adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016A&A...595A..58C (acknowledgments)
We are very grateful to Mirko Boezio and Marius Potgieter for providing the Voyager data for protons, He nuclei, and electrons and to Gabriela Castelletti and Gloria Dubner for providing us with the radio data points from the region coincident with the AGILE ray detection. This work was partially funded through Grant PRIN-INAF 2012.
Paper: 2020ApJ...890L...5Y (fulltext)
X-ray fluxes. A flare is defined using the following algorithm (Ryan et al. 2016 ; NOAA GOES X-ray flux 1 1 https://www.swpc.noaa.gov/products/goes-x-ray-flux ): (1) the begin time of an X-ray event is defined as the first minute, in a sequence of 4 minutes, of steep monotonic increase in 0.1–0.8 nm flux, (2) the flux at the end of the fourth minute is at least 40% greater than the flux in the first minute, (3) the X-ray event peak time is taken as the minute of the peak X-ray flux, and (4) the end time is the time when the flux level decays to a point halfway between the maximum flux and the background level. We apply the algorithm to GOES 10 one-minute X-ray flux data, from 1998 August to 2006 May, to recognize major solar flares. In order to select reliable solar flare events, we remove multiple-peak events and pre-flare phase events by simple automatic process and visual inspection. As a result, we select 845 major solar flare events. Soft X-ray fluxes from 26 minutes before flare start time to 3 minutes after it are used for input and those of following 30 minutes are used for target. Our data set is constructed using the sliding window method. The window slides from 3 minutes after flare start time to its peak time. We randomly split the data set into training and test sets since flare X-ray profiles can be assumed to be mostly independent one another in that flares have a Poisson distribution in time (Rosner Vaiana 1978 ; Moon et al. 2001 ; ?, ?). The data set is separated by each flare event to avoid that the data from the same flare event are used for training and test both. It is noted that when we divide the data set chronologically, the number of major flares in the test data set is too small. In order to reduce the effect of test data selection and random initial weights, we consider 10-fold cross-validation. As a result, 6227–6381 data (90% of events; 69 X-class and 691 M-class) are used for training and 654–808 data (10% of events; 8 X-class and 77 M-class) for test. The difference in data number is due to different lengths of flare events in each cross-validation set. 3. Model 3.1. Methods LSTM is one of the advanced recurrent neural networks (Hopfield 1982 ) that is good to learn time dependence in sequence. LSTM have been widely employed in various fields such as machine translation (Sutskever et al. 2014 ; Wu et al. 2016 ), speech recognition (Graves Schmidhuber 2005 ), and time-series prediction (Duan et al. 2018 ; Tan et al. 2018 ). LSTM layer has a cell state candidate and three gates, to transfer hidden state from prior time step to next time step. The time step means a sequential order of elements in time-series data. Details of the LSTM calculation are described below. where f t represents the forget gate, i t represents the input gate, represents the cell state candidate, c t represents the cell state, o t represents the output gate, and h t represents the hidden state at time step t ; X is input data; W terms
Paper: 2018MNRAS.476..528E (fulltext)
the fit is repeated; the uncertainty on each RV is taken to be the standard deviation of the best-fitting velocity at each epoch when this procedure is repeated many times. Fitting a Keplerian orbit amounts to simultaneously maximizing the likelihood of the RV curves of the primary and secondary, where the model RV for a given set of orbital parameters is obtained by solving the two-body problem (Murray Correia 2010 ). We use a custom python implementation of the adaptive simulated annealing algorithm (Iglesias-Marzoa, López-Morales Jesús Arévalo Morales 2015 ) to obtain an initial maximum-likelihood estimate of the best-fitting orbital parameters and then sample the parameter space in the vicinity of the maximum likelihood with emcee (Foreman-Mackey et al. 2013 ) to estimate parameter uncertainties. We use non-informative, flat priors throughout. In addition to the seven standard Keplerian orbital parameters, 10 we fit a ‘jitter’ term, s 2 , to allow for the possibility of intrinsic scatter in the RVs due to e.g. stellar pulsation or underestimated RV uncertainties (see e.g. Baluev 2009 ; Price-Whelan et al. 2017 ). The effective total uncertainties in the RVs used in fitting are then , where σ i are the RV uncertainties at each epoch found from bootstrapping. Explicitly, the log-likelihood function isHere, the sum is over N epochs at times t i , represents the predicted RV at time t for a system with orbital parameters (Murray Correia 2010 ), and and are the orbital parameters for each component. For most systems, the best-fitting jitter is small ( s 2 ∼ 0.1 km 2 s − 2 ), indicating that our estimates of σ i are reasonably accurate. However, for stars with large v macro , which indicates significant rotation, jitter is sometimes of order 1 km s − 1 . This suggests that our velocity measurements are less accurate for rapidly rotating stars, which is also supported by our experiments with semi-empirical binary spectra (Appendix B1 ). For some systems with mass ratios near 1, we found that it was initially impossible to obtain a good fit to the measured RVs because the velocity assignments of the two stars were switched in the fits for one or more visits. We attempted to fit these systems by allowing the fitting algorithm to switch the assigned velocities for individual visits if doing so would improve the fit. In most cases, this solved the problem. A few systems (∼5 per cent of those with sufficient coverage) remained with RV curves that could not be well fitted by a Keplerian orbit, even with the possibility of switching the assigned velocities; these systems may have poorly measured RVs or contain an unseen component. Fig. 10 (a) shows an example orbital solution for a system with typical phase coverage, RV errors, and number of epochs. This system has the lowest mass ratio, q = 0.44, of the systems for which we derive orbital solutions. Because of the system’s low mass ratio, the secondary contributes only a small fraction (∼3 per cent)
Paper: 2018MNRAS.475.1238K (acknowledgments)
ACKNOWLEDGEMENTS This research has made use of data and/or software provided by the High Energy Astrophysics Science Archive Research Center (HEASARC), which is a service of the Astrophysics Science Division at NASA/GSFC and the High Energy Astrophysics Division of the Smithsonian Astrophysical Observatory. We acknowledge the use of public data from the Swift and Fermi data archives. In this work, we analysed observations performed with INTEGRAL , an ESA project with instruments and science data centre funded by ESA member states (especially the PI countries: Denmark, France, Germany, Italy, Switzerland, Spain) and with the participation of Russia and the USA. We also acknowledge the use of NASA’s Astrophysics Data System (ADS) and of the SIMBAD data base, operated at CDS, Strasbourg, France. We thank Dr. David Smith for useful discussions on Fermi LAT pulsar timing issues, and Dr. Matthew Kerr for providing radio pulsar ephemerides for PSR B1509−58.
Paper: 2019ApJ...879...50L (acknowledgments)
We thank Bill Paxton for making the MESA code publicly available, as well as Aaron Dotter for the valuable assistance in the construction of the stellar isochrones with the MIST package. We also thank the anonymous referee for the useful comments that have contributed to the discussion of our results. J.L. acknowledges financial support by Fundação para a Ciência e Tecnologia—FCT grant No. PD/BD/128235/2016 in the framework of the Doctoral Programme IDPASC—Portugal.
Paper: 2018MNRAS.478.1272H (fulltext)
a new era of research on eclipsing binary systems (EBs). The mission was designed with the main objective of searching for transiting planets (Borucki et al. 2011 ). As a by-product, the Kepler satellite provides the detection and monitoring of variable stars, most of which are EBs. This is an interesting fact, because EBs are important astronomical targets for determining the orbital and physical parameters of component stars from the geometry of their light curves (LCs) (Deb Singh 2011 ). The Kepler space mission Kepler was originally designed to investigate a region located between Swan and Lyre constellations, having a telescope with an aperture of 0.95 m and a field of view of 115.6° squares. Equipped with a photometer containing 42 CCDs with 2200 × 1024 pixels, where each pixel covers a region of 4 s of arc, Kepler provides unprecedented quality and quantity photometric data (e.g. Thompson et al. 2016 ). Details on the instrumentation of the mission can be found in Koch et al. ( 2010 ). The Kepler Eclipsing Binary Catalog 1 (KEBC; Prša et al. 2011 ; Slawson et al. 2011 ; Kirk et al. 2016 ) is an excellent bridge between the eclipsing binary data of the mission and the scientific community. In the catalogue versions, the parameters of the systems were determined by the method of Eclipsing Binaries via ArtificialIntelligence (EBAI; Prša et al. 2008 ), a computational tool for data mining of large volumes of data. Trained with synthetic LCs generated by the PHOEBE (Prša Zwitter 2005 ), the EBAI uses polynomial adjustment and does not aim at the precise and individual adjustment of LCs. On the other hand, the JKTEBOP code (Southworth, Maxted Smalley 2004a ; Southworth et al. 2004b ), based on the EBOP code ( Eclipsing Binary Orbit Program ; Etzel 1981 ; Popper Etzel 1981 ), is an efficient alternative for individual and precise analysis of LCs, obtaining more exact adjustments by allowing the parameters of interest to be free. The larger Kepler data set is a challenge for analysis tools. Consequently, a test of the fidelity of data mining tools for different processes is crucial, since the large observational programme such as Convection, Rotation and planetary Transits (CoRoT; Baglin et al. 2006 ) and All Sky Automated Survey (ASAS; Pojmanski 1997 ; Pojmański 2001 ), and in the future, programs such as Transiting Exoplanet Survey Satellite (TESS; Ricker et al. 2014 ) and PLAnetary Transits and Oscillations of stars (PLATO; Catala et al. 2010 ) will discover a lot of EBs and surely have data studied by powerful instruments such as neural networks. Furthermore, missions such as Gaia (Perryman et al. 2001 ) and Large Synoptic Survey Telescope (LSST; Tyson 2002 ) will discover millions of EBs but obtain LCs with few points (Southworth 2012 ), requiring more accurate and appropriate analysis tools. In this paper, we present the orbital and physical parameters of 78 EBs and investigate possible discrepancies between the results found by EBAI and
Paper: 2018MNRAS.480.3584D (fulltext)
compared with that of J1846−0258. The differences between the behaviour of J1119−6127 and J1846−0258 suggest that the peculiar spin frequency evolution of J1119−6127 reveal processes not well understood during the transition from a high- B radio pulsar to a magnetar. The net spin-down we observed following the X-ray bursts implies a release of a large amount of rotational energy. Assuming the canonical neutron star moment of inertia of I 45 = 10 45 g cm 2 , the rotational energy released can be estimated as This is ∼20 times of the total X-ray outburst energy, and in fact we did not observe obvious correlation between the X-ray light curve presented in Coti Zelati et al. ( 2018 ) and the radio flux densities nor the spin-frequency evolution. For a time span of ∼100 days, the spin-down power gives us an averaged luminosity of ∼4.4 × 10 36 erg s −1 , about two times larger than the normal spin-down luminosity. While only a small portion of the spin-down energy goes into radio and X-ray, we could expect much stronger high energy emissions from the pulsar (e.g. γ-ray) if energy loss via high energy radiation is the main energy-loss mechanism. Such a high spin-down luminosity could also power strong relativistic particle outflows that can brighten the pulsar wind nebula (Blumer et al. 2017 ). A complete picture of the PSR J1119−6127 event is likely to be that ultra-strong magnetic fields powered the X-ray bursts and triggered an additional torque that produced the peculiar spin frequency evolution. The fact that the polarization PA changed significantly following the X-ray bursts indicates that the magnetic field might have changed. If we assume that the spin-down process is dominated by dipole braking, the surface magnetic field strength can be estimated as where P and are the spin period and spin-period derivative. Applying equation ( 2 ) on our measurements of P and of PSR J1119−6127, we found that after the X-ray burst the surface magnetic field strength can reach as high as ∼9 × 10 13 G, which is more than two times stronger than the normal field strength. Under such assumptions, the fast increase and slow recovery of as shown in the third panel of Fig. 1 indicate similar evolution of the surface magnetic field strength of the pulsar. This suggests that the X-ray bursts triggered some reconfiguration of the magnetosphere of the pulsar, resulting in the increase of the apparent field strength. The energy of the global dipolar field external to the neutron star can be estimated as and in order to increase the magnetic field strength from ∼4 × 10 13 G to ∼9 × 10 13 G a total amount of energy of ∼2 × 10 45 erg is needed. This is more than one order of magnitude larger than the spin-down power released following the X-ray bursts. Energies either from multipole (and/or internal) magnetic fields or neutron star interior need to be converted to the strong dipolar field and/or distort the magnetic field. Under such a scenario, the slow recoveries of
Paper: 2020A&A...641A.117D (fulltext)
about 200 Myr after the MS turnoff). The second phase coincides with the base of the RGB, where τ DR decreases from about 3 Gyr to about 100 Myr for both stars. These rapid changes in τ DR can be explained by departures from thermal equilibrium, which are measured by ε grav = − T ∂ s /∂ t . The bottom panel of Fig. 8 shows the variations in ε grav in the stellar core of both stars, which are clearly anti-correlated with the variations in τ DR . The forcing of differential rotation is maximal at the peaks of ε grav , that is when the core contracts on a thermal timescale. Fig. 8. Variations in the timescale τ DR over which differential rotation is forced ( top panel ) and in ε grav ( bottom panel ) along the subgiant branch for our best-fit models of KIC 8524425 (red dashed line) and KIC 5955122 (solid blue line). The filled circles correspond to the stars at current age. The stars studied by D14 all lie after the peak of ε grav corresponding to the base of the RGB except for the least evolved one (star B), which lies precisely at the maximum of the peak (see Fig. 6 of Eggenberger et al. 2019b ). As shown by Fig. 8 , the evolutionary stage of KIC 8524425 and KIC 5955122 places them well before the maximum of ε grav , which confirms that they are indeed less evolved than the sample of D14. The nearly solid-body rotation profiles that we obtained show that the internal redistribution of AM for these stars takes place on timescales lower than the current value of τ DR (about 320 Myr for KIC 5955122 and 570 Myr for KIC 8524425). 5.2. Efficiency of AM transport To estimate the efficiency of the AM transport that is required to reproduce the observed rotation profiles, we computed rotating models for the two subgiants, including an additional constant viscosity ν add in the equation for the transport of AM in radiative zones. This approach was already followed for instance by Eggenberger et al. (2012 , 2019b) , Spada et al. (2016) , and den Hartogh et al. (2019) , for more evolved subgiants and red giants. As already mentioned in these works, this is a very crude description of the AM transport in subgiants. Naturally, we do not expect whatever mechanism is at work to indeed operate like a diffusive process with constant viscosity. The main goal of this approach is to study how the mean efficiency of the unknown transport processes vary with stellar parameters, and in particular with evolution. It was shown by Eggenberger et al. (2019b) that the transport efficiency decreases during the late subgiant phase, near the base of the RGB, but must increase again as stars evolve along the RGB ( Eggenberger et al. 2017 ). We here had the opportunity to estimate the efficiency of the transport closer to the MS turnoff and compare with its value on the MS. 5.2.1. Rotating models We used two different stellar evolution codes: YREC ( Demarque et al. 2008 ) and GENEC ( Eggenberger et al. 2008 ). The two codes differ in their treatment of the rotation. GENEC
Paper: 2015MNRAS.450.1375H (acknowledgments)
We thank Linda Tacconi, Reinhard Genzel, Jing Wang, Richard D'Souza, Sambit Roychowdhury for helpful discussions. GALEX is a NASA Small Explorer, launched in 2003 April, developed in cooperation with the Centre National d'Etudes Spatiales of France and the Korean Ministry of Science and Technology. This research has made use of the NASA/IPAC Infrared Science Archive, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. Funding for the SDSS and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the US Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, the Max Planck Society, and the Higher Education Funding Council for England. The SDSS is managed by the Astrophysical Research Consortium for the Participating Institutions. The Participating Institutions are the American Museum of Natural History, Astrophysical Institute Potsdam, University of Basel, Cambridge University, Case Western Reserve University, University of Chicago, Drexel University, Fermi National Accelerator Laboratory, the Institute for Advanced Study, the Japan Participation Group, Johns Hopkins University, the Joint Institute for Nuclear Astrophysics, the Kavli Institute for Particle Astrophysics and Cosmology, the Korean Scientist Group, The Chinese Academy of Sciences (LAMOST), the Leibniz Institute for Astrophysics, Los Alamos National Laboratory, the Max-Planck-Institute for Astronomy (MPIA), the Max-Planck-Institute for Astrophysics (MPA), New Mexico State University, Ohio State University, University of Pittsburgh, University of Portsmouth, Princeton University, the US Naval Observatory and the University of Washington.
Paper: 2020ApJ...888...65G (acknowledgments)
This work was supported by the National Science Foundation of China (11721303) and the National Key RD Program of China (2016YFA0400702). We thank Peter Erwin for the constructive discussion. M.D. is supported by the grants “National Postdoctoral Program for Innovative Talents” (No. 8201400810) and “Postdoctoral Science Foundation of China” (No. 8201400927) from the China Postdoctoral Science Foundation. V.P.D. was supported by STFC Consolidated grant ST/R000786/1. V.P.D. acknowledges support from the Kavli Visiting Scholars Program for a visit to the KIAA during this work. We utilized the High-performance Computing Platform of Peking University.
Paper: 2020MNRAS.496.2391J (fulltext)
in the parlance of the General Catalogue of Variable Stars. 1 The first-order combination terms between the modes n and n ′ have the following frequencies: and Given negligible first-order corrections ε T f (1, 0) , equations ( 15 ) are consistent with equations ( 4 ) and ( 7 ), while equations ( 14 ) with equations ( 8 ). The referee has suggested a test that our f n ,0 and modes are indeed associated with the l = 1, m = 0 spherical harmonics and provided examples of applying the test to simulated data. The test consists in dividing the data into two parts according to the orbital phase in such a way that one part contains the data covering orbital phases from one quadrature to the other, and the second part, the remaining data, and then computing amplitude spectra for the two parts separately. Using simulated l = 1, m = 0 light curves with an assumed pulsation frequency, Reed, Brondell Kawaler ( 2005 ) found for a range of inclination of the pulsation axis to the line of sight that in the amplitude spectra of the two parts of the data there appears a peak at the assumed frequency flanked by f orb aliases, whereas in the amplitude spectrum of the complete data set the peak at the assumed frequency is missing (see their fig. 4). In addition, there is a phase difference equal to between the light curves in the two parts of the data. For the test, we used the red BRITE magnitudes because their spectral window is cleaner than that of the blue magnitudes (see the insets in the lower panels of Fig. 1 ). We removed the orbital light-variation by pre-whitening with f orb , 2 f orb , 3 f orb , 4 f orb , and 6 f orb , divided the data into two parts as described above, and computed amplitude spectra. The results are displayed in Fig. 6 . The top left-hand panel shows the amplitude spectrum of the complete data (referred to as set 1 in the caption to the figure) with the peaks at the frequencies appearing in equations ( 2 )– ( 5 ) labelled. The upper middle left-hand panel shows the amplitude spectrum of the magnitudes covering the orbital phases from the western to eastern quadrature, i.e. the phases from 0.3825 to 0.8825 in Figs 3 and 5 (set 2). The peak at f n ,0 = ( f 2 + f 4 )/2 = 0.4857 d −1 and its f orb aliases dominate the spectrum. The aliases occur at the same frequencies as the f 2 and f 4 peaks in the top left-hand panel but should not be confused with them. While the aliases reproduce the side-lobes of the spectral window seen in the top right-hand panel, the frequencies f 2 and f 4 arise as the result of a transformation of the corotating frame of reference whose polar axis coincides with the line joining the components’ mass centres to the non-rotating frame with the polar axis parallel to the pulsating component’s rotation axis (see the second paragraph of this section). The lower middle left-hand panel contains the amplitude spectrum obtained from the set 2 data pre-whitened with f n ,0 . Now, the highest peak appears at
Paper: 2015ApJ...805..129K (acknowledgments)
This paper is dedicated to the memory of our co-author James Caswell who passed away January 14th 2015. James was a leading figure in maser astronomy for more than three decades and his work in this field represents a peerless legacy to future researchers. Funding assistance was provided in part by Sigma Xi Grants-in-Aid of research, the Deutscher Akademischer Austauschdienst (DAAD), and National Natural Science Foundation of China (Grant No. 11133008). The authors would like to thank the referee for their detailed analysis and comments in reviewing this paper.