adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018MNRAS.475.3098D (fulltext)
with indirect estimates of the helium spread, based on the morphology of the HB. For the clusters for which these estimates were available, it was possible to verify a satisfactory consistency. The APOGEE data used in this work are of paramount importance, because they confirm that the material from which SG stars formed was exposed to a very advanced nucleosynthesis, with the activation of Mg burning and, in the most metal-poor clusters, the synthesis of silicon. This is of great help in the way towards the understanding of the possible GC polluters, because the possibility that either fast-rotating massive stars (Krause et al. 2013 ) or massive binaries (De Mink et al. 2009 ) played a role in this context can be ruled out. Indeed in neither of the two cases the temperatures of the nuclear burning regions are sufficiently hot to allow such an advanced nucleosynthesis. The only possibility left besides the massive AGB scenario is super massive main sequence stars. As shown by Denissenkov Hartwick ( 2014 ), the core temperature during H-burning reach ∼70 MK, thus triggering the activation of Mg burning and the production of aluminium. A straight comparison between the predictions of the AGB hypothesis and the chemical patterns expected when pollution from super massive stars is considered is cumbersome, because while it was shown that in the interiors of this class of objects the ignition of Mg burning occurs, the trend with metallicity, if any, needs further investigation. While in the AGB case the sensitivity of the HBB nucleosynthesis to the metallicity is a direct result of stellar evolution modelling, it is not clear how the chemical composition would affect the core H-burning of super massive stars. At the moment, this problem is still open. 6 CONCLUSIONS We discuss the chemical composition of giant stars in nine Galactic GCs, for which the abundances of C, N, O, Mg, Al, and Si are available. The wide range of metallicities ( − 2.4 [Fe/H] -0.7) and the simultaneous knowledge of the elements mentioned above offer a valuable opportunity to test the origin of multiple populations in these complex stellar systems. We concentrate on the possibility that the polluters of the intra-cluster medium, providing the gas from which SG stars formed, were massive AGB stars (), which experienced HBB at the base of their external envelope. To this aim, we calculated ad hoc AGB models, with the same metallicity and the same mixture of the stars belonging to the FG of each cluster. In agreement with previous studies on GCs in the Milky Way, we find that all the clusters show the presence of SG stars, as deduced on the star-to-star chemical differences, which define abundance patterns in the different observational planes. We find that the chemical patterns observed (Mg, Al, Si, and O in particular) are in good agreement with the theoretical expectations: (a) on the qualitative side, the spread observed for the different species follows the results from AGB
Paper: 2017MNRAS.471.1797M (fulltext)
by photon pile-up (corresponding to a nominal count rate threshold of about 150 counts s −1 ), which is known to induce distortion of the XRT spectral response (see http://www.swift.ac.uk/analysis/xrt/pileup.php ). High photoelectric absorption can also cause distortion of the spectral shape at low energies (i.e. below 1 keV, see http://www.swift.ac.uk/analysis/xrt/digest_cal.php ). For these reasons, we treated our data as follows. First, following the XRT reduction threads ( http://www.swift.ac.uk/analysis/xrt/#abs ), we extracted only grade 0 events, which helps minimize the effects of pile-up when the source is bright (>150 counts s −1 , see http://www.swift.ac.uk/xrt_curves/cppdocs.php ) and reduces the spectral distortion encountered in WT mode below 1.0 keV when the spectra are highly absorbed. Furthermore, we ignored data below 0.6 keV as at such low energies, the spectra can be dominated by strong redistribution effects associated with the WT readout process, as well as trailing charge released from deep charge traps in the CCD on time-scales comparable to the WT readout time, which results in additional low-energy events. We extracted events in circular regions centred at the source position, with variable inner radius to mitigate pile-up, and outer radius fixed at 10, 20 or 30 pixels to adapt the photons extraction to the source properties. In order to reduce the effects of pile-up to the minimum, we varied the inner radius of the extraction region until the spectral shape was no longer varying as a function of the inner radius and the count rate was lower than ∼150 counts s −1 . The outer radius had been determined based on the brightness of the source and depending on the presence of a dust scattering halo around it, which could contaminate the source emission (see Appendix A2 ). In general, we used a 20 pixels outer radius extraction region. However, when the source was very bright, we extracted the source photons in a annular region with outer radius 30 pixels (see http://www.swift.ac.uk/analysis/xrt/spectra.php ). Instead, in the cases when the presence of a dust scattering halo was likely contaminating the source (see Appendix A2 for details), we shrank the outer radius of the extraction region to 10 pixels, in order to minimize the halo contamination. In Table 1 we reported, for each observation/snapshot, the outer radius of the extraction region. We divided each event file thus obtained into time segments of variable duration. Then, we determined the duration of each segment by requiring that each segment contained about 1600 counts after mitigating the pile-up. We set the minimum exposure to 16 s, while the maximum exposure we obtained was of 224 s. These requirements ensure that all the spectra have roughly constant overall S/N and that they will be well described by the XRT response calibration files. This choice has been made in order to obtain a good compromise between S/N and sensitivity to the spectral variations –
Paper: 2020A&A...643A.167P (fulltext)
fixed tile must be smaller than the image separations, which are not known a priori. The sky area spanned by strongly lensed images, however, is ≲arcsec 2 ( Schneider et al. 2006 ; Collett 2015 ), and investigating it at such fine resolution is highly time-consuming. Nevertheless, when both galaxies or galaxy clusters and microlenses are involved, a complete solution of the system requires identification of strongly lensed and microlensed images. In addition, the overall potential may vary profoundly on short length scales. This is particularly true when many microlenses are involved: standard integrators may stall and oscillate between points corresponding to nearby solutions without converging. Finally, gravitational-wave analysis is performed using signal templates predicted by general relativity (GR). The inference of compact source parameters relies on matching gravitational-wave detector strains to a bank of known gravitational waveforms ( Allen et al. 2012 ). This method allowed LIGO and Virgo to unveil a stellar-mass population of binary black holes and produce a catalog of their properties ( Abbott et al. 2019a ), to perform tests of GR ( Abbott et al. 2016c , 2019b , c ), and to detect a binary neutron star coalescence ( Abbott et al. 2017 ). However, lensed signals resulting from the superposition of multiple images can differ significantly from unlensed GWs. As a consequence, matched filtering of lensed GWs with unlensed templates could result in missed detections or possibly biased measurements of the binary parameters. Lensed gravitational waves might also constitute a probe of microlensing. Even when microimages are as close as microarcseconds, observable beating patterns may originate in the GW signal if the time delays between the images are higher than the millisecond time resolution of the interferometers ( Lai et al. 2018 ; Abbott et al. 2019a ). Thus, lensed templates might unveil microlenses and dark matter substructures on the path traveled by the wave ( Liao et al. 2018 ). Therefore we must be able to predict GW signals from arbitrary lens configurations. LENSING GW has been designed to address these necessities: on the one hand, it implements a new solver tailored to handle very different scales in the lens potentials; on the other hand, it supplies unlensed and lensed GW signals from arbitrary lensed systems through dedicated modules. Such lensed waveforms can serve as injections in parameter estimation (PE) studies to demonstrate the inference of astrophysical parameters of lensed compact sources through Bayesian analysis. We will demonstrate Bayesian PE of lensed systems solved with LENSING GW in a follow-up study (Pagano et al., in prep.). The source code of LENSING GW and examples are hosted on GitLab 2 . 3.1. Lens equation solver The numerical solver implemented in LENSING GW introduces two novelties: a two-step procedure designed to simultaneously resolve strongly lensed images and microimages, and a dedicated
Paper: 2017MNRAS.472.2728H (acknowledgments)
Acknowledgements We thank the anonymous referee for valuable comments. GH would like to acknowledge CSIR, India for financial support. ARCs research is supported by J. C. Bose fellowship granted by DST, India.
Paper: 2016ApJ...821...38S (fulltext)
agree well with Equation ( 19 ), though there are substantial differences at lower masses. Better agreement with our models is obtained if , and that is our suggested value. The breakdown below 12 is not particularly surprising. These low-mass SNe have low energy (10 50 erg), but also low 56 Ni mass (). The combination is still appreciable even though the mass of radioactivity itself is very small. If the mass fraction of 56 Ni in a given zone, after mixing, is less than about 0.001, the decay energy, erg g 1 , of 56 Cowill, after time , be only a small fraction of the internal energy at recombination, erg g 1 , and recombination will not be greatly affected. For the very low mass explosions and mixing prescription assumed, the cobalt abundance is below this limiting value in all zones except at the very base of the hydrogen envelope. In any case the correction factor is small, less than 10, in these low-energy explosions. Combining our best fit to the no-radioactivity models with this best-fit correction factor results in good agreement with the full set of models with radioactivity included (Figure 34 ). As expected, most of the large deviations are for the lightest and heaviest stars. For small stars, the scaling relation overestimates the plateau duration because the correction term, , is too big. At high mass, there are cases with a large amount of fallback (Figure 13 )and therefore no 56 Ni production. Indeed, such short plateaus in massive stars with normal luminosities (Figure 31 ) may be an observable signature of BH formation. Figure 34. The scaling laws that showed good agreement in Figure 32 are now compared against the standard explosion models with their 56 Ni included. The models used are from the Z9.6 and N20 series. A red vertical bar represents a range of possible plateau durations calculated from the models assuming that (1) the photospheric radius had receded to 50 of its maximum value (lower bound)or (2) the whole star had recombined (upper bound). Black points result from multiplying Equation ( 14 ) with C t = 88 by the correction term, Equation ( 19 ), with C f = 50. 7.1.3. Systematics Given the generally good agreement of our SN IIp model characteristics with analytic expressions, it is worth exploring whether these systematics can be exploited to obtain insights into the masses and energies of observed events. Another interesting question is whether SNe IIp, or some subset, can be used as standard candles. Neglecting the radioactive correction, Equation ( 19 ), the luminosity and plateau duration are proportional to powers of the explosion energy, pre-SN radius, and envelope mass. These are not independent variables. The radius of the pre-SN star increases monotonically with its main-sequence mass, ,andto good approximation in the mass range where most SNe IIp occur (920 ), (Figure 35 ). Above 20 , the hydrogen envelope mass of the pre-SN star declines with owingto mass loss, but below 20 , it too has a monotonic
Paper: 2019MNRAS.485.4274H (acknowledgments)
ACKNOWLEDGEMENTS We sincerely thank the referee for valuable suggestions on the paper. This research is in part based on the data obtained from the Chandra Data Archive, and has made use of software provided by the Chandra X-ray Center (CXC) in the application packages ciao, ChIPS , and Sherpa . This research has used observations obtained with XMM–Newton , and the ESA science mission, with instruments and contributions directly funded by ESA Member States and NASA. C-PH and C-YN are supported by a GRF grant from the Hong Kong Government under HKU 17300215P. WCGH acknowledges support from STFC in the UK through Grant No. ST/M000931/1.
Paper: 2018MNRAS.478.3220M (acknowledgments)
ACKNOWLEDGEMENTS This work was supported by the UK Science and Technology Research Council (STFC) No. ST/M001334/1 and the COST action MOLIM No. CM1405. This work made extensive use of UCL’s Legion and DARWIN and COSMOS high performance computing facilities provided by DiRAC for particle physics, astrophysics and cosmology and was supported by STFC and BIS.
Paper: 2017ApJ...835...51L (fulltext)
data. Therefore, as a first step, we have computed the autocorrelation function (Equation ( 9 )) on timescales of up to 100 years for different directions on the celestial sphere. These autocorrelation functions can be formally approximated by a superposition of several components decreasing exponentially with different characteristic times T i . In contrast to the standard deviation, the characteristic decay time of exponents depends not only on the direction on the celestial sphere but also on the lower limit of integration over coordinates ( R min ), as can be seen from Figure 4 . Note, that the behavior of autocorrelation functions for both galactic models (DB and BS) is similar; therefore, in the following analysis, we are restricted only by the DB model. With increasing of R min , i.e., with increasing the minimal impact parameter, the characteristic decay time of exponents becomes larger, which seems quite natural. At the same time, varying the parameters of calculation does not affect an exponential form of the normalized autocorrelation function. Figure 4. Normalized autocorrelation functions calculated for the DB model for two directions in the sky at different minimal impact parameters. For the autocorrelation function in a form of a decaying exponent , the PSD is simplywhere T is characteristic time of the exponent, and . From this formula and Figure 5, it is clearly seen that on the reasonable observing timescales (up to one hundred years) the PSD index equals −2 for any direction on the celestial sphere and does not depend on the characteristic time. Thus, if an analysis of the apparent celestial positions of extragalactic sources, including the reference sources of the ICRF, reveals a component with a slope of −2 in their power spectra, this component can be explained by the collective influence of stars and compact objects in the Galaxy. Figure 5. Spectral index of PSD calculated for two exponents with the characteristic time of 100 and 1000 days. For the analysis of astrometric observations of staggering precision, it is important to know an expected value of the jitter of reference sources, arising due to the local non-stationarity of the Galactic gravitational field, for a specific time interval. Intuitively, the larger the time interval between observations, the greater the relative offset of sources can be. If we know the behavior of the autocorrelation function of the studied process in time, we can predict the expected value of the jitter (dispersion) for any time interval. For illustration purposes, the dependence of the coefficient (Equation ( 3 )) on time is shown in Figure 6 . This coefficient has been calculated for the DB model for different directions in the sky, and for . To obtain maps of the conditional standard deviation (see Equation ( 3 )), the corresponding coefficients were determined for all sky directions for three time intervals (three months, one year, and ten years) and convolved with the
Paper: 2015MNRAS.446.2186M (acknowledgments)
We thank the anonymous referee for their helpful comments which improved the clarity of the paper. This research was based on data taken with the Anglo-Australian Telescope. Parts of this research were conducted by the Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO) through project number CE110001020. SMC acknowledges the support of an ARC future fellowship (FT100100457).
Paper: 2016ApJ...829..133K (fulltext)
intrinsic to the sources, while intervening material introduces a small but pervasive broadening of the FD distribution. We explore further the implications of the latter effect in Section 6. Further exploration of the properties of the background sources from QU-fitting (O’Sullivan et al. 2012 ; Sun et al. 2015 ) and analysis of the shape of the plots is beyond the scope of the current work, but we plan to develop this further in the future. The results of the QU-fitting will also be very interesting to be compared with . 6. DISCUSSION In this section, we will try to be more quantitative about the size of the Faraday Effects that are associated with the presence of intervening material, as revealed by Mg ii absorption. This will enable us to better characterize the physical properties of this material. We estimate the dispersion of φ introduced by intervening systems. The largest separation in the empirical distribution functions for sources with and without interveners is observed at m −2 . As noted earlier, this value should be treated with caution because the φ resolution limit of our experiment is roughly m −2 for VLA and m −2 for ATCA. The difficulties of the RMSF can to a certain degree be circumvented by simply subtracting in quadrature the median of sources with and without absorbers (since the RMSF has been subtracted from both equally). This yields an estimate for the excess m −2 . The maximum separation seen in the distribution is at m −2 . However, here this value should also be treated with caution because of the non-monotonic structure in and the rather ad hoc approach to choosing the wavelength range for the fit (only longwards of the peak in polarization). Likewise, subtracting the median values of the distribution with and without absorbers in quadrature gives an estimate for the excess m −2 . and attempt to trace by different approaches. The fact that their values are similar gives some confidence that the proposed scenario is self-consistent. From the current work, we will use a value of order 10 rad m −2 as a crude order of magnitude estimate of the FD dispersion that is associated with intervening systems. This is an estimate of the range of the observed φ within (i.e., across the face of) a given intervening system. To convert to the rest-frame of the absorber, we must multiply by , i.e., by a factor of the order of two for our median absorber redshift of , yielding of the order of m −2 . This value is of the same order of magnitude as the spread in Galactic RMs m −2 as estimated by Schnitzeler ( 2010 ). Other works have claimed m −2 for the Large Magellanic Cloud (Gaensler et al. 2005 ) and m −2 for the Small Magellanic Cloud (Mao et al. 2008 ). As in Bernet et al. ( 2008 ), we can crudely associate a φ with an estimate of magnetic field strength using an estimate of the free electron column density in the interveners based on the known Mg ii column density. Assuming a compact absorber system at redshift z and the
Paper: 2018A&A...609A..51W (acknowledgments)
The authors thank the referee Ludovic Petitdemange for numerous useful comments and suggestions, which improved the paper. The simulations have been carried out on supercomputers at GWDG, on the Max Planck supercomputer at RZG in Garching, in the HLRS High Performance Computing Center Stuttgart, Germany through the PRACE allocation “SOLDYN” and in the facilities hosted by the CSC-IT Center for Science in Espoo, Finland, which are financed by the Finnish ministry of education. J.W. acknowledges funding by the Max-Planck/Princeton Center for Plasma Physics and from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA grant agreement No. 623609. This work was partially supported by the University of Colorado through its support of the George Ellery Hale Visiting faculty appointment, the Swedish Research Council grants Nos. 621-2011-5076 and 2012-5797 (A.B.), and the Academy of Finland Centre of Excellence ReSoLVE 272157 (J.W., M.R., S.T., P.J.K., M.J.K.) and grants 136189, 140970, 272786 (P.J.K).
Paper: 2020MNRAS.491.3996V (acknowledgments)
ACKNOWLEDGEMENTS We thank Dr. Eleanor Sansom for a helpful and detailed review of an earlier version of this manuscript. Also, we thank Dr. Auriane Egal for suggestions about the modelling of the Draconids. Funding for this work was provided by the NASA Meteoroid Environment Office under cooperative agreement 80NSSC18M0046. PGB acknowledges funding support from the Canada Research Chair program and the Natural Sciences and Engineering Research Council of Canada.