Appendix G Additional Figures of the Normalized Growth Timescale versus Length Scale Figure 15 shows the curves of normalized growth timescale as a function of normalized length scale for different normalized rotation Q = 0, 0.1, 0.2, 0.3, 0.4. This figure represents our model of the protostellar disk for distinct normalized mass-to-flux ratios μ 0 = 0.5, 1, 1.1, 2, and 6, with and , corresponding to n n ,0 = 10 11 cm −3 . This is one of our fundamental results, which can be obtained by plotting the normalized form of the full dispersion relation as seen in Equation ( 63 ). Because of the combination of both nonideal MHD effects, the timescale versus length-scale curve attains a minimum at a smaller value of even for the subcritical case μ 0 = 0.5 as compared to the case when only one nonideal MHD effect is present. With the transition of μ 0 from subcritical to supercritical, these curves gradually approach that of the hydrodynamic case. See Sections 3.2 and 3.3 for a detailed discussion. Figure 15. Normalized growth time of the gravitationally unstable mode as a function of the normalized wavelength for models with a fixed normalized ohmic diffusivity and neutral–ion collision time for different normalized mass-to-flux ratios μ 0 = 0.5, 1, 1.1, 2, and 6. Each figure shows the timescale curves for models with different normalized rotation Q = 0 (black), 0.1 (red), 0.2 (blue), 0.3 (green), and 0.4 (magenta). Appendix H Monochromatic Perturbation Here we focus on the form of
Paper: 2020MNRAS.491.4843R (acknowledgments)
ACKNOWLEDGEMENTS We are very grateful to Alberto Bolatto and Tony Wong for providing us with a compilation of H i masses that they made for galaxies from the EDGE-CALIFA survey (see the references listed in item ii of Section 3 ), and to Ed Elson for providing us with measurements of mass and specific angular momentum that he made for galaxies from the WHISP survey (Elson 2017 ). In addition, we are very grateful to Oscar Agertz, Alberto Bolatto, Françoise Combes, John Forbes, Mark Krumholz, Claudia Lagos, Lorenzo Posti, and Florent Renaud for useful discussions. We are also very grateful to an anonymous referee for insightful comments and suggestions, for further constructive criticism, and for encouraging future work on the topic.
Paper: 2016ApJ...824..110O (acknowledgments)
This work was funded in part by National Science Foundation AST Award 1211005. M.P.H. acknowledges support from NSF/AST-1107390 and the Brinson Foundation, and R.A.F. acknowledges support from NSF/AST-0847430. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS website is www.sdss.org . SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the universe (IPMU)/University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatory of China, New Mexico State University, New York University, University of Notre Dame, Observatário Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autónoma de México, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University. Facilities: Arecibo , Sloan.
Paper: 2017ApJ...835..105R (fulltext)
distributions could be the following. For a given mass of gas and dust and assuming a homogeneous distribution of the material, it is more difficult to have CT lines-of-sight if the inner radius of the absorbing material is at larger distances from the X-ray source than that for local AGNs. The inner radius of the dust around hot DOGs can be calculated from the dust sublimation radius (e.g., Nenkova et al. 2008b ):where is the sublimation temperature of the dust grains. Considering silicate dust grains ( K) and the average bolometric luminosity of hot DOGs reported by Assef et al. ( 2015 ; ), we find that the inner radius of the dust is . This is much larger than the typical value expected for Swift /BAT AGNs (, considering ). It should be remarked that, while these distances might be systematically smaller by a factor of ∼3, as found by near-IR reverberation studies (Kishimoto et al. 2007 ), the ratio between the of local AGNs and hot DOGs would be the same. Therefore, if hot DOGs have a dust mass comparable to that of local AGNs and gas and dust are strongly coupled, a difference in the column density distribution is expected. While there might be a significant fraction of dust-free material within the sublimation radius, this material is also likely to be highly photo-ionized by the very luminous AGN. Moreover, as shown by Díaz-Santos et al. ( 2016 ) for WISE J2246−0526, it is possible that in hot DOGs, due to the strong radiation pressure, the gas is being blown away isotropically. Hot DOGs could be very different from local, less luminous AGNs, where the bulk of the gas and dust is believed to be distributed in a torus-like structure, and might represent a short-lived transition phase between heavily obscured and unobscured AGNs (Bridge et al. 2013 ), similar to red quasars (e.g., Urrutia et al. 2008 ; Banerji et al. 2012 ; LaMassa et al. 2016 ). The idea that this phase might happen following a major merger is supported by the recent results of Fan et al. ( 2016b ) based on Hubble Space Telescope /WFC3 images. In a study of SCUBA observations, Jones et al. ( 2015 ) showed that hot DOGs have an excess of SMG neighbors, which would increase the chances of a merger. These results are in agreement with the idea that the AGN unification model might not be valid at high luminosities, where galaxy mergers are more important than secular processes in triggering accretion onto the SMBH (e.g., Treister et al. 2012 ). 5.2. Are Hot DOGs Intrinsically X-Ray Weak? AGNs show a strong positive correlation between the 2–10 keV and the mid-IR luminosity (at 6 μ m or 12 μ m; e.g., Lutz et al. 2004 ; Ichikawa et al. 2012 ; Mateos et al. 2015 ), as confirmed by high angular resolution (∼0.35 arcsec) mid-IR studies (e.g., Gandhi et al. 2009 ; Levenson et al. 2009 ; Asmus et al. 2015 ) of AGNs in the Seyfert regime (). In the quasar regime (), Stern ( 2015 ) found evidence of a flattening of the relationship for , with most sources being fainter than
Paper: 2018A&A...618A.116P (fulltext)
of Gaia’s astrometry. The V -band bolometric correction of − 0.847 ± 0.075 was derivedusing the polynomial equations from Flower (1996) combined with the coefficients and the solar bolometric magnitude from Torres (2010) . Assuming no reddening ( A v = 0 mag), we found a stellar radius of R ⋆ = 0.622± 0.051 R ⊙ , which agrees with the spectroscopic radius derived using SpecMatch-Emp (cf. Table 2 ). We finally converted T eff , R ⋆ , and [Fe/H] into stellar mass M ⋆ and surface gravity log g ⋆ using the empirical relations of Mann et al. (2015) and Monte Carlo simulations. We found that GJ 9827 has a mass of M ⋆ = 0.637 ± 0.051 M ⊙ and a surface gravity of log g ⋆ = 4.657 ± 0.042 (cgs), which agrees with the spectroscopic gravity derived using SME (cf. Table 2 ). According to the analysis performed with SpecMatch-Emp , the three stars 2 whose spectra best match the HARPS and HARPS-N spectra of GJ 9827 have masses between 0.62 and 0.64 M ⊙ , confirming our results. The derived stellar mass and radius are are given in Table 1 . 3.3 Stellar activity and rotation period The K2 light curve of GJ 9827 displays a quasi-periodic photometric variability with a peak-to-peak amplitude of about 0.4 % ( Fig. 2 , left panel). Given the late spectral type of the star (K6 V), the observed photometric variation is very likely caused by active regions (sun-like spots and plages) crossing the visible stellar hemisphere as the star rotates about its axis. This is corroborated by the detection of emission components in the cores of the Ca II H K lines ( Fig. 3 ), from which we measured an average S-index of 0.677 ± 0.034 and 0.739 ± 0.021 using the HARPS and HARPS-N spectra, respectively. Fig. 2 Left panel : Gaussian process regression model applied to the K2 light curve. The black points are the K2 light curve, and the purple curve and shaded band are the GP model predictive mean and 1-sigma uncertainty. Right panel : K2 light curve folded to the P rot ∕2 stellar rotational period. Fig. 3 Cores of the Ca II H and K lines of GJ 9827 as observed with HARPS. Applying the auto cross-correlation technique to the K2 light curve, Niraula et al. (2017) and Rodriguez et al. (2018) found that the rotation period P rot of the star is either ~17 or 30 days. A Gaussian process (GP) analysis of the K2 light curve (Sect. 5.2 ) yields a bimodal posterior distribution with rotational periods peaking at 15.1 ± 1.6 and 30.7 ± 1.4 days, and thus does not provide a conclusive result about the rotation period of the star. However, we note that the ratio between the two measurements is close to 2, suggesting that the first might be the harmonic of the second. A visual inspection of the K2 time series photometry reveals that there are two dips whose minima occur at BJD TBD -2454833 ≈ 2922 and 2971 days, with a duration of ~20 and 16 days, respectively ( Fig. 2 , left panel). If the observed dips are caused by active regions crossing the visible hemisphere of GJ 9827, the rotation period
Paper: 2019MNRAS.487L..30T (acknowledgments)
ACKNOWLEDGEMENTS This work was supported by the Russian Science Foundation grant 19-12-00423. We also acknowledge the support from the Academy of Finland travel grants 317552, 324550 (SST), and 322779 (JP), the Väisälä Foundation (SST), the Netherlands Organization for Scientific Research Veni Fellowship (AAM), the DFG grant WE 1312/51-1 (VFS), and the German Academic Exchange Service (DAAD) travel grant 57405000 (VFS). We grateful to the NuSTAR team for approving the DDT observation of A 0535+262.
Paper: 2015MNRAS.450.2739M (acknowledgments)
BDM acknowledges helpful conversations with Damiano Caprioli, Koji Mukai, Jeno Sokoloski, Andrey Vlasov, and Bob Williams. We thank Fred Walter for providing optical data on nova Sco. BDM gratefully acknowledges support from NASA Fermi grant NNX14AQ68G, NSF grant AST-1410950, and the Alfred P. Sloan Foundation. We thank the reviewer, Guillaume Dubus, for helpful criticism and suggestions that led to an improvement of the manuscript.
Paper: 2016A&A...590A...3P (fulltext)
the galaxy power spectrum in order to arrive at predicted BAO constraints. The previous Euclid forecasts ( Amendola et al. 2013 ) are currently being updated with the new HLFs and updated instrument parameters, and we anticipate that the public documents will be updated soon. The HLFs presented here have already been incorporated in the most recent WFIRST-AFTA science report ( Spergel et al. 2015b ). Appendix A: Cosmic variance This Appendix describes the treatment of cosmic variance in the Model 3 fits. In the linear regime, the cosmic variance error covariance between two luminosity function bins and coming from the matter density field is given by where is the number of independent fields, is the real-space matter power spectrum at redshift , is the growth rate (which boosts the cosmic variance in narrow-band surveys due to redshift-space distortions), and is the window function, the Fourier transform of the survey volume, normalized to . We have used this result here assuming a bias of (from a semianalytic model, Orsi et al. 2010 , although there is evidence that the bias of star-forming galaxies might be higher; see e.g. Geach et al. 2012 ). This reduces the cosmic variance matrix to (Here all the entries in the covariance are constant.) The HiZELS error bars do not incorporate a contribution from cosmic variance. However, we can estimate it from Eq. ( A.1 ) assuming the geometry of independent boxes of size deg each. The depth in the radial direction is given by the width of the narrow-band filter, and is , 0.030, 0.032, and 0.032 at , 0.84, 1.47, and 2.23 respectively. The faintest bins in HIZELS at come from the HAWK-I camera, and the survey volume is smaller in this case: it is a single field, with size deg, and width . For the 4 redshift bins and the luminosity function bins where the full field has been observed, the implied diagonal elements of the covariance are 0.100, 0.045, 0.032, and 0.025. For the HAWK-I data (faintest objects at ), we find a variance of 0.256. A more subtle issue is that the above procedure assumes that the bias is independent of . This assumption has been commonly used for the purpose of forecasting H survey performance and its dependence on survey design. However, in combination with Eq. ( A.1 ), it implies that the cosmic variance contributions in each bin are perfectly correlated. That means that a fit using Eq. ( A.1 ) will assume that the shape of the HLF has no cosmic variance: the cosmic variance term will instead allow only the normalization to float up and down with an uncertainty given by Eq. ( A.1 ). Since cosmic variance is the largest contributor to the errors in some luminosity ranges, the procedure above could lead to fit results that are artificially well-constrained, if the bias is in fact dependent on . There is no reason for to be exactly zero, although for star-forming galaxies it is not obvious which sign to expect. We have thus explored the possibility of averaging the covariance matrix
Paper: 2016MNRAS.457..351Y (acknowledgments)
We thank Tony Pan for useful comments on X-ray emission from PPI SNe. This work has been partly support by the grants-in-aid for Scientific Research (24244028, 26400271, 26800100) from the MEXT of Japan and WPI Initiative, MEXT, Japan.
Paper: 2019ApJ...885...36H (fulltext)
and fast (up to 0.1 local sound speed) accretion happening at disk surface, which is similar to the flow pattern in global ideal MHD simulations (Zhu Stone 2018 ). A similar trend can be seen in the radial mass flux ( ρv r ) plot in the lower middle panel. The Rφ and zφ components of total stress are plotted in the rightmost two panels. The Rφ stress at both radii are close to zero at midplane, as already shown by very low midplane α in Figure 4 . Almost zero Rφ stress at different radii also suggests that midplane accretion or decretion is not driven by the Rφ component. As calculated in Section 3 , the vertical ( z ) gradient of also contributes to . In the zφ stress panel, maximizes at midplane, which is in agreement with the ρv r spike in the lower middle panel. At R = 1.33 (13.3 au), the midplane decretion is driven by zφ stress’s negative z gradient. The surface accretion is related to the two positive z gradient parts around disk surface. The differences of at these two radii also suggest very different magnetic field structure. Figure 5. Various quantities along the vertical direction at R = 1 (blue) and R = 1.33 (orange, local diffusion maximum at the CO 2 snow line). The quantities are averaged over both the azimuthal direction and time (from t = 49 to t = 50 T 0 with Δ t = 0.1 T 0 ). Top row from left to right: gas density, gas radial velocity to local sound speed ratio, R – φ stress; bottom row: inverse of plasma β , radial mass flux, and z – φ stress. The origin of the differences in stress structure can be understood from the 2D plot on the z – R plane, showing how magnetic field is evolved in Figure 6 . At the beginning of the simulation, the magnetic field only has a vertical ( z ) component, so B φ , B R = 0 everywhere. Apart from the dominant Keplerian shear ( dv φ / dR ) at midplane, the disk is also differentially rotating vertically ( dv φ / dz ), as midplane is the fastest and the atmosphere portion, which is mostly pressure gradient supported, rotates much slower. After the simulation starts, the field lines closer to midplane are dragged toward the rotation direction, making the B φ positive below midplane and negative above midplane. The motion along the r direction is even more complicated. From t = 0.5, at the snow line, the velocity shear between wind and surface accretion forms a vortex-like flow pattern that drives gas infall to the midplane. This gas motion drags field lines toward midplane, making a kinked form of magnetic field. Accretion at midplane also drags field lines inward, amplifying horizontal field component B R , and the polarity of B R changes sign in the accretion stream. Close to midplane, Keplerian shear stretches horizontal field lines, just like the growth of azimuthal fields in the linear phase of MRI. The process makes the field kink at midplane even sharper. The vertical gradient of the B φ component tends to get steeper with AD (Brandenburg Zweibel 1994 ). The strength of AD is
Paper: 2019ApJ...887..219B (fulltext)
property of the galaxy allows us to isolate its effects and explore its consequences. Here, we will discuss two criteria. The first is the metallicity threshold, already mentioned, that provides an upper mass limit on galaxies that will be able to host a GRB (see Equation ( 1 )). The second criterion we explore is that a given galaxy will be more likely to host a GRB if it has a high specific SFR. We implement this by assuming that to host a GRB, a galaxy will need to have a specific rate above a given threshold value that we further assume to be independent of redshift. Again, this provides an upper stellar-mass limit for galaxies hosting GRBs. Figure 2 shows examples of the critical masses as a function of redshift for three different values of S * thresholds, as well as three different metallicity thresholds. The critical mass increases more rapidly with increasing redshift for the S * thresholds than for the metallicity thresholds. In addition, at a fixed redshift, the critical mass increases with an increasing metallicity threshold, but decreases with an increasing specific rate threshold. Galaxies with masses above the critical mass values have, in this model, a very low probability of producing a GRB. Figure 2. Critical galaxy stellar mass (in solar masses) as a function of redshift, z , for three different values of S * (in Gyr −1 , solid curves) and three different metallicity thresholds (dashed curves). The critical mass increases faster with increasing redshift for the S * thresholds. For galaxies more massive than the critical mass, GRB production is suppressed. For comparison we also show the redshift evolution of M 0 and M 1 (dashed–dotted). It is clear that for both threshold types ( S * and metallicity), in fact, very few if any GRBs would be produced by galaxies in the mass range out to redshifts z ≲ 2. Indeed, galaxies of mass ∼10 11 M would not be hosting a GRB out to a redshift of z ∼ 3–4 in either scenario, as observed by Perley et al. ( 2016b ). Only in the case of relatively low values of S * and rather high values of the metallicity threshold does the critical mass reach the characteristic mass, M 1 , of Equation ( 2 ), at the highest redshifts shown. Massive galaxies, around M 1 or higher, are only likely to host a GRB at high redshift and thus contribute to the GRB-probed star formation if of high metallicity or of low specific SFR. 3. The SFR Fraction We now proceed as in Robertson Ellis ( 2012 ) and define the fraction of star formation occurring in galaxies with M M cr as:Here, P ( x , y ) = Γ( x , y )/Γ( x ), with Γ( x ) the gamma function,is the lower incomplete gamma function, and the second equality in Equation ( 5 ) holds for the functional forms adopted for and SFR( M , z ). Recall that ψ ( z ) is also the fraction of star formation producing a GRB (Robertson Ellis 2012 ). In Equation ( 5 ), t cr = M cr (1/ M 0 + 1/ M 1 ), and the redshift dependence of ψ ( z ) is only through the mass scales if the