adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016ApJ...822L..17T (fulltext)
but the authors note that the resulting parameters A and B are not particularly sensitive to this, provided that the function can adequately reproduce the variation of s ad . This function comprises six parameters that define the relationship of s ad across the – plane, and the least squares minimization algorithm of Markwardt ( 2009 ) was then used to determine their values (listed in Table 1 ). The process is repeated for different convective envelope compositions (see Figure 3 ), each of which require a unique projection of the – plane. This fitting process effectively reduces the dimensionality of the initial variation of s ad by projecting the – plane onto an axis that is aligned with the convective envelope adiabats. The process used in this work is only one possible method for reducing the dimensionality of the problem, and further study using other statistical tools, such as principle component analysis, may yield additional insights into the fundamental relationship between convection zone entropy and the stellar surface parameters. Figure 3. Adiabatic entropy from sets of 3D simulations with varied chemical compositions (similar to the lowermost panel in Figure 2 ) presented along projections in the – plane. Each metallicity requires different coefficients for A and B , which correspond to different adiabatic contours in the – plane. Table 1 Parameters for the Function Fit to s ad from Magic et al. Simulations in the Projected – Plane () Since the convection zone adiabatic entropy value in a stellar model is determined by the mixing length parameter, the curves in Figure 3 basically show how we need to change α as a function of and . Of course, the first step would be to determine which numerical value of α yields a particular s ad given the rest of the physics to set the mixing length scale, and thus determine how s ad changes with α . After setting the relationship between s ad and α , all that is required is to follow this relationship (i.e., the curve in Figure 3 ) as the star evolves. Since each time step in a stellar evolution calculation changes and T eff , we will need to keep changing α as we evolve a model. The two panels of Figure 4 outline, in principle, the steps that must be taken to translate the adiabatic specific entropy derived from a 3D simulation into the corresponding entropy calibrated value of α to be used in the 1D MLT stellar model. Figure 4 (left) shows, in the same entropy calibration plane as Figure 3 , the locus of a set of 3D simulations, all with the same chemical composition, but with different values of s ad in the deep part. Also in the left panel are three 1D MLT models, all with the same metallicity and surface conditions as the 3D simulations. Presented relative to the projected and coordinates in this way, the evolution tracks begin on the zero-age main sequence with a relatively low s ad , which increases as the model approaches the terminal age. For the purpose of demonstrating our calibration
Paper: 2017MNRAS.465..858G (fulltext)
that by defining a cluster subsample using clusters appearing both dynamically relaxed and hosting a CC, the luminosity–temperature ( LT ) relation appears self-similar, compared to unrelaxed and NCC clusters. While this method may be the preferred choice for defining subsamples of clusters for mass derivations, this limits the cluster sample size used for mass calculations; thus, the derived scaling relation may not be representative of the whole cluster population. This paper aims to measure the masses for a complete sample of 34 clusters to measure the X-ray LM scaling relation, utilizing hydrostatic mass estimates and fully accounting for selection effects. The outline of the paper is as follows. In Section 2 , we discuss the sample selection and data analysis. Section 3 details the cluster analysis and determines the dynamical state of individual clusters. Notes on individual clusters are given in Section 4 . Our results are presented in Section 5 . The discussion and conclusions are presented in Sections 6 and 7 , respectively. Throughout this paper, we assume a Wilkinson Microwave Anisotropy Probe 9 ( WMAP 9) cosmology of H 0 = 69.7 km s −1 Mpc −1 , Ω M = 0.282, Ω Λ = 0.718 and σ 8 = 0.817 (Hinshaw et al. 2013 ). 2 SAMPLE AND DATA PREPARATION The sample of clusters used in our analysis was defined by the conditions given in Dahle ( 2006 ). The sample of clusters represents a complete sample of X-ray luminous clusters taken from the RASS-based, X-ray flux-limited ROSAT Brightest Cluster Sample (BCS) of Ebeling et al. ( 1998 ) and its low-flux extension (eBCS; Ebeling et al. 2000 ). Dahle ( 2006 ) imposed a lower cutoff in X-ray luminosity of L = 6 × 10 44 erg s −1 (the limit based upon a cosmology assuming Ω M = 0.3, Ω Λ = 0.7 and h = 0.7), corresponding to a sample of 36 clusters within the redshift range 0.15 ≤ z ≤ 0.30. Fig. 1 plots the luminosity–redshift distribution of the BCS and eBCS, with the yellow shaded region highlighting the region enclosed by the luminosity and redshift cuts defining our sample selection. Note that two of the clusters were dropped from the cluster sample, as detailed below, leading to a final sample of 34 clusters. Figure 1. Plot of the luminosity–redshift distribution of the BCS and eBCS clusters. The yellow shaded region highlights the region enclosed by the luminosity and redshift cuts imposed to define our cluster sample (see Section 2 ). The cluster A689 satisfies these selection criteria, but was noted in the original detection as having a large portion of its flux coming from embedded point sources, and was therefore excluded from the cluster sample. The source of the embedded point source was found to be a central BL-Lac object (Giles et al. 2012 ), with the re-analysis determining a cluster X-ray luminosity ∼10 times lower than quoted in the BCS, well below the sample cutoff X-ray luminosity. Furthermore, we found that the redshift given for the cluster Zw5768 in Ebeling et al. ( 1998 ) was
Paper: 2016MNRAS.457.4330Y (acknowledgments)
APY gratefully acknowledges financial support from an EXPERTS III Erasmus Mundus doctorate fellowship.
Paper: 2019MNRAS.484...19J (fulltext)
full parameter space are shown in Table 1 , describing both the prospects per field and averaged over all Galactic Plane fields tested. Figure 4. Colour maps displaying the period determination of LMXBs possible in LSST field 630 with observing strategies astro_lsst_01_1004 , Minion_1020 , baseline2018a , and Minion_1016 . The y -axis denotes the orbital period in days, and the x -axis denotes the reddened r magnitude before adding contributions from ellipsoidal modulation, flaring, and noise. The colour denotes the significance of the period detected. If the measured period differed from the actual period by more than 5 per cent, then the significance was set to zero. The graph shows a bimodality in the significances of period determination, as recovered periods that had low significance were often incorrect and manually set to zero. Table 1. The fraction of the simulated parameter space for which P orb was correctly recovered for each observing strategy, both for the individual LSST fields and the total, combined over all three Galactic Plane fields. The initials denote which cadence was used for that field; South Celestial Pole (SCP), Galactic Plane (GP), or Wide-Fast-Deep (WFD). The reddening is listed in magnitudes. The reddening and coordinates refer to the centre of the field. The P orb recovery is worst for the baseline2018a observing strategy, as it only correctly recovers 0.23 of the simulated parameter space. This is to be expected as although it has a similar number of observations per field as Minion_1016 , a 25 per cent segment of the observations corresponding to potential outburst durations was removed from the full survey lifetime. Therefore, it offered the fewest usable observations per Galactic Plane field of any strategy. This is then followed by Minion_1016 that correctly recovers 0.46 of the parameter space, the low fraction is again due to the relatively small number of Galactic Plane observations per field. The two strategies that performed best were Minion_1020 and astro_lsst_01_1004 that correctly recovered P orb for 0.70 and 0.69 of the simulated magnitude- P orb parameter space (respectively), averaged over the Galactic Plane fields. The vast majority of the incorrectly recovered periods for both strategies had no observations in LSST’s visible magnitude range, within that region of parameter space. In these regions, there is no potential for good recovery of P orb , regardless of the number of observations. In order to evaluate the relation between Galactic reddening and period determination, the P orb recovery and reddening were plotted against both magnitude and P orb . In order to construct the magnitude-reddening graph, the significance of the P orb recovery was first averaged over all 20 periods for each region of parameter space with a distinct magnitude, field, and strategy. This average P orb recovery significance per magnitude was then plotted against Galactic extinction. Each significance-extinction
Paper: 2019ApJ...885L...2P (fulltext)
exact derivative of P ) and (the derivative when just GWs are considered) because this difference will be important for subsequent discussions. In this case , so thenGiven that will be difficult to measure, I focus on the simpler scenario where η = 1 with . To find the second derivative including tides, it is helpful to first rewrite Equation ( 6 ) asTaking the derivative of both sides of this expression, Rewriting all the first derivatives in terms of , dividing by J orb , and then collecting similar terms, the final result isWhen combined with Equation ( 6 ) for (using for consistency), this expression provides an analytic relations for . Comparing Equations ( 9 ) and ( 12 ) shows that tidal interactions make the second derivative even more negative than the GW only case. 4. Braking Index Beyond the values of the first and second derivative, another way to think about tides is in terms of a braking index. The basic idea is analogous to pulsars (Shapiro Teukolsky 1983 ) where one wishes to measure n such thatThe main difference is that here Ω refers to the orbital frequency of the binary rather than the pulsar spin frequency (and perhaps it should be referred to as an “acceleration index” here), although also see discussions of the braking index by Nelemans et al. ( 2004 ) and Stroeer et al. ( 2005 ) in the context of AM CVn systems. It is straightforward to showUsing the relations from Sections 2 and 3 , for GWs only (Webbink Han 1998 ),while when tides are includedThus fitting for the power law in real systems could be another way to infer the presence of tides. Although the exact value of n depends on the specific parameters of the binary and the degree of tidal locking, simply showing that n > 11/3 would demonstrate tides are occurring without having to know these parameters. 5. The Rate of Change of Tidal Locking An additional factor in that is often not considered is , the rate of change of the tidal locking. This will depend in detail on the model for the tidal interaction. In Piro ( 2011 ), the tide is treated using a parameterization with a standard quality factor Q . When this tidal interaction is integrated forward in time, it is found that the binary reaches an equilibrium spin at any given point where the ratio of the tidal forcing frequency to the orbital frequency is roughly the ratio of the tidal synchronization time τ tide to the GW inspiral time , i.e.,Furthermore, this work finds that for constant Q , . This ratio thus gets smaller at shorter orbital period, resulting in a more tidally locked binary. In reality, this will depend on the details of how the tides act, and so I consider a general form of with β > 0. Assuming a model of this form and taking the derivative results inThis particular model has a couple of important consequences for . First, it makes even more negative. Second, it adds corrections to even when η ≈ 0 because it implies a rapidly changing η , while for η ≈ 1 this model gives . In the future, it would
Paper: 2017A&A...600L..11C (acknowledgments)
The authors thank the referee K. Heng for useful and constructive comments. This research is based on observations made with the Gran Telescopio Canarias (GTC), installed in the Spanish Observatorio del Roque de los Muchachos, operated on the island of La Palma by the Instituto de Astrofísica de Canarias. This work is partly financed by the Spanish Ministry of Economics and Competitiveness through projects ESP2013-48391-C4-2-R, ESP2014-57495-C2-1-R, and AYA2012-39346-C02-02. G.C. also acknowledges the support by the National Natural Science Foundation of China (Grant No. 11503088) and the Natural Science Foundation of Jiangsu Province (Grant No. BK20151051). This research has made use of the VizieR catalogue access tool, CDS, Strasbourg, France ( Wenger et al. 2000 ).
Paper: 2017A&A...601A.131W (acknowledgments)
I am grateful to Matthias Bartelmann and Rüdiger Vaas for their strong support. I wish to thank Mauricio Carrasco, Agnese Fabris, Matteo Maturi, Sven Meyer, Björn-Malte Schäfer, Gregor Seidel, Nicolas Tessore, and the anonymous referee for helpful comments and discussions, and gratefully acknowledge the support by the Deutsche Forschungsgemeinschaft (DFG) WA3547/1-1.
Paper: 2020A&A...634A.123R (acknowledgments)
We thank the anonymous referee for his/her comments that helped to improve this work. We thank Carlton Baugh for his careful reading of the manuscript that has helped us to improve the text. This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement No 734374. This work has been supported by MINECO grants AYA2015-71825 ESP2015-66861. IEEC is partially funded by the CERCA program of the Generalitat de Catalunya. This work was also partially supported by Agencia Nacional de Promoción Científica y Tecnoóogica (PICT 2015-3098), the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET, Argentina) and the Secretaría de Ciencia y Tecnología de la Universidad Nacional de Córdoba (SeCyT-UNC, Argentina). MS has been supported by the European Union’s Horizon 2020 research and innovation programme under the Maria Skłodowska-Curie grant agreement No 754510 and National Science Centre (grant UMO-2016/23/N/ST9/02963). MM acknowledges support from the Beatriu de Pinos fellowship (2017-BP-00114). We made an extensive use of the following python libraries: http://www.numpy.org/ , http://www.scipy.org/ , https://www.astropy.org/ and http://www.matplotlib.org/ . This work has made use of CosmoHub. CosmoHub has been developed by the Port d’Informació Científica (PIC), maintained through a collaboration of the Institut de Física d’Altes Energies (IFAE) and the Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), and was partially funded by the “Plan Estatal de Investigación Científica y T’ecnica y de Innovación” program of the Spanish government. EJG and FR would like to specially thank the contribution of Darío Graña who helped to run the algorithms. Gracias por las picadas. Go team Barcelona! EJG, FR and AO thank Orion for the laughter and the good luck.
Paper: 2016A&A...585A..89A (fulltext)
of 5000 simulated white noise time series; d) : LS periodogram of the average of 5000 simulated red noise time series; e) : integrated WWZ power spectrum of 5000 simulated red noise time series; f) : WWZ power spectrum of the average of 5000 simulated red noise time series. Fig. 6 Statistical significance test of WWZ-detected periods in Figs. 2 c and 3 c. The x -axis represents the peak power of simulated periodograms at the periodicity under investigation. The y -axis gives the occurrence number within equally wide bins of 1. Top : histogram obtained from 5000 simulated white noise time series. Bottom : histogram obtained from 5000 simulated red noise time series. The vertical red dashed lines denote the peak powers of the periodic components identified in Figs. 2 and 3 . In the regions to the right of the line, the WWZ power of the simulated fake light curves is higher than the observed value. The ratio of the accumulated count of these light curves to the total number used in the MC test (i.e. 5000) determines the probability of the observed periodicity caused by the noise. As in to Sect. 2.1 , we also evaluated how the noise affected the WWZ power spectrum. The statistical confidence tests were performed using Monte Carlo (MC) simulations ( Linnell Nemec Nemec 1985 ). We first generated 5000 white noise time series, which had the same number of sampling points, temporal baselines, and sampling intervals as the observed light curve. We then calculated the WWZ periodograms for the 5000 white noise time series using the same parameters as for Figs. 2 c and 3 c. The averaged white noise power spectrum is shown in the top of Figs. 4 and 5 . The red noise power spectrum was also obtained in the same way (bottom of Figs. 4 and 5 ). No significant peaks were found at the positions of P1, P2, and P3, in either of the white noise or red noise spectra. For a quantitative evaluation of the statistical confidence, we counted the number of simulated light curves, which show higher power than the observed periodic components (P1, P2, and P3) at their peak positions. The ratio of the accumulated number to the total number of 5000 test light curves gives the probability of the false detection (i.e. the detected peak could be due to random noise). The percentage of the false detections in each case is below 0.1 (see Fig. 6 ). Therefore, the possibility that P1, P2, and P3 are generated by stochastic noise can easily be ruled out. 3. Discussion The periodicity analysis in Sect. 2 results in the detection of three possible periods. The properties of individual periodic components are discussed in the following: 1. P1 = 2.65 d . This is the most prominent pe-riodic component in both the LS periodogram and the WWZpower spectrum, which confirms the first detection by Griséet al. (2013) using the LS tech-nique. The component P1 is remarkable in the first two-thirdsof the interval covered by the light curve. It starts with a slightlylonger period of 2.67 d, then
Paper: 2016ApJ...828....3B (fulltext)
uvm2 − u and u − v colors of ASASSN-15lh. The SN begins as quite blue but reddens with time until the rebrightening, during which the SN becomes bluer in the MUV–NUV. Bottom panel: the integrated luminosity (in units of erg s −1 ) of ASASSN-15lh between 1600 and 6000 Å. Overplotted is the integrated luminosity of the SLSN II SN 2008es, which is of comparable brightness to the rebrightening event, as well as a bright SN IIn and a normal SN Ia. Table 1 Swift /UVOT Photometry of ASASSN-15lh Note. Magnitudes are given in the UVOT/Vega system for easier comparison with other SNe published in that system (see Brown et al. 2014 for a review). The 3 σ upper limits and lower (saturation) limits come from the exposure and background parameters. Most of the photometry of ASASSN-15lh is not close to either of these limits, but they are given for completeness and easy comparison with other SN photometry tables from the Swift Optical/Ultraviolet Supernova Archive (SOUSA; Brown et al. 2014 ). This table gives a portion of the data. (This table is available in its entirety in machine-readable form.) Only a portion of this table is shown here to demonstrate its form and content. A machine-readable version of the full table is available. One might be concerned about the effect of the optical tails of the uvw2 and uvw1 filters (sometimes referred to as the “red leaks”), which still have 1/1000 of the peak transmission all the way to 5000 Å (see Breeveld et al. 2011 and Brown et al. 2010 for graphical descriptions of the revised filter throughput curves). To estimate the contribution from the optical tails, we compare the count rates to those expected through synthetic filters similar to uvw2 and uvw1 but cutting off sharply to zero throughput at 2500 and 3300 Å (Brown et al. 2010 ). Using a blackbody spectrum corresponding to 16,000 K (the hottest estimated below), the counts coming from the redder portion correspond to 12% and 3 % of the total in the uvw2 and uvw1 filters, respectively. Using a blackbody spectrum corresponding to 10,770 K (the coolest estimated below), the extra counts coming from the redder portion correspond to 20% and 5 % of the total in the uvw2 and uvw1 filters, respectively. Thus the contribution from the red tails increases as the SN reddens, but never contributes a large fraction of the counts. The wavelengths beyond which are considered to be part of a red leak (as opposed to just the long wavelength edge of the filter) can be arbitrary and require a spectral model as well as the filter curves to quantify. Even if the red leak contributed a significant fraction of the observed count rate in the UV filters, the analysis below takes the red leaks into account. For example, blackbody fits are performed by comparing the observed count rates to those predicted from blackbody spectra through the whole filter. Brown et al. (2016, AJ accepted) give a detailed description of issues involved with the conversion of Swift /UVOT broadband