adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.470.1587A (fulltext)
to the spectroscopic data. This source is less luminous than J0100+2802 and its mass is smaller, according to the estimate obtained by fitting the SED. With the same accretion disc model as before, in fact, we obtain M BH = 1.3 × 10 9 M ⊙ , L disc = 3.9 × 10 46 erg s −1 = 0.23 L Edd , L X = 0.8 L disc ∼ 3 × 10 46 erg s −1 and L X + L disc = 0.41 L Edd . As previously explained for J0100+2802, the value of the black hole mass should be taken as a lower limit. We conclude that both sources, despite the difference in black hole mass, share similar Eddington ratios and similar partition between optical–UV and X-ray luminosities. We can compare our results on ULAS J1120+0641 with those of Mortlock et al. ( 2011 ), who found M BH ∼ 2 × 10 9 M ⊙ (through the virial method) and a disc luminosity of 2.5 × 10 47 erg s −1 (applying a a fiducial bolometric correction taken from Willott et al. 2010 ). Differently from us, the results of Mortlock et al. ( 2011 ) indicate a slightly super-Eddington luminosity. 4.3 Comparison with other powerful quasars It is well-established that the X-ray-to-optical power-law slope parameter α ox of quasars correlates significantly with the ultraviolet 2500-Å monochromatic luminosity ( L 2500 Å : Steffen et al. 2006 ; Just et al. 2007 ). For J0100+2802 with rest-frame 2500-Å flux density, f 2500 Å , estimated from Wu et al. ( 2015 ) and rest-frame 2-keV flux density, f 2keV , estimated from the power-law model, we have parameter α ox with value −1.74 ± 0.01. In Fig. 6 , we show the location of J0100+2802 and the other highest-redshift quasars with z > 6, for which we take α ox and L 2500 Å from the literature (Brandt et al. 2002 ; Farrah et al. 2004 ; Shemmer et al. 2006 ; Moretti et al. 2014 ; Page et al. 2014 ; Gallerani et al. 2017 ), in the α ox – L 2500 Å relation. It is clear that the SED of the ultraluminous J0100+2802 is not abnormal among the highest-redshift quasars and all quasars at z > 6 follow the α ox – L 2500 Å relation like low- and median-redshift quasars. As discussed in Nanni et al. ( 2017 ), which presents a systematic analysis of X-ray archival data of quasars at z > 5.5, these results support the non-evolutionary scenario of SEDs of luminous quasars. For J0100+2802, the inferred value of α ox in Nanni et al. ( 2017 ) is , which is in agreement with ours considering the scatter of the α ox – L 2500 Å relation. Figure 6. Location of J0100+2802 (red star) in the X-ray-to-optical power-law slope parameter α ox versus 2500-Å monochromatic luminosity. The grey dots are quasars from the samples of Just et al. ( 2007 ), Steffen et al. ( 2006 ) and Gibson, Brandt Schneider ( 2008 ). The blue dots are weak-line quasars and PHL 1811 analogues from Luo et al. ( 2015 ). The solid line represents the α ox – L 2500 Å relation from Just et al. ( 2007 ) and the dot–dashed line from Nanni et al. ( 2017 ). The red symbols represent high-redshift quasars with z > 6.0 from the literature (squares from Shemmer et al.
Paper: 2018MNRAS.475.2614E (fulltext)
behind the Sun. Our data include 48 epochs taken before the discovery, starting on 2003 December [upper limit from the Sloan Digital Sky Survey (SDSS) DR9 4 ] up to 2012 March. These data were taken mainly by amateur astronomers. One epoch was secured using the Ultraviolet and Optical Telescope on board the Swift satellite on 2013 September 05.65. However, only upper limits were estimated for the UVW2 (>20.9 mag) and UVW1 (>20.0 mag) bands (V egamag ). Photometric observations were preprocessed following the standard recipe in iraf 5 for CCD images (trimming, overscan, bias, and flat-field corrections). For the NIR exposures, we also applied an illumination correction and background-sky subtraction. For later epochs, multiple exposures obtained in the same night and filter were combined to improve the signal-to-noise ratio (S/N). The SN magnitudes were measured using point-spread-function (PSF) fitting with SNO o P y . 6 No template subtraction was performed since no major differences were found when comparing the photometry obtained through both PSF fitting and template subtraction. The data derived with the two methods are in agreement to within 0.1 mag. In order to calibrate the instrumental magnitudes to standard photometric systems, we used the zero-points and colour terms measured through reference SDSS stars in the field of SNhunt151. The Johnson–Cousins magnitudes of these reference stars were calculated from the Sloan magnitudes following the conversions of Chonis Gaskell ( 2008 ). For the NIR photometry, we used the Two Micron All Sky Survey (2MASS) catalogue as reference for the calibration. Once the local sequence was calibrated, we proceeded to calibrate our transient, even for non-photometric nights. Unfiltered magnitudes from amateurs were scaled to Johnson–Cousins V - or R -band magnitudes, depending on the wavelength peak efficiency of the detectors. Uncertainty estimates were obtained through artificial-star experiments. This is combined (in quadrature) with the PSF fit error returned by daophot and the propagated errors from the photometric calibration. The final calibrated SN magnitudes are listed in Tables B2 and B5 for the optical bands, and in Table B4 for the NIR range. 3.2 Space-Based Observations The SNhunt151 site was also observed by the Hubble Space Telescope ( HST ) with the Ultraviolet-Visible (UVIS; ∼ 0.04 arcsec pixel −1 ) Channel of the Wide-Field Camera 3 (WFC3) in the F 555 W and F 814 W bands. These images were taken on 2016 October 23 as part of the SN snapshot survey GO-14668 (PI: A. V. Filippenko), 1113.1 d after the V -band peak of SNhunt151's Event B (see below). A source was detected at the transient position in all images with root-mean-square uncertainties 0.01 arcsec (Fig. 1 d), through comparison with ground-based, post-discovery Copernico Telescope+AFOSC, and NOT+ALFOSC images taken on 2013 August 31 (seeing 1.16 arcsec) and 2014 August 24 (seeing 0.96 arcsec). The magnitudes of the source in
Paper: 2019ApJ...871..231H (acknowledgments)
We would like to thank the anonymous referee whose comments helped to significantly improve the paper. This work is based on observations made with the NASA/ESA Hubble Space Telescope , obtained from the Data Archive at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. These observations are associated with program 15074. Support for Program number 15074 was provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS 5-26555. The scientific results reported in this article are based in part on data obtained from the Chandra Data Archive. This work has made use of data from the European Space Agency (ESA) mission Gaia ( https://www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium ). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. The work of DS was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. Facilities: HST (WFPC2 and WFC3) , Chandra (ACIS) , Gaia . Software: astropy (Astropy Collaboration et al. 2013 ), DOLPHOT (Dolphin 2000 ), MESA (Paxton et al. 2011 , 2013 , 2015 ), CIAO (Fruscione et al. 2006 ).
Paper: 2017ApJ...846...18T (acknowledgments)
We gratefully acknowledge data provided by the NASA/Space Physics Data Facility (SPDF) and Wind /3DP Data Center. We thank K. Ogilvie, R. Lin, A. Szabo, and L. Wilson III for their support of this work. Also, we thank D. Reames, X. Shao, and the anonymous reviewer for their valuable comments.
Paper: 2018MNRAS.479.5638N (acknowledgments)
ACKNOWLEDGEMENTS This work was supported in part by the NWO DRIFT project (contract 628.002.002). The authors would like to thank Ahmad Mouri Sardarabadi for discussions on an initial idea captured in Naghibzadeh et al. ( 2016b ) which evolved into this paper, Patrick Dewilde for in-depth discussions and comments regarding the algorithm and the paper, Martin van Gijzen for discussions regarding the iteratively reweighted algorithms and Stefan Wijnholds for providing the lofar station data.
Paper: 2015MNRAS.451.1090S (acknowledgments)
The author thanks F. Vincenzo, and V. Grieco for several fruitful discussions, and F. Matteucci for many useful suggestions and for reading the manuscript. The author also thanks an anonymous referee whose comments noticeably improved the paper. The work was supported by PRIN MIUR 2010-2011, project ‘The Chemical and dynamical Evolution of the Milky Way and Local Group Galaxies’, prot. 2010LY5N2T.
Paper: 2017MNRAS.468.4441R (fulltext)
at t check as a function of the initial virial ratio vir for clusters with different initial fractal degrees. The open circles, crosses and open squares show the average value for clusters with D = 1.6, 2.0 and 3.0, respectively. The red and black symbols show the results for the inner orbit and the outer orbit, respectively. Middle panel: half-mass radius at t check as a function of the initial virial ratio vir for clusters with different initial fractal degrees. Symbols and colours as in the left-hand panel. Right-hand panel: velocity dispersion of the cluster members at t check as a function of the initial virial ratio vir for clusters with different initial fractal degrees. Symbols and colours as in the left-hand panel. We found that the initially virialized clusters experience a smaller mass-loss rate than both supervirial and subvirial clusters of the order of 10percent (see left-hand panel of Fig. 6 ). This result is evident also in the left-hand panel of Fig. 7 , in which we show the mass evolution of three realizations of clusters on the outer orbit as a function of their initial virial state for the first 1Gyr of evolution. Once again, such a trend mirrors the value of the relaxation time at t check . We found that initially sub/supervirial clusters evolve with systematically smaller relaxation times from the very early stages. A collapsing system will then exhibit a shorter crossing time, inducing faster relaxation of the system. On the other hand, in a tidally limited system such as a star cluster in a galaxy it is possible that if the cluster is initially expanding the number of escapers in the early stages can be enhanced, once again reducing the number of cluster members and hence the relaxation time of the system. Figure 7. Evolution of the mass (left-hand panel), half-mass radius (middle panel) and velocity dispersion (right-hand panel) of initially spherical clusters ( D = 3.0) on the outer orbit with different initial virial ratios. The dark blue, red and light blue lines show the results of the simulations of clusters with initial virial ratio D 0.2, 0.5 and 0.8, respectively. The vertical black dashed line indicates the time t check at which the properties have been evaluated in Fig. 6 . This trend is also reflected in the size of the clusters. Initially virialized clusters evolve with slightly bigger half-mass radii. However, we also note that such a variation is of the order of only a few percent and is of the order of the variations associated with the random realizations of the clusters (see the middle panels of Figs 6 and 7 ). Similar to the effect of the initial fractal dimension, the initial virial state of the clusters also does not significantly affect the evolution of the velocity dispersion of the cluster members. Finally, we note that also in this case, the discussed trends are quite well reproduced for both the inner and outer orbits. 3.3 Impact of the initial degree of mass segregation on the long-term
Paper: 2018MNRAS.481...36A (acknowledgments)
ACKNOWLEDGEMENTS The authors acknowledge the support from the Program of target financing of the Ministry of Education and science of the Republic of Kazakhstan, Grant No. BR05236730.
Paper: 2020ApJ...899..112S (fulltext)
Hirschmann et al. 2010 ; Jahnke Macciò 2011 ). A closely related issue involves the connection between BH accretion and star formation in the host galaxy. Here, too, no consensus has yet emerged. While some contend that star formation rate (SFR) correlates strongly with AGN luminosity across a wide range of redshifts, implying the simultaneous growth of the BH and its host (e.g., Bonfield et al. 2011 ; Rosario et al. 2012 ; Xu et al. 2015 ; Lanzuisi et al. 2017 ; Dai et al. 2018 ; Stemo et al. 2020 ), others find only moderate or no correlation (Rosario et al. 2013 ; Azadi et al. 2015 ; Stanley et al. 2015 , 2017 ). The disagreement is partly explained by the different timescales of the measured SFR and AGN variability (Hickox et al. 2014 ). At high redshifts (e.g., z > 1), many works do not find a significant correlation between SFR and AGN luminosity (e.g., Shao et al. 2010 ; Rosario et al. 2012 ; Schulze et al. 2019 ; but see Stemo et al. 2020 ). For low redshifts, the correlation is moderate in weak AGNs (e.g., Shimizu et al. 2017 ) but strong in more powerful systems (e.g., Netzer 2009 ; Imanishi et al. 2011 ; Xia et al. 2012 ; Lani et al. 2017 ; Zhuang Ho 2020 ). It has also been argued that the relationship between SFR and AGN luminosity is not intrinsic but instead arises indirectly from the the mutual correlation of both quantities to the host galaxy stellar mass (e.g., Xu et al. 2015 ; Yang et al. 2017 ; Suh et al. 2019 ; Stemo et al. 2020 ), stellar mass surface density (Ni et al. 2020 ), or BH mass (e.g., Stanley et al. 2017 ). Using a hierarchical Bayesian model, Grimmett et al. ( 2020 ) recently demonstrated that AGNs of higher X-ray luminosity more tightly correlate with higher SFRs than lower luminosity AGNs. In any event, when present, the association between star formation and BH accretion seems to occur preferentially on nuclear scales, both in moderately luminous AGNs (Davies et al. 2007 ; Watabe et al. 2008 ; Diamond-Stanic Rieke 2012 ; Esquej et al. 2014 ; Zhuang Ho 2020 ) and in more powerful quasars (Imanishi et al. 2011 ; Canalizo Stockton 2013 ; Bessiere et al. 2014 , 2017 ; Lutz et al. 2016; Kim Ho 2019 ; Zhao et al. 2019 ). This is not unexpected, as cold gas on small scales naturally fuels nuclear star formation and feeds the BH (Hopkins Quataert 2010 ; Volonteri et al. 2015 ; Gan et al. 2019 ). Observations of nearby galactic nuclei offer useful clues. For example, the molecular gas in the central regions of many galaxies (e.g., Scoville et al. 1994 ; Smith Harvey 1996 ; Rubin et al. 1997 ; García-Burillo et al. 2005 , 2019 ; Hicks et al. 2009 ; Izumi et al. 2018 ; Salak et al. 2018 ; Treister et al. 2018 ; Boizelle et al. 2019 ), including the Milky Way (e.g., Ho et al. 1991 ; Hsieh et al. 2017 ), resides in the form of a circumnuclear disk. Theoretical works suggest that the star formation in the circumnuclear disk may control the gas fueling BH accretion (Shlosman et al. 1989 ; Goodman 2003 ; Thompson et al.
Paper: 2018ApJ...856..115P (fulltext)
including SNe II). We have found a good agreement between the L X – M ⋆ relation of the models and that of observed ETGs, over the whole M ⋆ range. In particular, the largely different observed L X values at the same M ⋆ are reproduced by the sensitivity of the gas flow to many properties characterizing ETGs, as galaxy shape, presence of rotation, presence of star formation, and AGN feedback. The models also show an overall agreement with the observed L X – relation, though of the models is slightly larger than that derived by F17 , for ETGs of the same M ⋆ . The agreement between models and observations holds with and without radiative and mechanical (from AGN winds) AGN feedback. These results imply that the mass input from the stellar population is able to account for a large part of the observed L X , and that—under the assumptions of this work—AGN feedback does not have a significant role in determining the present epoch L X . This is an important conclusion about the fate of recycled stellar material in the presence of AGN feedback, since an outcome of the simulations could have been, for example, a major degassing of the galaxies. Such a different outcome would have implied the necessity to assume other sources of mass (as gas accretion from outside). F17 noticed a good agreement between their results and the predictions of cosmological SPH simulations, where the hot gas mostly originates from a cosmological reservoir from which gas falls into the dark matter halos, is shock-heated, and is then further significantly heated by mechanical feedback due to AGN winds (Choi et al. 2015 ). If this mechanical feedback is not included, the simulations predict an L X far larger than that observed; thus, it is concluded that the AGN plays an important role in determining L X (in addition to M t ). This conclusion is not in contradiction with the results of the present work, which starts after z ≃ 2, and indicates that the AGN has not shaped dramatically the value of L X observed today. In fact the cosmological simulations of Choi et al. ( 2015 ) as of many others (see, e.g., Section 1 ), start from the galaxy formation epoch, and obtain at early times AGN-driven powerful outflows that remove nearly all the gas, and stop star formation. In this sense, the role of the AGN is certainly very important. Our simulations ( N14 , N15 , C17 ) start from these “initial conditions” created at the epoch of quenching, and find that at later epochs the energy injected by the AGN is not crucial in determining L X of the recycled material (while fundamental to stop the recurrent cooling flow and keep the MBH mass low). The conclusions of the two approaches, one including and the other excluding the epoch of galaxy formation and the presence of cosmological gas, then agree, provided that AGN feedback in the first approach is stronger than that found here. In fact, if AGN-driven outflows are to clear the gas out of massive ETGs at early epochs and/or prevent a
Paper: 2016A&A...589A..83G (fulltext)
for stars hotter than 5500 K ( Ryabchikova et al. 2015 ). The Balmer lines in B to F stars are in general well matched by LTE computations, and they are practically identical to the non-LTE predictions ( Przybilla Butler 2004 ). The region around H was chosen because it harbours several iron lines with accurate atomic parameters. The Balmer lines provide an excellent diagnostic of for stars cooler than 8000 K ( Gray 1992 ; Heiter et al. 2002 ). For stars hotter than 8000 K, Balmer line profiles, in particular their wings, are sensitive to both temperature and gravity ( Gray 1992 ; Smalley 2005 ). Metal line diagnostics are also good indicators of , , and [Fe/H] as the best-fit synthetic spectrum corresponds to the spectrum that yields the same abundance for different ionization stages of the same element (ionization balance) and in the same abundance for all excitation potentials of the same element in a given ionization stage (excitation potential). On the other hand, lines such as the Mg ii triplet around 4481 Å and the Fe ii lines between 4500 and 4530 Å are excellent indicators of (see for example Royer et al. 2014 ). For all the above mentioned reasons, we applied the global spectral fitting technique to the whole spectral range from 4400 and 5000 Å. The derived parameters are obviously model dependent, but the models that we are using (Sect. 2.2 ) are well suited to describe the atmospheres and spectra of “normal” A and Am stars. The selection of the observed spectra and construction of the learning databases are detailed in Sect. 2 . The PCA and the derivation of the eigenvectors and coefficients are recalled in Sect. 3 . Radial velocity correction and re-normalization of the spectra are discussed in Sect. 4 . The results of the inversion are presented in Sect. 5 . A few outliers have been analysed in detail in Sect. 6 . Discussion and conclusion are gathered in Sect. 7 . 2. Observations and learning database 2.1. Target selection The PolarBase contains reduced stellar spectra collected with the NARVAL and ESPaDOnS high-resolution spectropolarimeters. These two spectropolarimeters are mounted on the 2 m aperture Télescope Bernard Lyot (TBL) in France and on the 3.6 m aperture CFHT telescope in Hawaii, respectively. Both instruments have a spectral resolving power of 65 000 in polarimetric mode and 76 000 when used for classical spectroscopy. As a first step, we have selected all high-resolution Echelle spectra of stars whose spectral type ranges from F0 up to B9 from the database at two different resolutions and in spectroscopy mode only. Fifty spectra were thus retrieved. We have also retrieved A-type stars spectra from the ELODIE and SOPHIE archives. These archives are on-line databases of high-resolution Echelle spectra and radial velocities. The ELODIE is a fiber-fed, cross-dispersed Echelle spectrograph that was attached to the 1.93-m telescope at Observatoire de Haute-Provence (OHP; Baranne et al. 1996 ). The ELODIE recorded
Paper: 2016A&A...587A..15L (acknowledgments)
We thank the IRAM PdBI staff for excellent support. This research was supported by J.E.L.’s appointment to the NASA Postdoctoral Program at the NASA Goddard Space Flight Center, administered by Oak Ridge Associated Universities through a contract with NASA. S.A. thanks the Swedish National Science Council for grant support. We also thank the referee Chiara Feruglio for comments and suggestions which significantly contributed to improving the quality of the manuscript.
Paper: 2016A&A...593A.106R (acknowledgments)
The authors dedicate this work to Stanislav (Stan) Štefl (1955–2014) in whose honor the Štefl frequencies are named. A. C. C. acknowledges support from CNPq (grant 307594/2015-7) and FAPESP (grant 2015/17967-7). The authors are very grateful to an anonymous referee for comments serving to clarify the results. This paper includes data collected by the Kepler mission. Funding for the Kepler mission is provided by the NASA Science Mission directorate. Some of the data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST). STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. Support for MAST for non-HST data is provided by the NASA Office of Space Science via grant NNX09AF08G and by other grants and contracts. This research used the facilities of the Canadian Astronomy Data Centre operated by the National Research Council of Canada with the support of the Canadian Space Agency. Partly based on observations obtained at the Canada-France-Hawaii Telescope (CFHT) which is operated by the National Research Council of Canada, the Institut National des Sciences de l’Univers of the Centre National de la Recherche Scientique of France, and the University of Hawaii. This research has made use of NASA’s Astrophysics Data System Service, as well as of the SIMBAD database, operated at CDS, Strasbourg, France.