adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016ApJ...825..115F (fulltext)
al. 2004 ; Rahmani et al. 2014 ; Albareti et al. 2015 ) out to redshifts z ∼ 1, where δλ denotes the separation between two spectral lines, and the subscript refers to the redshift. Our results are not in contradiction with this upper limit. The studies of the variation of the fine structure constant use multiplets of a given atomic element. The targeted spectral features, such as the [O iii ] doublet ( λλ 4960, 5008 Å), cover a relative narrow spectral window (Δ λ ≲ 100 Å). In comparison, our study covers a wavelength window at least 30 times larger. In this context, we wonder whether analyses of the variation of the fine structure constant based on absorption lines (e.g., Quast et al. 2004 ) may suffer from similar type of systematic effects. In addition, the use of nebular emission lines might present equivalent systematic complications in the determination of the centroid of the line, this time caused by the gas kinematics, but such musings are beyond the scope of this paper. Is the result shown in this paper an absolute limit to the accuracy one can achieve with galaxy spectra? Although the present data cannot provide an accuracy better than one part in 100,000 out to z ≲ 0.25, we speculate that high S/N spectra at significantly higher resolution— R ≳ 10,000—can be used to probe in detail the effective shapes of the absorption lines, potentially allowing us to break this barrier. The denser sampling at higher resolution will decrease the statistical weight in the methodology due to differences in the number of fitting points between the blue and the red sides of the spectral features, caused by the stretching of the spectra. APPENDIX POTENTIAL SYSTEMATICS A.1. Wavelength Calibration A.1.1. Testing the Wavelength Calibration of SDSS Spectra In addition to the signal found in the simulated data (Section 4 ) one could also consider the possibility of an inaccurate wavelength calibration of the SDSS spectra. The official SDSS pipeline quotes typical errors (Abazajian et al. 2009 ) of order 2 km s −1 , which translates into an uncertainty of δ Δ( z ; λ 0 ) ∼ 7 × 10 −6 for individual spectra. Although this effect should be significantly lower than our measurements, especially if we consider that the whole analysis rests on the determination of approximately 6.8 million individual spectral features, we nevertheless explore below a possible systematic from a wavelength calibration trend. If the observed signal is due to a calibration wavelength problem, then we should not expect the calibration bias to depend on the redshift bin considered. Consequently, we conduct the following test: for each redshift bin we select only those lines within the common observer-frame window, between 4500 and 6800 Å. We calculate in this region how much the observed wavelengths ( λ obs ) depart from the standard (1 + z ) law for each redshift bin. In this test, we hypothesize that such departures are caused by an inaccurate wavelength calibration, so that the
Paper: 2015A&A...575A...9P (acknowledgments)
We are grateful to the MPIfR team, as well as the APEX and IRAM 30 m staff for their help and support during and after the observations. We are grateful to C. Brogan for providing the 21 cm map and the data cube of the velocity-resolved optical depth of HI estimated for M17 SW. We thank the referee for the careful reading of the manuscript and constructive comments that helped to improve our work. We are also grateful with the editor, Dr. Malcolm Walmsley, for his timely comments and suggestions that helped to improve even more our work. Molecular Databases that have been helpful include the NASA/JPL spectroscopy line catalog and the University of Leiden’s LAMDA databases.
Paper: 2017ApJ...836...84D (acknowledgments)
The work of TW was supported by the NASA Cooperative Agreement NNG11PL10A to the Catholic University of America. chianti is a collaborative project involving the US Naval Research Laboratory, the Universities of Florence (Italy) and Cambridge (UK), and George Mason University (USA). IDL wavelet software was provided by C. Torrence and G. Compo, and is available at http://paos.colorado.edu/research/wavelets/ . The SOHO LASCO CME catalog is generated and maintained at the CDAW Data Center by NASA and The Catholic University of America in cooperation with the Naval Research Laboratory. SOHO is a project of international cooperation between ESA and NASA. Facilities: RHESSI , GOES .
Paper: 2016A&A...593A..16T (fulltext)
luminosity and thus accretion rate. For this purpose we used the results of our spectral analysis of the emission of the sources in a wide energy range, obtained during previous outbursts with the RXTE and INTEGRAL observatories ( Tsygankov et al. 2006 , 2007 ). It was found that the source flux in the 0.510 keV energy was 50% from the bolometric (i.e. flux in the 0.5100 keV energy range). Based on these results, in the subsequent calculations we used the coefficient 2 for the conversion of fluxes for both sources in the bright (accretion) state. In the propeller state, Swift /XRT energy coverage is sufficient to detect most of the emergent flux and no additional bolometric correction is needed. Indeed, this conclusion is valid both for the black body and power-law models due to the low temperatures ( keV) or steep photon indexes (). The bolometric correction may introduce additional uncertainty because of our ignorance of the broadband source spectrum at luminosities around the transition. We estimated this uncertainty at the level of 1020% and added it to the final uncertainty for the limiting luminosity. Taking all uncertainties into account, we estimate the bolometric limiting luminosities for the onset of the propeller regime at erg s and erg s for 4U 0115+63 and V 0332+53, respectively. Fig. 4 Observed correlation between a combination of the propeller limiting luminosity and the period, , and independently determined magnetic field strength for five pulsars and one accreting magnetar (M82 X-2) is shown with circles with error bars ( Tsygankov et al. 2016 ). The dashed and dash-dotted lines represent the theoretical dependence from Eq. ( 2 ), assuming NS standard parameters (, km) and and , respectively. The red star and open box indicate the positions of 4U 0115+63 and V 0332+53 obtained in this work, respectively. 4.2. “Propeller” effect The “propeller effect” ( Illarionov Sunyaev 1975 ) is caused by a substantial centrifugal barrier produced by the rotating magnetosphere of the neutron star. The matter stopped at the magnetospheric radius can only penetrate the magnetosphere if the velocity of the field lines is lower than the local Keplerian velocity. In other words, the matter will be accreted only in the case when the magnetospheric radius () is smaller than the corotation radius (). The magnetospheric radius depends on the mass accretion rate, and, therefore, one can estimate the limiting luminosity corresponding to the onset of the propeller regime by equating the corotation and magnetospheric radii (see e.g. Campana et al. 2002 ) where is the neutron star mass in units of 1.4 , is neutron star radius in units of cm, its rotational period in seconds, is the magnetic field strength in units of G, and is the mass accretion rate onto the neutron star. Factor relates the magnetospheric radius for disc accretion to the Alfvén radius calculated for spherical accretion and is usually assumed to be ( Ghosh Lamb 1978 ). Because the
Paper: 2016MNRAS.463.3686B (acknowledgments)
The authors are supported by an ERC Starting Grant (grant no. 280127 ). MLB is an STFC Advanced/Halliday fellow. We wish to thank Tomasz Kacprzak for making the IM3SHAPE simulations available to us and Wendy Williams and Huub Röttgering for useful discussions on the ionosphere. We also thank Robert Braun for help with the SKA sensitivity curves.
Paper: 2020MNRAS.494.2178M (fulltext)
fluctuation in GX 301–2. This implies that the observed fluctuations are highly significant. The source flux was estimated based on Neil GehrelsSwift Burst Alert Telescope (BAT) data (Krimm et al. 2013 ) 2 and the X-ray light curve was folded in the way similar to the frequency derivative to trace the corresponding flux. The flux was transformed to X-ray luminosity using three simultaneous observations by the Nuclear Spectroscopic TelescopeArray ( NuSTAR ). In the full energy band, 3–79 keV, NuSTAR observed absorbed luminosities of (0.24 ± 0.07) × 10 37 , (0.4 ± 0.1) × 10 37 , and (1.2 ± 0.3) × 10 37 erg s −1 on MJD 56959, 57299, and 58545 (Nabizadeh et al. 2019 ). The luminosities corrected for absorption in the interstellar medium and in the system are consistently about 20 per cent higher. The corresponding count rates measured by Swift / BAT on the same dates are 0.017 ± 0.003, 0.020 ± 0.003, and 0.06 ± 0.03 counts cm −2 s −1 , which imply an average conversion factor of (18 ± 1) × 10 37 erg s −1 (counts cm −2 s −1 ) -1 . With the computed luminosity [Fig. 1(b) ], we now obtain variations of the specific accreted angular momentum [Fig. 1(c) ] with the orbital phase using the equation where I = 10 45 g cm 2 is the moment of inertia of the NS and is the mass accretion rate, which is connected to luminosity L through where ε = 0.15 is the assumed efficiency. It is evident that the spin evolution during the preperiaston flare is complex, showing both spin-up and spin-down episodes. If the mass accretion rate was simply correlated with the transported angular momentum, the NS would be expected to strongly spin-up throughout the flare. However, the maximum spin-up is already observed before the observed peak of the flux, when maximal accretion torque is naively expected. The observed spin-up rate, however, starts to steadily decrease several days prior to the flux maximum even though the accretion rate continues to increase. In fact, zero spin-up rate is observed at the peak of the flare, and the NS is already spinning down right after the peak. The maximal spin-down rate is then reached several days after the peak of the flare, still at a high accretion rate. 3 DISCUSSION Here, we would like to focus on the discussion of the spin-frequency changes around the periastron. As discussed in the data analysis section, even considering uncertainties in the orbital parameters determined by Koh et al. ( 1997 ), the observed evolution of the frequency derivative over the orbit has a high statistical significance and therefore requires a physical explanation. First of all, it is important to emphasize that in GX 301–2, the observed orbital flux dependence is neither consistent with accretion from a transient accretion disc, nor from a smooth homogeneous wind. However, in an eccentric binary system, the wind may be enhanced in the periastron due to tidal interaction of the NS with the primary (Stevens 1988 ). Based on this result, the flaring has been
Paper: 2017A&A...597A..78L (fulltext)
density in this domain becomes comparable to the previously introduced offset. Finally, the most interesting result revealed by these plots is that during phase 2 the velocity profiles are most often located very close together and are, moreover, in a good correspondence with the macrospicules’ leading edge trajectories (black line in Fig. 8 ) discussed in the next section. Fig. 7 Reconstructed evolution of the macrospicule’s one-dimensional velocity field as projected on the vertical axis. Upward velocities are shown in blue and downward in red. Fig. 8 Velocity profiles at varied heights from 15 to 40 Mm (coloured lines). The straight black line corresponds to the parabolic fit of the macrospicule’s leading edge trajectory. Table 1 Obtained parameters of the macrospicules. Notes. Here, deceleration is the actual value measured from the parabolic fit, while ballistic deceleration is calculated based on Eq. ( 13 ). Beyond that, for one of the macrospicules (No. 10 in Table 1 , which was chosen for illustration in Figs. 4 – 8 , and 10 ) we identified a train of three compression waves which start to propagate upwards from the macrospicule’s base in the early phase of its motion. The waves are scarcely seen in the initial density map, but are clearly identifiable in the subtracted high-frequency component (Fig. 5 c) as a set of fringes. They arise mainly from intensity variations, and we are inclined to interpret them as the magneto-acoustic waves since the intensity change is more likely to result from the density oscillations, rather than from a change in temperature or in ionisation degree. The inclination of the fringes and the distance between them yields a varying propagation velocity, which gradually decreases from km s for the first wave to km s for the last one, and a wave period of roughly s. Most probably, the leftovers of these waves in the filtered density map are responsible for the quasi-periodic disturbances in the velocity field, seen in Fig. 8 at , , and min. 3.2. Leading edge trajectories Along with the reconstruction of the velocity field, we studied trajectories of the apparent end points of the same 18 macrospicules, and for 15 of them we found these points to follow parabolic trajectories with high precision (Fig. 4 d). The obtained trajectory parameters corrected for the inclination angle are presented in Table 1 . The decelerations typically range from 160 to 230 m s and initial velocities (defined as the extrapolated velocity at zero height) from 80 to 130 km s. Two macrospicules, however (Nos. 2 and 4), have significantly higher decelerations, which are even larger than gravity on the solar surface m s, and correspondingly have significantly higher initial velocities. Also, the maximum heights reached by the macrospicules are typically from 30 to 45 Mm, and the lifetimes are in the range of 10–20 min, which is in a good agreement with earlier studies ( Withbroe et al. 1976 ; Dere et al. 1989 ; Karovska Habbal 1994 ).
Paper: 2018MNRAS.475.4043K (acknowledgments)
ACKNOWLEDGEMENTS We like to thank the referee Prof. Mathias Steinmetz for a constructive and insightful report. Software credit : astropy (Astropy Collaboration 2013 ), bmcmc (Sharma 2017a ), chainconsumer (Hinton 2016 ), daft https://github.com/dfm/daft , ipython (Pérez Granger 2007 ), matplotlib (Hunter 2007 ), numpy (van der Walt, Colbert Varoquaux 2011 ), pandas (McKinney 2012 ), and scipy (Jones et al. 2001 ).
Paper: 2018A&A...609A.114G (fulltext)
et al. ( 1994 ), 17 = period redetermined by combining Catchpole Feast ( 1981 ), and WBF, 18 = OMC (Mas-Hesse et al. 2003 ), 19 = various literature -band data. The full table is available at the CDS. Appendix B: Mass-loss rates, luminosities, and fits to the SEDs Tables B.1 and B.2 list the parameters of the models which best fit the observed data. These include the identifier, information on the atmospheric model used (effective temperature, , and for the C stars, C/O ratio), grain size and dust type, luminosity, dust optical depth in the -band, mass-loss rate, whether was fitted (1) or fixed (0), , whether was fitted or fixed, the slope of the denisity law, whether was fitted or fixed, outer radius (in units of inner radius), and the reduced . These tables are published in its entirety at the CDS; a portion is shown here for guidance regarding its form and content. The reduced chi-square value is given for reference only. There is a large range in values, and sometimes the values are far larger than unity. This is in part related to the construction of the SED and the available photometery (and error) which can be very different across sources, and the role of variability. Figures B.1 and B.2 show the best-fitting models, and give a subjective indication of the quality of the fits. The top panel shows the observed SED and IRS spectrum and the fitted model on an absolute scale, while the bottom panel shows the IRS spectrum and the model (the blue line), scaled to a quasi-continuum point based on the average flux in the 6.35–6.55 m region. Horizontal lines near the bottom indicate wavelength regions excluded from the fit. Table B.1 Fit results of the C-star sample. Notes. Column 1 lists the identifier. Column 2 lists the information on the model atmosphere that is used as (in K)/((C/O)). Column 3 indicates the grain size and type: The number after the indicates the grain size in m. Then follows the proportion of AMC (zubko): SiC (Pitm): MgS (Hofm) = 100 : x : y. Column 4 lists the luminosity. Column 5 lists the dust optical depth at 0.5 m, and Col. 6 lists the total mass-loss rate (assuming an expansion velocity of 10 km s, and a dust-to-gas ratio of 0.005). Column 7 indicates if the optical depth was fitted (, or fixed ). Column 8 lists the temperature at the inner dust radius, and Col. 9 indicates if this temperature was fitted (, or fixed ). Column 10 lists the slope of the density law, and Col. 11 indicates if this temperature was fitted (, or fixed ). Column 12 lists the outer radius in units of the inner dust radius. Column 13 lists the reduced of the fit. The full table is available at the CDS. Table B.2 Fit results of the M-star sample. Notes. Column 1 lists the identifier. Column 2 lists the information on the model atmosphere that is used as (in K)/. Column 3 indicates the grain size and type: The number after the indicates the grain size in m. Then follows the proportion of olivine (MgFeSiO), corundum, metallic iron, and
Paper: 2017A&A...607A.116E (acknowledgments)
We thank the anonymous referee for valuable feedback that helped improve the paper. The research leading to these results was funded by the Spanish Ministerio de Economía y Competitividad (MINECO) under grants AYA2012-32295, ESP2015-68964-P, AYA2016-76682-C3-2-P, and AYA2015-54346-C2-1-P. Based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere, Chile, progs. 077.B-0151A and 081.B-0042A. Based on observations made with the NASA/ESA Hubble Space Telescope, and obtained from the Hubble Legacy Archive, which is a collaboration between the Space Telescope Science Institute (STScI/NASA), the Space Telescope European Coordinating Facility (ST-ECF/ESA), and the Canadian Astronomy Data Centre (CADC/NRC/CSA). The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc.
Paper: 2021MNRAS.504.2853H (fulltext)
fragments. Our analysis finds that of the Ca-rich systems outlined by Hollands et al. ( 2018 ), only one can be attributed to being crust-rich (SDSSJ0744+4649 to 5σ significance), whilst for two other systems (SDSSJ1033+1809 and SDSSJ1351+2645) there is insufficient information available for a crustal model to be the best fit. SDSSJ1055+3725 was no longer included in the sample (see § 2.1 ). The three Fe-rich systems (SDSS1043+3516 (5.2σ), SDSSJ0741+3146 (2.8σ) and SDSSJ0823+0546 (10σ) are indeed found to be core-rich. Both the two two Mg-rich systems, SDSSJ0956+5912 and SDSSJ1158+1845 actually have fairly typical Mg/Fe ratios and due to the combination of sinking time-scales, the best-fitting models suggest that both systems are core-rich to 2σ. 4.3.3 The mass of white dwarf pollutants For those systems in the declining phase, accretion has finished and the mass in the atmosphere, combined with the sinking time-scales can be converted into the total mass of the polluting body. This calculation no longer yields a lower limit as there cannot be any material left in a circumstellar orbit which is yet to accrete. Such systems uniquely probe the mass of the polluting bodies. As shown in Fig. 11 , the bodies polluting the white dwarfs considered here are found to have masses between just half that of Vesta and the mass of the Moon. The bodies polluting white dwarfs are likely to be similar to large Solar System asteroids, as previously concluded in the literature (Farihi et al. 2010a ; Girven et al. 2012 ), although at least two systems have accreted planetary bodies approaching the mass of the Moon. Figure 11. The posterior distributions for the total mass accreted on to the thirteen polluted white dwarfs whose abundances suggest that they are in the declining phase. During the declining phase, accretion has ceased and therefore, the mass currently in the atmosphere, alongside the sinking time-scales, can be used to calculate the total mass accreted. The masses range from Vesta to the Moon. 4.3.4 Constraints on the accretion event lifetimes The accretion event lifetime for a system can be constrained from the abundances because the duration of accretion affects the probability that the system will be observed in build-up, steady state, or declining phase. The accretion event lifetimes from the 202 systems analysed here suggests that the average accretion events last yr. This result is consistent with the result derived in Girven et al. ( 2012 ) and is consistent with the theoretical predictions of Rafikov ( 2011a ) and Rafikov ( 2011b ), but is in stark contrast to the short accretion event (or disc) lifetimes predicted by Wyatt et al. ( 2014 ) to explain the difference between observed accretion rates of DA and DB white dwarfs. 5 CONCLUSIONS In this work, we present a Bayesian model to explain the abundances of planetary material observed in the atmospheres of some white dwarfs. Our method reproduces the abundance patterns observed in polluted
Paper: 2018MNRAS.474.1194Z (acknowledgments)
ACKNOWLEDGEMENTS We thank the referee, Georges Meynet, for his very detailed and valuable comments. This research was supported by the Israel Science Foundation and by the Pazy Research Foundation. NZ thanks the support of the PEF's Alfred and Anna Grey Excellence Scholarship.
Paper: 2017ApJ...836...53B (acknowledgments)
I thank the referee, Richard Durisen, for a number of perceptive and constructive comments on the manuscript and Sandy Keiser for computer systems support. The calculations were performed primarily on the flash cluster at DTM and also on the Carnegie memex cluster at Stanford University.
Paper: 2017MNRAS.464..840P (acknowledgments)
The authors wish to thank Ettore Perozzi, Michael Freyberg, Frank Haberl, Matteo Guainazzi and the XMM–Newton calibration team for checking that the observed jitter has no instrumental origin. We thank Jan-Uwe Ness, Ignacio de la Calle and the rest of the XMM–Newton scheduling team for the enormous support that made the new XMM–Newton observations possible. We also thank Barbara De Marco, Thomas Tauris, Jonathan Ferreira, Pierre-Olivier Petrucci, Henri Gilles and Rosario Iaria for very useful discussion as well as the referee for the very useful comments that significantly improved the paper. This research has made use primarily of data obtained with XMM–Newton , an ESA science mission with instruments and contributions directly funded by ESA Member States and NASA , and on data obtained from the Chandra and ASCA Data Archives. The GC XMM–Newton monitoring project is supported by the Bundesministerium für Wirtschaft und Technologie/Deutsches Zentrum für Luft- und Raumfahrt (BMWI/DLR, FKZ 50 OR 1408 and FKZ 50 OR 1604) and the Max Planck Society . KD would like to thank MPE for the hospitality during his stay, and the German Academic Exchange Service (DAAD) for the fellowship which supported his participation in the project. TMD acknowledges support by the Spanish Ministry of Economy and Competitiveness (MINECO) under the grant AYA2013-42627 . LS acknowledges partial support from ASI INAF I/004/11/1 .