adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017ApJ...847...85Y (acknowledgments)
We thank NuSTAR PI Fiona Harrison and Belinda Wilkes for granting NuSTAR and Chandra DDT observations of SGR J1935+2154 during the 2015 and 2016 outbursts, respectively. We also thank the Swift team for performing the monitoring of the source during all of its outbursts. G.Y. and C.K. acknowledge support by NASA through grant NNH07ZDA001-GLAST. A.J. and J.W.T.H. acknowledge funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013) ERC grant agreement no. 337062 (DRAGNET). The Arecibo Observatory is operated by SRI International under a cooperative agreement with the National Science Foundation (AST-1100968) and in alliance with Ana G. Méndez-Universidad Metropolitana and the Universities Space Research Association. We would like to thank Arecibo observatory scheduler Hector Hernandez for the support during our observations. A.J. thanks Daniele Michilli for helping with the single-pulse search analysis. We thank the anonymous referee for their careful reading and insightful comments that improved the quality of the manuscript. Software: CIAO ( http://cxc.harvard.edu/ciao/ ), SAS (v14.0.0, https://www.cosmos.esa.int/web/xmmnewton/sas ), xrtpipeline (v13.2), nustardas (v1.5.1, https://heasarc.gsfc.nasa.gov/docs/nustar/analysis/ ), HEASOFT (v6.20, https://heasarc.nasa.gov/lheasoft/ ), ChaRT ( http://cxc.harvard.edu/ciao/PSFs/chart2/ ) MARX ( http://space.mit.edu/CXC/MARX/ ), PRESTO (Ransom 2001 ; Ransom et al. 2002 , 2003 ), XSPEC (v12.9.0k; Arnaud 1996 ).
Paper: 2019MNRAS.489L..42M (fulltext)
standard packages in iraf . 1 Aperture photometry was carried out using the task daophot ii (Dominion Astrophysical Observatory Photometry software, Stetson 1987 ). Signal-to-noise ratio was found to peak for a photometric aperture radius nearly two times the seeing disc whose FWHM was determined by averaging the profiles of five fairly bright, unsaturated stars recorded in the same CCD frame. The measured instrumental magnitudes were then used to derive the ‘differential light curves’ (DLCs) of the target AGN relative to two (steady) comparison stars, as well as the star−star DLC for the session (see Goyal et al. 2013 , for details of the procedure). In Fig. 1 , we show the DLCs obtained from one of the 30 sessions; the DLCs for all 30 monitoring sessions are presented in the online Fig. 1. Figure 1. An example of the DLCs obtained in the present study. The target AGN is the BAL quasar J090552.41+025931.5. DLCs for all the 30 sessions are presented in online Fig. 1. The date and duration of monitoring and the telescope used are mentioned at the top. The profile in the upper panel presents the DLC of the chosen two comparison stars (‘star–star’ DLC). The two middle profiles display the DLCs of the BAL quasar relative to the two comparison stars, as mentioned in the labels on the right side. The profile in the bottom panel shows the variation of the seeing disc (full width at half-maximum, FWHM) through the monitoring session. 4 STATISTICAL ANALYSIS AND RESULTS Following Goyal et al. ( 2013 ), we applied the widely used F η test to the DLCs of the target AGN relative to the two comparison stars (s1, s2). The parameter F η computed for the two AGN DLCs is defined as Here Var( q − s j ) is the variance of the ‘AGN− j th comparison star’ DLC, where j = 1,2 and σ i ,err ( q − s j ) are the photometric error of the N individual data points in the DLC (as returned by the daophot routine). For each of the two DLCs, i = 1 to N and η = 1.5 (see Goyal et al. 2012 ). For each of the two DLCs of the target AGN, online Table 2 (Column 7) compares the computed values of F η with the critical value of F (=). The values of α are set at 0.05 and 0.01, corresponding to 95 per cent and 99 per cent confidence levels for INOV detection. If the computed F η for a DLC exceeds , the null hypothesis (i.e. no variability) is rejected at the corresponding confidence level. We thus classify a DLC as variable (‘V’) if its computed F η > F c (0.99); probably variable (‘PV’) if the F η lies between F c (0.95) and F c (0.99), and non-variable (‘NV’) in case F η F c (0.95) (Goyal et al. 2013 ). Column 10 in the online Table 2 lists the session averaged photometric accuracy of the measured differential magnitudes . It is estimated using the two AGN-star DLCs and is nearly always better than 3 per cent (median = 2.0 per cent). 5 DISCUSSION AND CONCLUSION The main result from this study (online Table 2) is the non-detection of INOV in any of the 30 sessions devoted to the
Paper: 2021MNRAS.505.3755T (fulltext)
the r- process event would overpower signatures left over from any neutron-capture processes that may occur before or after. But to make matters more complication, at least two stars in Ret II display rather low Sr and Ba values (not shown in the figure but see fig. 3 in Ji et al. 2016c ), and the UFDs without clear r -process enrichment do contain a lot of stars with significant amount of s -process material. Nevertheless, our results clearly show that for stars with measurable neutron-capture element abundances, AGB stars alone are not sufficient source to explain the origin of Ba and Sr for these stars. However, we also note that likely more stars exist in these systems that have lower Sr and Ba measurements than what is represented with the current observed sample. 2 Those stars (1) might not yet have such measurements due to being faint and corresponding poor data quality especially in the blue spectral regime preventing even meaningful upper limits, (2) or these stars simply have not even been observed yet at all. Such a putative low Sr-Ba-population may well be explained solely with an AGB origin scenario. Future observations can hopefully shed further light on this issue so that our predictions could be further put to the test. In fact, there are hints of this being the case. Metal-poor halo stars have [Ba/Fe] going down to about [Ba/Fe] ∼ −2.0 which is roughly at the level of the simulated stars formed last in our simulation (light green circles in the figure). Furthermore, all stars with red symbols with [Ba/Fe] > −2.0 form a branch not unlike that of MW stars albeit offset to lower [Ba/Fe] values. The lower of these stars overlap with the top end of the simulated later-time stars – clearly more overlap would be achieved with improved spectral line detections should additional data become available (e.g. with the Giant Magellan Telescope ). Figure 3. Observed abundances in UFDs overlaid with our RMS model results for [Ba/Fe] (top panels) and [Sr/Fe] (bottom panels). Symbols and colours are the same as in Fig. 2 . The simulated abundances agree well with the observed abundances, including the largely flat trend with [Fe/H]. The scatter of [Ba/Fe] and [Sr/Fe] in each galaxy is also an interesting piece of information. Observationally, Bootes I (Boo I), Carina II (Car II), and Coma Berenices (ComBer) show significant scatter while most other UFDs do not. We also note that the number of observations is limited due to observational difficulties such as stars being too faint to obtain higher signal-to-noise observations while also requiring high spectral resolution. Therefore, even for UFDs with little apparent scatter, significant scatter in [Ba/Fe] and [Sr/Fe] may still be present within. In our simulation, large scatter is observed both in the L- and M-UFDs. The Ba content of S-UFD is too little, and therefore we cannot discuss the scatter. There are two kinds of inhomogeneities to diversify the abundances of the neutron-capture
Paper: 2020MNRAS.492.1432Z (acknowledgments)
ACKNOWLEDGEMENTS We want to thank all people in social networks that shared their pictures and data and that finally allowed professional and amateur astronomers to work on this. We are particularly grateful to the MIDAS survey for discovering the impact and announcing early without any embargo. This generosity allowed all of us to discover the data hidden in pictures and videos. We especially want to thank the careful review of the first versions of the manuscript made by Dr. Rodrigo Leiva from Southwest Research Institute, his suggestions and comments were very important for our work. We would also like to thank all the amateur astronomers that contributed the images necessary for this work: Petr Horálek from Cape Verde, Gregory Hogan from USA, Fritz Helmut Hemmerich from Canary Islands, Libor Haspl from Czech Republic, Robert Eder Artis from Austria, Dr. Sighard Schräbler and Dr. Ulrike Löffler from Germany, and a couple more amateur astronomers who provided their images, but were unable to meet the scientific precision required. Last, but not least, we greatly appreciate the insightful comments of Maria Gritsevich, Janne Sievinen, and an anonymous colleague. Their skepticism, criticism, and key suggestions shaped this work to its final form.
Paper: 2017MNRAS.470.2314M (fulltext)
Section 3.3 ). A two-sided KolmogorovSmirnov (KS) test on the redshift and i -band magnitude distributions of the matched samples return probabilities of 0.999863 and 0.984483, respectively, thus there is no evidence for rejecting the null hypothesis that the two histograms are drawn from the same underlying distributions of these two observables. Figure 1. The i -band luminosity and redshift ranges for the quasars detected in the FIR at 2 (red points), and the matched sample of quasars undetected at 1 (blue points). The histograms on the bottom and side show the normalized distributions of the respective populations in redshift and L i . Fig. 2 shows the cumulative distributions of C iv blueshift for the FIR-detected and FIR-undetected matched samples. The FIR-detected quasars show larger blueshifts than is expected from their FIR-undetected counterparts, with a two-sided KS test returning a probability that the two distributions are drawn from the same underlying sample of 10 12 . Using 3 and 4 FIR detections instead of the 2 detections, the KS test returns probabilities of 10 8 and 0.0001 that the FIR-detected and FIR-undetected distributions are from the same underlying samples. The 4 sample contains only 140 FIR-detected objects, and thus suffers from the small sample size. Recall that BALQSOs have been removed from the sample, so the difference cannot be attributed to absorbed flux in the blue wing of the emission line. While a nuclear outflow is not always accompanied by a FIR excess, for the sample as a whole, at a given optical luminosity, FIR-bright quasars have larger C iv blueshifts than FIR-undetected quasars. Figure 2. Cumulative distribution of the blueshift of the C iv emission line for the quasars detected in the FIR at 2 (red solid line), and those undetected at 1 (blue dashed line). The FIR-detected quasars have larger C iv blueshifts, indicating more prominent nuclear-driven outflows. The bottom panel shows normalized histograms of the FIR-detected and FIR-undetected C iv blueshifts, in red and blue, respectively. 3.3 Red or reddened quasars Vigorous SF is known to be a dusty process, so if the FIR flux is at least partly from SF, we may expect the FIR-detected quasars to be reddened by dust in the host galaxies, as evidenced by redder optical colours with respect to the general quasar population. Using the same FIR-detected and FIR-undetected matched samples as before, we compare the SDSS g r colours of the two samples. For these luminous quasars, the optical photometry is entirely dominated by emission from the quasar, even if accompanied by a starburst in the host galaxy. To remove the redshift dependence of quasar colours, the colour of an unreddened model quasar similar to that from Maddox etal. ( 2008 ) is subtracted from each object to create a normalized colour. Using the normalized colour, with the redshift dependence removed, creates a distribution that better illustrates departures from the median colour of a
Paper: 2019MNRAS.486L..20K (acknowledgments)
ACKNOWLEDGEMENTS JMDK is supported by the German Research Foundation (DFG; KR4801/1-1), the European Research Council (ERC; ERC-StG-714907), and SFB 881 (subproject B2) of the DFG. The participants of the Lorentz Center workshop ‘Formation of Stars and Massive Clusters in Dwarf Galaxies over Cosmic Time’ (2019 February 18–22) and particularly the SOC chair, Rychard Bouwens, are thanked for inspiring conversations that led to the development of this work. N. Bastian, I. Cabrera-Ziri, M. Chevance, N. Choksi, M. Gieles, O. Gnedin, S. Larsen, M. Maseda, J. Pfeffer, M. Reina-Campos, A. Sills, S. Trujillo-Gomez, C. Usher, E. Vanzella, and an anonymous referee are thanked for helpful feedback.
Paper: 2018ApJ...856...71B (fulltext)
field strength in their study does not hold below 10 G, which is a typical mean field strength for CHs. In our study, the rates of change in mean FIP bias in the small flux regions during their late-decay phase are significantly faster than the rates of change observed in larger decaying ARs. Baker et al. ( 2015 ) and Ko et al. ( 2016 ) find remarkably similar rates of decreasing mean FIP bias in the range [0.004, 0.009] h −1 within the core of ARs over 2–5 days compared to a range of [0.1, 0.2] h −1 for R3, R4, and R7 during their late-decay phase. Once again, the rates of decrease exhibited in the large ARs are maintained over periods of days, whereas in the small regions within the CH, the rapid changes occurred in a matter of hours. 5.4. Spatial Distribution of FIP Bias The spatially resolved composition map in Figure 8 , as well as maps in previous studies (e.g., Baker et al. 2013 , 2015 ; Brooks et al. 2015 ), have revealed that FIP bias does vary over spatial scales of a few arcseconds. Patches of higher FIP bias (>2) were found in the core of the EFRs, which is consistent with the Hinode /EIS observations of large ARs in Baker et al. ( 2013 , 2015 ). There was enriched plasma in the vicinity of loops linking the positive polarity of R1 and the negative polarity of R2 (see patches of FIP bias in the range [2, 3] between the mask contours of Figure 8 (b), which spatially correspond to the loops connecting R1 and R2 in Figures 7 (a), (b) and Movie 2). The magnetic field alignment of R1 relative to R2 was favorable for reconnection between the external loops of each region. The reconnecting loops contain already enriched plasma but it is also possible that the Alfvén waves generated by reconnection may have stimulated the enrichment (e.g., Laming 2004 , 2015 ). Finally, the plasma composition within the two largest EFRs had non-Gaussian distributions ranging from photospheric to coronal FIP bias (Figure 9 ). The distributions were mildly skewed toward the higher end of the distributions, with 32%–36% of the pixels within the masked regions exceeding FIP bias = 2. The lower end of the range of FIP bias values was consistent with previous observations of FIP bias in newly emerged loops (Sheeley 1995 , 1996 ; Widing Feldman 2001 ; Laming 2015 ) and with studies of plasma composition in CHs (Feldman et al. 1998 ; Brooks Warren 2011 ; Laming 2015 ), whereas the upper end of the range, ∼3, was lower than the levels typically found in quiescent AR cores, ∼3–4 (e.g., Baker et al. 2015 ; Del Zanna et al. 2015 ). The fraction of pixels containing FIP bias >3 was small in these regions: ∼4% for R1 and ∼1% for R2. The extent to which plasma is enriched is likely to be affected by the CH environment. EFRs are partially or fully surrounded by CH field containing a large reservoir of unmodified photospheric-composition plasma. Interchange reconnection between the closed loops of the EFRs and the open field of the CHs creates pathways for mixing of
Paper: 2019ApJ...881...32B (acknowledgments)
A.M.B. was a Grote Reber Fellow for a portion of this project, and support for this work was provided by the NSF through the Grote Reber Fellowship Program administered by Associated Universities, Inc./National Radio Astronomy Observatory and the Virginia Space Grant Consortium. A.M.B. also acknowledges support from the Smithsonian Institution as a current Submillimeter Array (SMA) Fellow. A.M.B. would like to thank Karin Öberg for helpful discussions on ice chemistry. C.N.S. wishes to thank the Alexander von Humboldt Foundation for support. Support for B.A.M. was provided by NASA through Hubble Fellowship grant No. HST -HF2-51396 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. E.H. thanks the National Science Foundation for support of his astrochemistry program.
Paper: 2015ApJ...812...83N (acknowledgments)
We thank C. Steidel for providing a reduced HIRES/Keck quasar spectrum. This material is based upon work supported by the National Science Foundation under grant No. 1210200 (NSF East Asia and Pacific Summer Institutes). N.M.N. was also partially supported through an NMSGC Graduate Fellowship and a Graduate Research Enhancement Grant (GREG) sponsored by the Office of the Vice President for Research at New Mexico State University. G.G.K. acknowledges the support of the Australian Research Council through the award of a Future Fellowship (FT140100933). M.T.M. thanks the Australian Research Council for Discovery Project grant DP130100568, which supported this work.
Paper: 2016MNRAS.460.2432H (fulltext)
resolution surrounding the selected minihalo. Each ‘parent’ particle within the most refined region is split into 64 ‘child’ particles, with a minimum mass of m gas = 1.85 M ⊙ and . These refined simulations are then run until the gas approaches the typical onset of runaway collapse at n = 10 4 cm −3 . 3.1.1 Halo 2 cut-out and refinement Once the simulation reaches 10 4 cm −3 all particles beyond 10 comoving kpc from the centre of the collapsing minihalo are removed to conserve computational resources. To achieve a maximum resolution comparable to Halo 1, all particles within the 10 comoving kpc volume are split into two child particles placed randomly within the smoothing kernel of the parent particle. This particle-splitting is repeated twice more; all particles within 8 comoving kpc are replaced by eight child particles; within 6 comoving kpc, each of these child particles is split into an additional 8 for a total of 128 child particles in the most refined region. At each step, the mass of the parent particle is evenly divided among the child particles and the smoothing length is set to , where h is the smoothing length of the parent and N new is the number of child particles created. All particles inherit the same entropy, velocity, and chemical abundances as their parent, ensuring conservation of mass, internal energy, and momentum. While our cut-out technique leads to a rarefaction wave propagating inward from the suddenly introduced vacuum boundary condition, the average sound speed at the edge of the most refined region is ∼1 km s −1 . As a result the wave only travels ∼0.5 pc over the remaining 400 000 yr of the simulation, a negligible fraction of the ∼350 pc physical box size. 3.2 Chemistry and thermodynamics We employ the chemistry network described in detail by Greif et al. ( 2009 ), which follows the abundance evolution of H, H + , H − , H 2 , , He, He + , He ++ , D, D + , HD and e − . All relevant cooling mechanisms are accounted for, including H and He collisional excitation and ionization, recombination, bremsstrahlung and inverse Compton scattering. In order to properly model the chemical evolution at high densities, H 2 cooling induced by collisions with H and He atoms and other H 2 molecules is also included. Three-body reactions involving H 2 become important above n ≳ 10 8 cm −3 ; we employ the intermediate rate from Palla, Salpeter Stahler ( 1983 ), but see Turk et al. ( 2011 ) for a discussion of the uncertainty of these rates. In addition, the efficiency of H 2 cooling is reduced above ∼10 9 cm −3 as the ro-vibrational lines of H 2 become optically thick above this density. To account for this we employ the Sobolev approximation together with an escape probability formalism (see Yoshida et al. 2006 ; Greif et al. 2011b for details). 3.3 Cosmic ray ionization and heating Each time a CR proton ionizes a hydrogen atom, an electron with average energy 〈 E 〉 = 35 eV is produced (Spitzer Tomasko 1968 ). Including the
Paper: 2016ApJ...819..123N (fulltext)
) multi-redshift sample used Spitzer -IRS data to constrain the torus SED. This sample was discussed in great detail by Lusso et al. ( 2013 ), who showed a very good agreement with their results. Thus the data we present here represent well the results of Maiolino et al. ( 2007 ). Figure 9 shows all the NIR–MIR-based compilations of C f , including ours, for the case of complete isotropic dust emission ( b = 1) on the left, and maximum anisotropy ( b = 0) on the right. For the present sample, we only show the case of Bol 1350 = 2. The uncertainty on represents the range in this property that is used to derive the median values. The uncertainty on / is taken from the original papers (Lusso et al. 2013 ; Roseboom et al. 2013 ) or from the present calculations. Given the uncertainties on and , we estimate a combined uncertainty on C f of at least ±0.2 dex at all . We also show, as blue open squares, the median values obtained from Shen et al. ( 2011 ) and shown in Figure 8 left panel (). Figure 9. Covering factor as a function of in large AGN samples. Open black squares: the Mor Netzer ( 2012 ) sample. Full black circles: the Lusso et al. ( 2013 ) sample. Open red circles: the Roseboom et al. ( 2013 ) sample. Open blue circles: the the present z = 2–3.5 sample without the weak-NIR sources. Open blue squares: SDSS sources from the Shen et al. ( 2011 ) catalog with the same redshift and luminosity as the present sample and measured as explained in the text. The left panel shows the covering factors under the assumption of isotropic dust emission. As explained in the text, the Roseboom et al. ( 2013 ) values are scaled up by factors of 1.5–1.8 to allow for the different assumed bolometric correction factors. The parallel dotted lines show the range of ±0.15 dex around the mean value of 0.68. The right panel shows the same data for the case of complete anisotropy ( b = 0 in Equation ( 3 )). The dotted lines indicate a range of ±0.15 dex around 0.4. Figure 10. Median (top) and mean stacks. In each part, the high luminosity group is shown in the upper row and the low luminosity group in the lower row. From left to right: 250, 350, and 500 μ m. The crosses mark the centers of the stacks. The diagrams presented here show that all medians of all the samples used here, at all , are confined to a band of width ±0.15 dex in / around 0.68 for the isotropic case, and 0.4 for the case of complete anisotropy. Because much of the uncertainty in C f is systematic and depends on the poorly known bolometric correction factor, the inclusion of a large number of individual measurements cannot improve the situation by much. Thus, the torus covering factors that are based on NIR–MIR measurements show no indication for a decrease of C f with increasing source luminosity. This finding seems to contradict earlier findings, such as those presented by Lusso et al. ( 2013 ) and Roseboom et al. ( 2013 ). An independent way of estimating the covering factor is to compare the