adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017MNRAS.471.2537H (acknowledgments)
Acknowledgements We would like to thank Douglas C. Heggie for N -body simulations (referred in the text as DH simulations) and for very valuable discussions concerning blue straggler spatial distributions in star clusters. Results from these simulations added much confidence to the validity of the results obtained with the mocca code. The project was supported partially by Polish National Science Center grants DEC-2011/01/N/ST9/06000 and DEC-2012/07/B/ST9/04412. We thank anonymous referee for great comments and suggestions. It helped us to improve the paper.
Paper: 2018MNRAS.479.4440G (fulltext)
our current understanding on the formation of wide binaries, when comparing the abundances of their components, we would expect both stars to have similar metallicities, as both stars come from the same parental material. Empirical evidence for this expectation has been reported in the works of Andrews et al. ( 2018a ), Desidera et al. ( 2004 ), and Desidera et al. ( 2006 ), reinforcing the idea that wide binaries can be thought of as the smallest versions of open clusters (cochemical and coeval stars). Fig. 7 shows the metallicity comparison of the data sample pairs on the left (blue), and of the random alignments pairs on the right (red). The 1:1 relation, and a ±0.1 dex displacement from it, are shown as the solid and dashed lines, respectively. It can be seen that the data pairs tend to follow the 1:1 relation, while the random alignments pairs do not. We take this result as a confirmation that our multiple-parameter criteria used to select the data sample pairs is indeed recovering promising wide binary candidates, as for most of the components of the pairs their metallicities tend to be similar (even though the metallicity was not used as a criterion). This result is in agreement with the expectations. In spite of these promising results, some contamination might still be present in our nominal data sample. For instance, one pair in Fig. 7 is located well off the 1:1 relation, with [Fe/H] 1 = −0.132 dex and [Fe/H] 2 = +0.24 dex. Because of this, we decide to assign qualitative flags to our candidates depending on how consistent or inconsistent they appear to be when taking all the parameters into account. For any given pair, we assign it one of three types of flags: ‘ a ’, when it is consistent in all the parameters and most likely is a real binary; ‘ b ’, when it appears to be consistent in some regards but not in others; and ‘ c ’, when even though it passed all the required criteria of the search, it shows clear inconsistencies under a more detailed inspection, and most likely correspond to a chance alignment. Out of the 10 pairs obtained in the 4th row of Fig. 6 , we classify three of them as ‘ a ’, five of them as ‘ b ’, and two of them as ‘ c ’. Fig. 7 also shows the data sample pairs labelled according to this classification. We report the ‘ a ’ and ‘ b ’ pairs in the first block of Table 2 , where we also provide the parameters associated with the pairs and their component stars. The pairs assigned with flag=‘ c ’ are discarded from our candidate list and not further analysed. Table 2. List of our wide binary candidates in the Subsample 1 : ‘PM-π-RV’. We only report the pairs qualitatively flagged as ‘ a ’ and ‘ b ’, as the pairs flagged as ‘ c ’ have been discarded from our candidate list. The table is separated in three blocks, each one of them containing the candidates found in Section 5.1 , Section 5.2 , and Section 5.3 , respectively. In any given block of the table, the pairs are separated by horizontal lines. For each
Paper: 2017ApJ...850L..34B (fulltext)
are heated by the nuclear decays of products of the rapid neutron-capture process (Metzger et al. 2010 ). The light-curve properties were interpreted as being produced by dynamical ejecta from the merger and secular ejecta from the merger remnant. The estimated ejecta mass is in the range of 0.03 to 0.05 (Chornock et al. 2017 ; Cowperthwaite et al. 2017 ; Drout et al. 2017 ; Kasen et al. 2017 ; Kasliwal et al. 2017 ; Kilpatrick et al. 2017 ; Nicholl et al. 2017 ; Smartt et al. 2017 ; Tanaka et al. 2017 ; Tanvir et al. 2017 ), which even for asymmetric binaries lies near the high end of the theoretical range expected from simulations. This can be interpreted as tentative evidence for a delayed/no collapse in GW170817 because this merger outcome tends to produce larger ejecta masses as compared to a direct collapse (Bauswein et al. 2013b ; Fernández Metzger 2013 ; Hotokezaka et al. 2013 ; Metzger Fernández 2014 ; Perego et al. 2014 ; Siegel et al. 2014 ; Wanajo et al. 2014 ; Just et al. 2015 ; Sekiguchi et al. 2016 ). We thus use below the assumption of no prompt collapse in GW170817 and leave the detailed interpretation of the electromagnetic emission to future work. Our assumption can be corroborated by refined models and future observations. 3. NS Radius Constraints 3.1. Threshold Binary Mass If GW170817 resulted in a delayed collapse or no collapse, its total mass provides a lower limit on the threshold binary mass for prompt collapse as given by Equation ( 1 ). The threshold binary mass M thres depends sensitively on the EoS (Shibata 2005 ; Baiotti et al. 2008 ; Hotokezaka et al. 2011 ; Bauswein et al. 2013a ). Considering different EoSs, in Bauswein et al. ( 2013a ), we found by hydrodynamical simulations that the threshold binary mass to good accuracy followswith being the radius of a nonrotating NS with a mass of 1.6 and M max being the maximum mass of nonrotating NSs. The relation was derived from simulations of symmetric binary mergers but also holds for moderately asymmetric systems (Bauswein et al. 2013a ; Bauswein Stergioulas 2017 ). We verify through additional simulations for five representative EoSs that strongly asymmetric mergers with mass ratio q = 0.6 have a threshold binary mass, which is systematically lower by 0.1 to 0.3 than M thres of equal-mass binaries. This reduction of M thres for asymmetric binaries is understandable because, according to Kepler ’s law, asymmetric binaries have less angular momentum than equal-mass binaries with the same M tot at a given orbital separation, which implies less stabilization for asymmetric mergers. (With the low-spin priors, the 90% credibility interval of the mass ratio of GW170817 is ). If GW170817 was very asymmetric, one has , which implies that Equation ( 1 ) is conservative because . A similarly accurate description of M thres is given by the fitwith the radius R max of the maximum-mass configuration. Equation ( 2 ) is accurate to better than (Bauswein et al. 2013a , 2016 ). The
Paper: 2020ApJ...904L...9C (fulltext)
The LIGO Scientific and Virgo Collaborations have so far reported 15 confirmed gravitational-wave (GW) signals from the mergers of compact objects (Abbott et al. 2019a , 2020b , 2020c , 2020d , 2020e ). Depending on the nature of the objects and the environment surrounding the binary, merging compact binaries can also emit electromagnetic radiation and neutrinos. In the second observing run (O2) of Advanced LIGO and Advanced Virgo, low-latency GW candidates were distributed to partner astronomers for multi-messenger follow-up (Abbott et al. 2019b ). This partnership resulted in the combined measurement of electromagnetic and GW emission from GW170817 (Abbott et al. 2017a , 2017b ), which revolutionized our understanding of a wide range of physics related to merging neutron stars. Accurate sky localization and the rapid release of GW candidates are crucial for multi-messenger observations of astrophysical events. The former is achieved through a global network of GW observatories: the two Advanced LIGO detectors in the United States (Hanford and Livingston) together with the Advanced Virgo detector in Italy have been able to pinpoint the sky location of nearby GW candidates to areas smaller than 50 square degrees. The latter is achieved through fast and efficient data analysis pipelines. During the third observing run (O3) of Advanced LIGO and Advanced Virgo, low-latency GW alerts were publicly available within minutes through the Gamma-ray Coordinates Network (GCN) 4 4 https://gcn.gsfc.nasa.gov / and on the GW-candidate event DataBase (GraceDB). 5 5 https://gracedb.ligo.org/ For compact binaries, the information released in Open Public Alerts (OPA) included 6 6 https://emfollow.docs.ligo.org/userguide/index.html : the false-alarm rate (FAR) estimated by the search pipelines, the inferred sky position and distance of the source (Singer Price 2016 ; Singer et al. 2016a ), the probability of astrophysical origin with classification according to different mass regions (Kapadia et al. 2020 ), and the probability of neutron star matter content (Foucart et al. 2018 ; Chatterjee et al. 2020 ). Rapid and public information release enabled every astronomer with access to telescopes or neutrino detectors to search for counterparts to the GW candidates (Andreoni et al. 2019 ; Antier et al. 2020 ; Coughlin 2020 ; Coughlin et al. 2020 ; Graham et al. 2020 ; Hussain et al. 2020 ; Kasliwal et al. 2020 ; Vieira et al. 2020 ). Preliminary OPAs in O3 were generated automatically when low-latency GW search pipelines reported an effective FAR below either 1 per 2 months for the matched-filtering searches or 1 per year for the unmodeled burst searches. 7 7 https://emfollow.docs.ligo.org/userguide/analysis/index.html#alert-threshold This FAR threshold targets an overall astrophysical purity of 90% (Abbott et al. 2020a ), hence some candidates will likely originate from instrumental artifacts. GW candidates recognized as the result of an instrumental
Paper: 2016MNRAS.455.1538F (fulltext)
migration rate is removed, the bumps are smoothed out. The 100-d population between 50 and 60 R ⊙ is negligible, due to the small number of such planets in the observed WJ period distribution. 7 COMPARISON TO OBSERVATIONS The model described here is motivated by the claimed deficit of WJs around moderately evolved stars (Johnson et al. 2007 , 2011 ), as seen in Fig. 1 and Section 2 . We postulate that the reason for this deficit is that the observed eccentricity distribution of WJs around main-sequence stars is really a snapshot of a population whose eccentricities are oscillating via the KL mechanism while they migrate inwards due to tidal friction. The fact that the oscillation time-scale is short compared to the characteristic time-scale for the stellar evolution means that planets are removed from the observed sample when their periapsides oscillate to the minimum value and interact with the host star. Fig. 15 shows the result of such a model and demonstrates that, under these conditions, a pre-existing WJ population will be largely removed by the time the stars evolve to 4 R ⊙ , in contrast to the case where the eccentricities of the observed population do not oscillate. The exact location of WJ removal depends on the details of tidal forces and the perturber, but the general behaviour is well described by Fig. 16 . However, our results do not match all observations. Figs 17 and 18 show the distribution of eccentricities for our simulated systems and observed WJs (from the Exoplanet Orbit Database), respectively, with our simulated population drawn from the same period distribution. In both cases, the systems are restricted to the Jupiter mass range (0.3–3) and the period range of 10–100 d. Comparison of these two populations using the Kolmogorov–Smirnov (KS) test gives a p -value of 1 × 10 −4 , indicating they are unlikely to be drawn from the same underlying population. The discrepancy is primarily due to the significant fraction (15 per cent) of simulated WJs with high eccentricity ( e > 0.8), while no observed WJs have such high values. The lack of high-eccentricity WJs has also been noted by Dawson, Murray-Clay Johnson ( 2015 ). Figure 18. The eccentricity distribution for observed WJs, taken from the Exoplanet Orbit Database. The small number of highly eccentric planets differs significantly from the oscillating distribution, but that may be a result of low DE. The observed population also includes an excess number of low-eccentricity planets, which is difficult to reconcile with the orbital behaviour of our planets. Many of the simulated planets, even those started with eccentricity of 0.05, had a minimum eccentricity peak above 0.1, similar to the eccentricity distribution shown in Fig. 7 . This figure also shows the cumulative distribution of eccentricity values; blue, green, and red dashed lines indicate the cumulative observed distribution at 75, 90, and 98 per cent of WJs, respectively, for the simulated planet (dotted lines)
Paper: 2018A&A...617A..34M (acknowledgments)
This study is based on data collected as part of the CLASH Multi-Cycle Treasury Program with the Hubble Space Telescope, as well as on data collected at the ESO-VLT (prog. ID 186.A-0798). This analysis relies on the work done within the CLASH and CLASH-VLT collaborations, who prepared the imaging and spectroscopic data. We thank the referee for the useful comments that improved our publication. The paper is based on the Master’s thesis of VM, who thanks the University of Vienna for the grant near completion of studies. This publication is supported by the Austrian Science Fund (FWF).
Paper: 2017MNRAS.470.4739S (fulltext)
show a comparison between the new and the old interpolation method to approximate the temporal evolution of the mass (Fig. A1 ) and the Helium core radius (Fig. A2 ) of a star with mass M ZAMS = 70 M ⊙ and metallicity Z = 2.0 × 10 −2 . It is apparent that the new interpolation method works much better than the previous one, especially for estimating the values of the physical parameters that are important for evolved stars (such as the Helium core radius). It is worth noting that the new version of sevn can also interpolate tables at different metallicity. That is, if the pre-evolved tracks are not available for the metallicity Z s , sevn first evaluates and , where Z 1 ≡ Z s − Δ Z , Z 2 ≡ Z s + Δ Z and Δ Z is step in the metallicity grid. Then, the value is where and . APPENDIX B: THE FITTING FORMULA FOR PPISNE AND PISNE The new version of sevn includes a fitting formula to derive M rem accounting for PPISNe and PISNe. This formula has been obtained by fitting the masses of the compact remnants shown in tables 1 and 2 of Woosley ( 2017 ) as a function of the final Helium mass fraction and of the Helium core mass M He,f of the stars. Using the following fitting formulas, we get the parameter α P so that M rem = α P M rem, no psn , where M rem, no psn is the mass of the compact remnant we would obtain without PPISN/PISN (see equation 1 ). First, we define the following quantities We then express α P as a function of , , and M He,f : APPENDIX C: COMPARISON WITH <xref ref-type="bibr" rid="bib62">W17</xref> In this appendix, we compare our results with those presented in Woosley ( 2017 ). Fig. C1 is a detail of Fig. 2 , where we also plot the compact remnant masses taken from table 2 of W17 (black points). W17 explores the evolution of stars at Z ≃ 0.1 Z ⊙ in the mass range 70 M ZAMS / M ⊙ 150, also varying the amount of mass-loss through stellar winds to mimic the results expected from stars at lower metallicity. The fitting formula we implemented in sevn gives results in agreement with those of W17 . We obtain a comparable maximum BH mass (this work:∼55 M ⊙ , W17 : 58 M ⊙ ) and a similar mass range where a dearth of compact remnants is observed (this work: 55 ≲ M rem / M ZAMS ≲ 120, W17 : 58 M rem / M ⊙ 133). The differences between this work and W17 are due to the parameters M He,f and used for the PPISNe and PISNe fitting formula (see Appendix B ). Indeed, the values of M He,f and M fin , for different M ZAMS and metallicity, strongly depend on the adopted stellar evolutionary models. Figure C1. A detail of Fig. 2 that shows the ranges 40 ≤ M ZAMS / M ⊙ ≤ 160 and 0 ≤ M rem / M ⊙ ≤ 75. Black points are the compact remnant masses taken from table 2 of Woosley ( 2017 ). APPENDIX D: STELLAR EVOLUTION TABLES Tables D1 , D2 and D3 show the final parameters of various progenitor stars and the mass of their compact remnants when PPISNe and PISNe are turned on and when PPISNe and PISNe are switched off in sevn , for Z = 2.0 × 10 −2 , 2.0 × 10 −3
Paper: 2019MNRAS.489.3665F (acknowledgments)
ACKNOWLEDGEMENTS We thank the referee for their comments. JPB and AJR acknowledge NSF grant AST-1616598. AJR was supported by National Science Foundation grant AST-1616710 and as a Research Corporation for Science Advancement Cottrell Scholar. We thank A. Ferre-Mateu, A. Wasserman, V. Pandya, L. Chevalier, and S. Bellstedt for their help with the mask design and/or observations. We thank P. van Dokkum, P. Kroupa, and H. Haghi for their comments on an early version of this paper. The data presented herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California, and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Mauna Kea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. This research has made use the NASA Extragalactic Database (NED).
Paper: 2016ApJ...829..114B (fulltext)
our calculations on a detailed level, we show below that the envisioned picture generally holds. We note that orbital excitation by sweeping secular resonances has previously been studied within the context of the solar system (see, e.g., Ward et al. 1976 ; Ward 1981 ; Nagasawa Ida 2000 ; Nagasawa et al. 2002 ; Minton Malhotra 2011 ; Agnor Lin 2012 ). In contrast with our work, these studies do not observe capture and instead report impulsive excitation of the orbital parameters upon encounter. However, these analyses are consistently based on a second order (Lagrange–Laplace) expansion of the Hamiltonian and therefore cannot properly model the establishment of secular commensurabilities, because homoclinic curves do not exist in phase space within this framework, disallowing a resonant domain to even be defined. A counter example is provided by the N -body simulations of Nagasawa et al. ( 2005 ), where long-term capture into first-order secular eccentricity resonances is observed. A final point worthy of attention is that as the host star spins down, the assumed angular momentum hierarchy (Equation ( 2 )) breaks down. Therefore, the assumption that the stellar spin axis remains fixed in inertial space does not formally apply toward the end of the evolutionary track of δ . If m is taken to approach a typical hot-Jupiter-like mass, however, it can be shown that by the time the angular momentum hierarchy is severely violated, the value of δ is already very close to that given by Equation ( 19 ). Accordingly, this complication does not pose a practical limitation. 3.1.2. Eccentricity Evolution Thus far, we have neglected orbital eccentricities in order to obtain an integrable Hamiltonian. Recalling that eccentricity and inclination terms are decoupled to second order in either quantity (Murray Dermott 1999 ), this assumption is justified as long as the eccentricities remain small. However, the ultimate result of adiabatic capture into a secular inclination resonance raises the question of whether or not similar dynamics can occur in the degree of freedom associated with the outer orbit’s eccentricity. Fortunately, very similar techniques to those already employed above can be used to attack this problem. In direct analogy to Equation ( 8 ), the inner orbit’s longitude of perihelion will not remain stationary in time, and will instead precess at a constant rate: π = μ t . In contrast with nodal regression, however, the rate of apsidal precession comprises numerous contributions (Ragozzine Wolf 2009 ):where quantities with the subscript p refer to the planet, and c is the speed of light. For simplicity, in the above expressions we have assumed that the planetary obliquity is null (and therefore only contributes to apsidal precession and not nodal regression), the rotation rate is synchronous with the orbital frequency (Hut 1981 ), and that eccentricities are sufficiently low to warrant the retention of only leading-order terms in e (see Kopal 1978
Paper: 2020MNRAS.493.4987A (fulltext)
is one possible strategy, it is important to remember that not all input features provide insight in all evolutionary phases (i.e. the small separation loses its diagnostic potential in subgiants, White et al. 2011 ). Furthermore, not all quantities can be defined in every system. We can improve the accuracy of spi if a priori knowledge of the evolutionary stage can be supplied. This ensures that the input features and training data are exploited optimally. We have implemented an RF classifier in the spi pipeline to determine the evolutionary phase based on ν max , T eff , Δν, and . Evolutionary phases were divided according to three sampling regimes listed in Section 4.1 . Detailed results are presented in Appendix C , however, we note here that the classifier accurately distinguishes between main-sequence and subgiant stars thus ensuring with high probability that the correct phase is included in the training data. 5 OBSERVATIONAL SURVEYS AND TARGETS The new spi input features have been designed to better extract information from the stellar interior. Moreover, in this work, one of the aims is to determine how asteroseismic precision impacts the determination of key CBM properties, which amongst other things is crucial for reliable stellar ages. To this end, we have reprocessed the results from Bellinger et al. ( 2019 ) with the new pipeline which include data from the KAGES (Silva Aguirre et al. 2015 ; Davies et al. 2016 ) and Legacy (Lund et al. 2017 ; Silva Aguirre et al. 2017 ) surveys. A majority of stars in the TESS field will be observed for 27 d, far shorter than the four-year timeseries of Kepler . The duration and cadence will reveal ridges in the echelle diagram although not with the precision required to probe the interior in detail. Many targets, however, will lie in the repeated/continuous viewing zones resulting in timeseries longer than 54 d. For these stars, we can expect a comparable frequency resolution to previously studied RV targets (e.g. Procyon A, α Cen A) or solar-like oscillators in the CoRoT field which we analyse with spi below. We summarize the main properties of these stars in Table 4 , while in Fig. 4 , we compare their measurement precisions with the precision obtained for 16 Cyg A – one of the Kepler benchmarks. The combination of different surveys, precisions, and observations essential for our scientific goal. Figure 4. The frequency precision for oscillation modes with different spherical degrees as a function of radial order. Here, we compare seven CoRoT stars, α Cen A, and Procyon A with the Kepler Star 16 Cyg A. The shaded regions indicate the uncertainty stemming from different mode identifications in the oscillation spectra (see Table 4 ). Table 4. An overview of CoRoT and RV stars analysed in this study. a In the supplementary online material. b Denotes that the star is likely a subgiant. c We have used these frequencies but relabelled the radial order based on an expected regular pattern. 6 RESULTS
Paper: 2015ApJ...810..111V (acknowledgments)
The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement No. ERC-2012-AdG- 320951-DREAMS.