adsabs/WIESP2022-NER huggingface dataset:
Paper: 2016MNRAS.459..455P (fulltext)
keV. As these features are not commonly observed in Galactic XRBs, they imply a new spectral regime, which is usually referred to as the ‘ultraluminous state' (Roberts 2007 ; Gladstone et al. 2009 ), and may be associated with super-Eddington accretion. A few luminous ULXs have very recently been observed with NuSTAR , resulting in spectra which demonstrate that the high-energy curvature persists above 10 keV (Bachetti et al. 2013 ; Walton et al. 2013a ; Rana et al. 2015 ), confirming this result. The spectral curvature may arise either from Comptonization in a cold, optically thick corona lying above an accretion disc (e.g. Stobbart et al. 2006 ; Gladstone et al. 2009 ); from a modified accretion disc, i.e. a slim disc (Mizuno et al. 2007 ); or the hot inner regions of the disc, which are highly distorted by advection, turbulence, self-heating and spin (Beloborodov 1998 ; Suleimanov et al. 2002 ; Kawaguchi 2003 ). The soft component is thought to originate from the photosphere of a radiatively driven wind that is ejected from the spherization radius of the accretion disc down to its innermost regions, which is expected to occur at super-Eddington accretion rates (e.g. Shakura Sunyaev 1973 ; Ohsuga Mineshige 2007 ; Poutanen et al. 2007 ; Ohsuga et al. 2009 ). Partially inhomogeneous winds in ULXs may explain the puzzling short-term variability reported in some ULXs (Heil, Vaughan Roberts 2009 ), through a combination of inclination angle and accretion rate (e.g. Middleton, Sutton Roberts 2011 ; Takeuchi, Ohsuga Mineshige 2013 ; Middleton et al. 2015 ). These same factors also determine the observed energy spectrum (e.g. Sutton, Roberts Middleton 2013b ; Pintore et al. 2014 ). On the other hand, HLX-1 in ESO 243-49 (Farrell et al. 2009 ), which has a peak luminosity of ∼10 42 erg s −1 , is observed with a strikingly different set of properties than standard ULXs. It displays a spectral evolutionary pattern similar to that of the Galactic BH X-ray transients (e.g. McClintock Remillard 2006 ; Fender Belloni 2012 ), undergoing periodic outbursts (Godet et al. 2014 ; Webb et al. 2014 ) in which the source switches from a high-luminosity soft state to a fainter hard state (Servillat et al. 2011 ). Multiwavelength emission has been detected from HLX-1 as it undergoes this outburst cycle. In particular, radio emission has been detected, which is associated with discrete jet ejection events (Webb et al. 2012 ), similar to those that have been observed during hard -to- soft state transitions state in Galactic BHs. By assuming that HLX-1 was in a similar state, the authors of that study were able to refine the BH mass estimates to ∼10 3 –10 4 M ⊙ . Other good IMBH candidates include: M82 X-1 (Feng, Rao Kaaret 2010 ), for which a BH mass of ∼400 M ⊙ has been suggested based on the tentative identification of twin-peak quasi periodic oscillations (Pasham, Strohmayer Mushotzky 2014 ); and an X-ray source in the galaxy NGC 2276 (Mezcua et al. 2015 ).
Paper: 2016A&A...592A..64B (acknowledgments)
We thank the anonymous referee for a detailed report that was very helpful in improving the manuscript. A.B. is grateful to the Austrian Science Fund (FWF) for funding (projects P19300-N16 and P23946-N16). The authors thank B. Bösch (Innsbruck) for providing a Python code of the Kelson ( 2003 ) method for improved sky subtraction in the FORS2/MXU spectra. This publication is supported by the Austrian Science Fund (FWF).
Paper: 2018ApJS..237...27H (fulltext)
24 μ m source in MIPSGAL data (Carey et al. 2009 ). This emission feature probably represents a large, red AGB star. Masers that are associated with neither SFRs nor AGB stars have an unknown association. In Table 2 we present data for all 325 H 2 O maser sites detected during the RAMPS pilot survey. Table 2 gives the maser positions, the errors on the positions, the velocity and intensity of the brightest maser line, and the associated environment of each maser. We found that out of 325 detected masers, 185 (57% ± 4%) are associated with an SFR, 92 (28% ± 5%) are associated with an AGB star, and 48 (15% ± 5%) have an unknown association. Figures 21 and 22 show histograms of the maser intensities and velocities for the full sample of masers and histograms of the masers separated by association, respectively. Figure 21 shows that the slopes of the maser intensity distributions look roughly like a power law past the peak of each distribution. The sharp cutoff at ∼1 K in each distribution represents our completeness limit. This number is expected, since the typical 1 σ rms in our H 2 O spectra is ∼0.4 K and we require that each detected maser has an intensity of at least three times the noise. While the intensity distributions of the various maser groups look similar, the distribution associated with SFRs shows several more masers with intensities in excess of 100 K. Figure 22 shows that we have detected masers predominantly at positive velocities, particularly the masers associated with SFRs. On the other hand, both masers associated with AGB stars and those with an unknown association have a much broader spread in their velocity distributions. Figure 21. From top to bottom, histograms of maser intensities for the full sample of masers, the masers associated with SFRs, the masers associated with AGB stars, and the masers with an unknown association, respectively. The histograms qualitatively look similar, with a roughly power-law slope at large intensities and a sharp cutoff at ∼1 K. Figure 22. From top to bottom, histograms of maser velocities for the full sample of masers, the masers associated with SFRs, the masers associated with AGB stars, and the masers with an unknown association, respectively. The distribution of masers associated with SFRs is more concentrated at positive velocities than all other distributions. This difference may indicate that some of the masers associated with AGB stars are in the Galactic halo. Table 2 RAMPS H 2 O Masers Note. Starting from the leftmost column, we present the Galactic longitude, the Galactic latitude, the error in the Galactic longitude, the error in the Galactic latitude, the velocity of the brightest channel in the spectrum, the intensity of the brightest channel in the spectrum, and the associated environment. We classify H 2 O masers as being associated with a star-forming region (SFR), an asymptotic giant branch (AGB) star, or an unknown environment. (This table is available in its
Paper: 2019MNRAS.485.1665K (fulltext)
parameters as reported in Table C1 . The N comp = 2 results are fully consistent with those reported by Patruno et al. ( 2017 ) within their confidence intervals. Table C1. GMM results for the f spin data set for NSs in LMXBs from Patruno et al. ( 2017 ), assuming negligible measurement errors. Looking at the various statistical criteria as a function of number of components N comp = 1, 2, 3 (also illustrated in Fig. C1 ), these are also fully consistent with the findings of Patruno et al. ( 2017 ): again the likelihood ratio alone is already in favour of two components (), but only additional criteria can yield a robust decision, and on its own, the additional gain of could tempt us to use an even more complex model. Fortunately, in this case AICc and BIC agree in clearly preferring 2 components over 1 (with Δ of 7–11), and also prefer 2 over 3 by Δ of almost 5. A leave-one-out CV test (see Fig. C2 ) also shows the two-component fit to be almost as stable as a single component in this case. A MixtureInf EM-test reports a nominal p -value of 0.008 which also seems to reject a single-component hypothesis much more clearly than in the DNS case. As in Section 2.6 the exact value needs to be interpreted with caution due to the low N data , but this being only a comparison example, no further simulation tests have been conducted in this case. Figure C1. Model selection criteria for GMMs with N comp = 1, 2, 3 applied to the Patruno + data set of f spin for NSs in LMXBs. The first three panels are for sklearnGMM fits. The last panel gives the average Bayesian evidence from 10 cpnest runs at each N comp . AICc, BIC, and are plotted as differences to the N comp = 1 value. Figure C2. Illustration of leave-one-out CV on the Patruno + f spin data set. Each dotted line corresponds to a fit after leaving out one data point. In this case, the fitted distributions are relatively stable not just for N comp = 1, but also for the preferred fit with N comp = 2, and only for even more components they start becoming unstable. Figure C3. Posteriors from cpnest runs on the LMXB data set for a single Gaussian (left-hand panel) and for a two-component GMM (remaining panels). Note that σ 1 appears twice on the x -axis of the last two panels. Figure C4. Median GMM distribution functions p ( f spin ) inferred with cpnest for one (blue solid line) or two components (orange dashed line), superimposed on a data histogram, and with 90 per cent quantiles background ‘haze’. cpnest results for this data set (with uniform priors in C k , log-uniform in σ k within Hz and Gaussian μ k priors of width 70 Hz) are very consistent with the other GMM fits, except for some differences in the more degenerate N comp = 3 case. The evidence ratio is in favour by ≥16 of 2 components over 1, with 3 components preferred by a marginal factor of only 3 and much less robust results. Overall, the LMXB data set seems to be a good example for information criteria, EM-test and Bayesian evidence
Paper: 2020A&A...635A..21W (fulltext)
by Weppner et al. (2015) , which presents a new cold EOS derived from the pressure derivative of the bulk modulus and the polytrope method. Where a polytrope is a structural assumption between pressure and density and it is also a solution to the Lane-Emden equation. The polytrope is defined as: where p E is the polytrope exponent. The authors show that the polytrope exponent is equivalent to the pressure derivative of the bulk modulus, 1 . This allows for an implementation of real material variables within the polytrope method. This is done by augmenting the classical Lane-Emden equation to handle zero pressure densities and a variable polytrope exponent. To correctly model the polytrope exponent, Weppner et al. (2015) also present an EOS (the Varpoly EOS) based on a variable polytrope exponent: The parameters A 0 , A 1 , and A 2 are connected to the extremes of the polytrope exponent by ambient pressure data and Thomas-Fermi Dirac (TFD) theory ( Salpeter 1967 ). In spite of its simplicity, the EOS provides an improvement in the modeling of P, B, B ′ over the other popular finite strain EOS. At extreme pressures, solids are predicted to follow TFD high pressure theory. Within TFD theory the infinite pressure asymptote of the polytrope exponent goes toward . This is the value that a finite strain EOS should go to, however, the B-M EOS goes toward a higher asymptote of and the Vinet EOS, in contrast, goes toward a lower asymptote of . The sensitivity to the compression curve with respect to the polytrope exponent is clearly shown in Weppner et al. (2015) and this highlights its importance when dealing with planetary interiors. The Varpoly EOS acts as the cold part of our EOS and in the following sections, we add a temperature dependent term to it. In addition, we develop a new model for the Gruneisen parameter, which is shown to fit well with experimental data and adheres to infinite pressure values. To correctly model the vapor transition, we introduce an expanded state model which can be seen to give good fits to prior estimates of the vapor curve. The EOS is thenfit to experimental data and compared to both the Tillotson EOS and the ANEOS model. We also test its caliber to model planets by fitting it to the Preliminary Reference Earth Model ( Dziewonski Anderson 1981 ). In the second paper, we perform numerical simulations on the lunar-forming impact, to test its merit in planetary impacts. In Sects. 2 and 3 we go through the development of the EOS. In Sect. 4 we test the EOS by fitting it to experimental data of ɛ -Fe and comparing it to the Tillotson EOS, and we also take a look at how well the EOS compares to the multiphase ANEOS model, in which we focus on the behavior of silicate material (SiO 2 , Mg 2 SiO 4 ). In Sect. 5 we discuss the EOS and our different results and finally end with some conclusions. 2 Adding a temperature dependence We begin by separating the Helmholtz free energy into three separate parts ( Zel’dovich Raizer 1967
Paper: 2020ApJ...892L..35B (fulltext)
effect, we find that the inclination must be i 58°. Hence, both light curve modeling and analytic estimates require i ≲ 50°otherwise we would see a reflection effect. 3.1. Joint Analysis We now perform a joint analysis that considers our spectroscopic, astrometric, and photometric constraints simultaneously. Our approach is to construct composite binary models by adding two synthetic WD spectra, properly weighted by their respective radii using the astrometric parallax constraint. We then simultaneously fit the spectroscopic Balmer line profiles and the broadband photometric measurements using the Levenberg–Marquardt algorithm (Bédard et al. 2017 ; Kilic et al. 2020 ). We use de-reddened Galaxy Evolution Explorer far-ultraviolet (FUV) and near-ultraviolet (NUV; Martin et al. 2005 ), SDSS ugriz (Abolfathi et al. 2018 ), Pan-STARRS grizy (Tonry et al. 2012 ), and UKIDSS YJ photometry (Lawrence et al. 2007 ). All four atmospheric parameters (, , , and ) are allowed to vary for a given distance. Figure 3 plots the best-fit solution: a 19,000 K DA WD plus a 8000 K DC WD, both with log g = 7. We note that a DA + DA solution yields comparable parameters, but predicts a strong double-lined H α feature that is not observed. The errors in the joint analysis are dominated by the parallax uncertainty. We estimate 1 σ errors of 1000 K in and 0.15 dex in for the DA WD, and 1500 K in and 0.25 dex in for the DC WD. Figure 3. Joint fit to the spectrum (left panel) and photometry (right panel) for the best DA + DC model. The left panel shows the synthetic model (red) overplotted on the observed MMT spectrum (black). The right panel shows the synthetic fluxes (filled circles) and observed fluxes (error bars). The red and blue lines show the contribution of each WD to the total monochromatic model flux, displayed as the black dotted line. We interpolate the atmospheric parameters through the Althaus et al. ( 2013 ) He-core WD tracks to estimate the masses. Conservatively ignoring the co-variance between and , we infer M ⊙ and M ⊙ . Our mass estimates imply that the cool WD may be less massive than the hot WD. The same result is seen in ZTF J1539 + 5027 (Burdge et al. 2019a ). This would be surprising because one expects the cool WD to have evolved first, and thus be more massive than the hot WD. However the models contain a major uncertainty linked to common envelope ejection (Li et al. 2019 , 2020 ). The thickness of the WD hydrogen envelope, assumptions about elemental diffusion and rotational mixing, the presence or absence of hydrogen shell flashes, and other issues have significant effects on the temperature, radius, and cooling age of a low-mass WD (Althaus et al. 2013 ; Istrate et al. 2016 ). To draw a significant conclusion about the WD masses and their past evolution, we need to improve our constraints, i.e., with future GW measurements. The present constraints are also consistent with J2322 + 0509 being an equal-mass binary. Given the mass
Paper: 2021MNRAS.505.4847H (fulltext)
the lowest error but with the longest training time, whereas the ERT can be seen to reach a very similar error much faster. 4 RESULTS The results given in Table 2 show how the seven ML algorithms performed at producing photometric redshift estimations. Furthermore, Fig. 6 displays the true spectroscopic redshifts plotted against the photometric redshift estimates for each ML algorithm. We also plotted the distributions of the redshift estimations for each of the algorithms as well as the true spectroscopic redshift in a violin plot in Fig. 7 to quickly see which algorithms were able to capture the correct distribution. Figure 6. Graphs of photometric redshift estimates against the true spectroscopic redshift where the lighter shaded contours display the more densely populated regions. From top left to bottom right – Linear Regression (LR), k -Nearest Neighbours (kNN), Multilayer Perceptron (MLP), Decision Tree (DT), Boosted Decision Tree (BDT), Random Forest (RF), and Extremely Randomized Trees (ERT). Figure 7. Violin plots showing the kernel density estimation of the underlying distributions of photometric redshift estimates of each algorithm along with the true spectroscopic redshift. From left to right – True spectroscopic redshift (zspec), Linear Regression (LR), k -Nearest Neighbours (kNN), Decision Tree (DT), Boosted Decision Tree (BDT), Random Forest (RF), Extremely Randomized Trees (ERT), and Multilayer Perceptron (MLP). From these results, we saw that all algorithms were able to successfully provide photometric redshift estimations. Using the violin plots from Fig. 7 , we could see that the rough distribution was recovered by each algorithm, with the Multilayer Perceptron (MLP) producing a slightly more similar shape to the true redshifts. However, from simply looking at the outputs shown in Figs 6 and 7 , it would be very difficult to determine which algorithm would be best to use. While the Decision Tree (DT) might be excluded due to the estimates being put into bands at set redshifts, its errors were still found to be quite low and it outperformed both the Linear Regression (LR) and MLP algorithms. Looking at the metrics in Table 2 alone, the Random Forest (RF) performed best having the lowest errors with an MAE = 0.0385 and MSE = 0.0042; however, the other algorithms k -Nearest Neighbours (kNN), Boosted Decision Tree (BDT), and Extremely Randomized Trees (ERT) all performed incredibly similarly with MAE 0.042 and MSE 0.0046. Indeed, the BDT was almost identically performing to the RF with a slightly improved precision and outlier fraction, and with such close performances of all the other algorithms, it was impossible to sufficiently determine which algorithm would be the most useful. To be able to further differentiate between them and determine which would be the best algorithm to use, it was therefore necessary to use the benchmarking results. The results of the benchmarking performed for each algorithm are plotted in Fig. 1
Paper: 2019MNRAS.486.3415L (fulltext)
loud (GL) sample]. The RoboPol polarimeter allows for the simultaneous measurement of the Stokes q and u vectors without any moving parts. Each source on the sky is projected four times (four spots in a cross-like pattern) on the CCD camera each having its polarization orientation rotated by 67.5°. This allows for measurement of the polarization degree and EVPA of a source in a single exposure. For enhanced sensitivity and to block unwanted light, a mask is placed in the centre of the CCD, the location where the source of interest is typically positioned. A consequence of the RoboPol design is that sources outside the mask could have one (or more) of their spots overlap with a neighbouring source preventing us from confidently measuring either source’s polarization parameters (see fig. 6 in Panopoulou et al. 2015 ), especially in crowded fields. For every blazar observed with the RoboPol polarimeter, we use the automated reduction pipeline to extract the polarization degree for the sources in the 13 arcmin ×13 arcmin field of view of the RoboPol instrument excluding sources affected by such overlap (for details on the RoboPol pipeline and reduction process, see King et al. 2014 ; Panopoulou et al. 2015 ). This process yielded polarization measurements for 5837 sources. Additionally, we include 6379 sources from the Heiles ( 2000 ) catalogue of polarized stars residing above and below the galactic plane (| b | > 10° and | b | −10°) resulting in a total of 12 216 sources [hereafter γ-ray quiet (GQ) sample]. Fig. 1 shows the median polarization degree of the GL and GQ samples. Clearly, the GL sources show systematically higher polarization due to the synchrotron nature of their emission as opposed to the, most likely, dust-induced polarization of the emission in GQ sources. Figure 1. Optical polarization degree distribution for the γ-ray loud and γ-ray quiet samples. We use the Wide-field Infrared Survey Explorer ( WISE ) all-sky survey 4 and the Two Micron All Sky Survey (2MASS, Skrutskie et al. 2006 to obtain the 3.4, 4.6, and 12 μm, and H -band magnitudes for all the sources in the GL and GQ samples. The WISE colours have been successfully used in the past to find blazars among the unidentified Fermi sources (e.g. Massaro et al. 2011 ). Fig. 2 shows the WISE colour–colour diagram along with the so-called blazar strip found in Massaro et al. ( 2011 , 2012 ). While there is some minor overlap, the GL sample occupies a very distinct area in the infrared colour–colour diagram. We also estimate the H -band–4.6 μm colour. In Majewski, Zasowski Nidever ( 2011 ) stars were shown to have a narrow distribution. The GQ sample (which is mostly comprised of stars) shows a similar narrow distribution, whereas GL sources show a wider distribution shifted to higher values (Fig. 3 ). In addition, stars and nearby galactic sources could have measurable proper motion while we do not expect any significant proper motion in distant extragalactic sources (i.e.