adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018MNRAS.476.5075S (fulltext)
h is the Planck constant, k is the Boltzmann constant, T d is effective dust temperature, and β is the emissivity index, which determines the steepness of the Rayleigh–Jeans slope of the spectrum. The python implementation emcee (Foreman-Mackey et al. 2013 ) was used to build a Markov Chain Monte Carlo (MCMC) analysis of the fitted SED for each data set, allowing the dust temperature and normalization as free parameters to sample the posterior probability distribution of the model. Where possible, we also leave β as a free parameter in the model, allowing for a test of the range of dust emissivities that are consistent with the data. However, fitting for β requires at least four data points to constrain the peak and Rayleigh–Jeans slope of the modified blackbody function. For many sources, our Herschel /SPIRE data are the only measurement in the FIR–submillimetre regime. Thus, we assume a value of β = 1.5 for these sources, as is frequently applied in the literature (e.g. Magnelli et al. 2012 ). Various combinations of T d and β have been found for samples of high-redshift quasars. For example, Priddey McMahon ( 2001 ) find an average of T d = 41 ± 5 K and β = 1.95 ± 0.3, whereas Beelen et al. ( 2006 ) find an average of T d = 47 ± 3 K and β = 1.6 ± 0.1 for their sample. For the sources that were not detected, or had only one detection to constrain the fit, we assume the median fitted dust temperature of the sample (38 K, see Fig. 6 ) and β = 1.5, and fit only for the normalization. For these sources with only one detection, we fit the 16th and 84th percentile values of the median fitted temperatures to estimate our errors on the FIR luminosity. As β is highly correlated with T d , errors on the derived properties of sources without β-fitting may be underestimated. However, the FIR luminosity is not strongly affected by our assumptions due to the joint dependency of T d , β and normalization, so this will not have a significant effect on the inferred values of L FIR or SFR. This is unsurprising, as the luminosity is derived by the integral of the fit defined by the data points. Figure 6. Histogram of effective dust temperatures for 53 quasars in the sample with temperature fitting, excluding those without a known redshift and synchrotron-dominated sources. The median dust temperature is K. Where two dust temperatures are fit, only the colder component is included here. For the purpose of spectral fitting, the ten sources without a known redshift are assumed to have z = 1.8, equivalent to the median redshift of the sample. The choice of redshift significantly affects the luminosity distance; thus, these objects are not included in the overall statistics. For three sources (APM 08279+5255, H1413+117, and iras F10214+4724), there are sufficient data in the mid-IR (MIR) to motivate fitting a two-temperature dust model. Table 3 shows the number of sources fitted with each combination of spectral parameters. Table 3. Number of sources fitted with
Paper: 2016MNRAS.461.3274C (acknowledgments)
We thank an anonymous referee for useful comments that helped to improve the paper. We are grateful to Ted Bergin for the use of his non-equilibrium code. We benefited from many discussions with him and his (former) graduate student Ilse Cleeves on astrochemical codes and analysis. Additionally, we thank Til Birnstiel, Roy van Boekel, Cornelis Dullemond, Thomas Henning, Hubert Klahr, Oliver Gressel and Dmitry Semenov for interesting discussions and feedback on this work. The work made use of the Shared Hierarchical Academic Research Computing Network (SHARCNET: www.sharcnet.ca ) and Compute/Calcul Canada. AJC acknowledges funding from the National Sciences and engineering Research Council (NSERC) through the Alexander Graham Bell CGS/PGS Doctoral Scholarship. REP is supported by an NSERC Discovery Grant. MA acknowledges funding by an Ontario Graduate Scholarship (OGS) and NSERC CGS-M scholarship. REP also thanks the MPIA and the Institut fur Theoretische Astrophysik (ITA) in the Zentrum fur Astronomie Heidelberg for support during his sabbatical leave (2015/16) during the final stages of this project. AJC also thanks MPIA and the Institut fur Theoretische Astrophysik (ITA) in the Zentrum fur Astronomie Heidelberg for their hospitality during his 1 month stay in 2016.
Paper: 2020ApJ...893...67M (fulltext)
(1) perturb the previous step to propose a new set of parameters θ * drawn from the prior; (2) use fleck to compute the smoothed-amplitude distribution for a large sample of stars, (3) for use as a summary statistic, we compute the two-sample Anderson–Darling statistic A 2 for comparing how close the trial smoothed-amplitude distribution is to the observed one (Anderson Darling 1952 ; Scholz Stephens 1987 , see discussion in Appendix 6); (4) if , we accept the proposed step and add it to our chain; (5) go back to step (1), and repeat. We select , not far from the minimum value of the Anderson–Darling statistic . The approximate posterior distributions produced by ABC should approach the true posterior distributions in the limit that , provided that the Anderson–Darling statistic is a sufficient statistic. In practice it is difficult to prove that a statistic is sufficient, so we note that the posterior distributions shown here are valid given the hypothesis that the Anderson–Darling statistic is a sufficient one. The posterior distributions from the ABC analysis illustrate the three-way degeneracy between starspot latitudes, radii, and contrasts for stars with unknown inclinations. For a fixed number of spots, small spots spread randomly over all latitudes generate rotational modulations similar to larger spots concentrated near the poles. Similarly, small spots with extreme intensity contrasts () reproduce similar rotational modulations to larger spots with less extreme spot contrasts (). This exercise is a demonstration of why it is so difficult to invert light curves of rotational modulation and recover unique starspot properties—a wide range of spot parameters can produce similar light curves. Figure 6 shows the simulated smoothed-amplitude distributions (gray histograms) with spot parameters drawn randomly from the posterior distributions, compared with the observed smoothed-amplitude distribution for stars in each association (colored histograms). This figure illustrates how the ABC algorithm minimized the Anderson–Darling statistic between the simulated and observed smoothed-amplitude distributions. Most simulated and observed distributions are statistically indistinguishable according to the Anderson–Darling statistic. Figure 6. Observed smoothed light-curve amplitude distributions (colored histograms) compared with simulated smoothed-amplitude distributions (gray) drawn from the posterior distributions for the spot parameters from the ABC technique with fleck. The goal of the ABC technique is to search for starspot parameters that produce simulated smoothed-amplitude distributions that are statistically indistinguishable from the observations. The poorest “fit” is M67, the oldest cluster, for which the simulations produce a more strongly peaked smoothed-amplitude distribution near 0.5%. The lack of a peak in the observed smoothed-amplitude distribution for M67 is likely real; observational bias in favor of detecting large
Paper: 2017ApJ...836L..21N (fulltext)
driven by neutrino heating, where the MRI significantly enhances the convection and angular momentum transport (Sawai Yamada 2014 ). Although neutrino heating plays an important role, neutron-rich matter is ejected especially in polar directions with a smaller influence of -capture on neutrons due to shorter expansion/explosion timescales. We compute additional models to investigate the effect of magnetic fields in explosion dynamics. However, simulating MRI-driven explosion models with different initial magnetic fields requires a huge amount of computer resources because the saturation of the MRI is strongly depending on the magnetic fields, i.e., all explosion models have different criteria for numerical convergence (see Figure 6 of Sawai Yamada 2016 ). In the current study, therefore, we adopt a more simplified parametric method based on the above MRI-driven explosion model (shown in Figure 1 ), whose numerical convergence has been confirmed. 8 8 We confirmed that a higher-resolution model, of which the finest grid is 30 m, shows the same result of r-process nucleosynthesis (plotted in Figure 4 ) as well as explosion dynamics. Instead of changing magnetic fields, we vary the time evolution of neutrino luminosity by multiplying a scale factor as , 0.20, 0.30, 0.40, 0.50, 0.60, 0.75, and 1.25. We suppose that explosion models with a lower are dominated by magnetic fields rather than neutrino-heating. To evaluate the strength of magnetic fields in jets, we consider the minimum value of the ratio of gas to magnetic pressure (), i.e., Since the has a large fluctuation, we calculate the mean value denoted by during jet propagation (from jet launching to the time the shock front reaches 2000 km). A lower is obtained in models with lower , where , 0.038, 0.047, 0.060, 0.071, 0.083, 0.15, 0.20, and 0.30 in ascending order of . We expect neutron-rich material to be mostly ejected in magnetically driven jets rather than heating-driven ejecta. In the jet direction, neutrino emission can be weakened due to lower mass accretion onto the proto-NS. Adopting a lower , therefore, is consistent with decreasing in the polar direction. We label the standard MRI-driven explosion model with as the h -model, indicating neutrino heating is dominant, while the case of the magnetically driven jet with is named as the m -model. We choose the case of as a typical intermediate explosion model as the i -model. As the behavior of the explosion varies gradually depending on , the distinction of models has uncertainties, e.g., the m -model can be categorized in the i -model as an extreme case. We adopt and 0.75 models as a variation of the i -model, denoted by i -model(−) and i -model(+), respectively. The h -model(+) and m -model(+), based on and 0.40, respectively, are also referred to in order to discuss the impact of neutrino absorption. The , entropy ( S ), and for the selected models are shown in Figure 2 , when the shock front reaches ∼1000 km. For the m -model, we
Paper: 2018A&A...617A..58C (acknowledgments)
This work was granted access to the HPC resources of CINES under the allocations x2016046955 and A0020406955 made by GENCI. The work of S.C. has been supported by the ERC Project No. 267117 (DARK) hosted by Université Pierre et Marie Curie (UPMC) – Paris 6 and the ERC Project No. 614199 (BLACK) at Centre National De La Recherche Scientifique (CNRS). AB acknowledges support from NSERC (Canada) through the Discovery Grant program, from Institut Lagrange de Paris (ILP), and the Pauli Center for Theoretical Studies ETH UZH. S.C. is also grateful to the University of Zurich’s Institute for Computational Sciences’s Center for Theoretical Astrophysics and Cosmology and the Institut d’Astrophysique de Paris for hospitality during his recent sabbatical visit. S.C. thanks Ewan O’Sullivan for the precious advise on the production of realistic X-ray images.
Paper: 2017A&A...608A.103Z (fulltext)
al. 2014 ). BRITE-Constellation obtained observations of the Cygnus-II field from June 1 to November 25, 2015. The star 43 Cygni was observed for only 13 days with BRITE-Lem (BLb) in the blue filter and for 156 days with BRITE-Toronto (BTr) in the red filter, both in chopping mode, where the position of the target star within the CCD plane is constantly alternated between two positions about 20 pixels apart on the CCD. This procedure was adopted to mitigate the impact of high dark current in CCDs. A detailed description of this technique is given in Popowicz et al. (2017) . As the blue data set from BLb did not have sufficient quality for an asteroseismic analysis, we omitted it from our further investigation. Fig. 1 Complete BRITE-Toronto light curve of 43 Cygni ( top ) and zoom into a 20-day subset ( bottom ). The BRITE-Constellation data of 43 Cyg were corrected for instrumental effects according to the iterative procedure described by Pigulski et al. (2016) . This procedure was, however, modified to include two-dimensional decorrelations. Since 43 Cyg shows variability with large amplitude, decorrelations were made using residuals from a fit consisting of a sum of sinusoidal terms. For 43 Cyg, the model consisted of five terms: four modes with frequencies 1.250 d, 1.288 d, 0.963 d, and 1.516 d, plus a combination frequency (1.250 d0.963 d = 0.287 d). This model was used for all BTr setups. The fit was recalculated at each step of the decorrelation. The whole procedure included outlier rejection, 1D and 2D decorrelations, and removal of corrupted orbits (e.g. affected by poor stability of the satellite). Decorrelations, both 1D and 2D, were made using parameters provided in Data Release 3 (see Popowicz et al. 2017 , or the BRITE Public Data Archive 2 for the explanation of parameters) and a given satellite orbital phase as an additional parameter. The decorrelation was made sequentially, allowing for multiple decorrelations with the same parameter (or a pair of parameters). At each step, the parameter showing the strongest correlation was chosen for correction. The strength of a correlation was defined as the degree of reduction in variance due to decorrelation with a given parameter (for 1D) or a pair of parameters (for 2D). The iterations were stopped when all correlations (both 1D and 2D) resulted in a variance reduction smaller than 0.05 per cent. The whole procedure was run independently for each setup. Outliers and the worst orbits were rejected at least twice during the whole procedure. After decorrelating the data, the blue and red data were separately combined, taking the mean magnitude offsets between the setups into account. The complete BTr light curve of 43 Cygni consists of 98 481 data points and is shown in the top panel of Fig. 1 , while the bottom panel shows a zoom into a 20-day subset. 2.2. HERMES spectra We obtained ten high-resolution spectra with high signal-to-noise ratio (S/N) for 43 Cygni using the HERMES
Paper: 2016A&A...594A..26P (fulltext)
defined by the effective beam at the given frequency. The algorithm starts optimization at the MHW2 source location, assuming initially a circular Gaussian source with the FHWM of the PSF. Optimization of all six parameters converges in a reasonable number of iterations, usually less than 1000. Appendix B.2: Error estimation The downhill simplex method does not provide any information on the uncertainties in the optimized parameters. We estimate errors using a Markov Chain Monte Carlo (MCMC) method to sample the posterior distribution. In our case, the burn-in time can be very small since to start the chain the simplex-optimized value of the parameter from the previous step can be used, and it is usually very close to the peak of the distribution. The length of the MCMC chain is set to 1000 samples by default. To obtain the proper sampling in the MCMC chain it is necessary to define the optimal step size in the parameter space. This determination is complex since there are more than 100 000 sources in the catalogue to process; it would be impossible to adjust every MCMC chain, since it would take an excessive amount of time to try several values of the sampling step using an iterative approach. Hence we decided to use an analytical approximation for the errors of most of the Gaussian profile parameters, following Hagen Dereniak (2008) . We used an analytical approximation for the separable 2D Gaussian profile model; the errors are derived for five parameters; namely position coordinates, flux density, and two non-equal width values. The calculated errors were used to find the value of the step size in the parameter space to start the MCMC sampler, which delivered the error on the tilt angle. This combined technique allowed us to avoid most of the problems related to undersampled or oversampled MCMC chains. Appendix B.3: Validation of the method using model data The results of the Gaussian fit were compared with the MHW2 positions and flux densities for a test set of sources with known parameters. The PR2 100 GHz map with injected sources and cold cores was used to check the 2D Gaussian fitting algorithm. The cold cores were modelled as 2D Gaussian profiles with uniformly distributed random orientation angles. The major FWHM was in the range from 4.́5 to 19, and the ellipticity parameter, , varied from 1 (circular source) to 7 (highly elliptical source). All sources were uniformly distributed across the sky. Plots comparing the flux density values for MHW2 and Gaussian fitting for this test set are shown in Figs. B.1 B.4 . Both methods give almost the same point source flux-density values (Figs. B.1 and B.2 ), but MHW2 gives less scatter for the fainter point sources. For the cold cores, Gaussian fitting works better and gives much less bias, especially for the bright sources (Figs. B.3 and B.4 ). The visible digitization of the flux densities reflects the fact that only integer values were used for the flux densities of injected sources. The
Paper: 2018MNRAS.477.5517W (fulltext)
5 and 6 arcmin from each cluster centroid generated a set of background values distributed symmetrically about the global background computed in Section 4.2 with no evidence of clusters with high MIR aperture flux measurements displaying enhanced local background values. Identification of X-ray faint, MIR bright clusters in Fig. 2 is therefore not influenced by the application of a global background correction to MIR cluster aperture fluxes. It is furthermore unlikely that the non-cluster masking procedure applied to the computation of X-ray aperture fluxes results in the exclusion of bona-fide cluster flux from the aperture measurement. As mentioned previously, XMM–LSS pipeline detections corresponding to non-C1/C2 sources were masked from the aperture measurement. An alternative masking procedure was tested, this time excluding source areas corresponding only to point sources classified as P1 (high significance, low extension), with little or no qualitative difference in the results presented above. We also compare the X-ray and MIR brightness measures of each cluster sample in terms of their luminosity using the measured redshift for each cluster and the assumed cosmological model to convert the measured aperture flux in either the X-ray or IRAC1 band, respectively, to a rest-frame [0.5–2] keV X-ray and K -band stellar luminosities. In addition to the luminosity distance to each source, the correction to luminosity requires the application of a k -correction. For the X-ray brightness, this correction is computed using the same T = 2 keV plasma model used to convert count rates to flux. In the MIR, we employ a k -correction derived from a 1 Gyr, solar metallicity burst of star formation which evolves passively from a formation redshift z f = 10.This model is taken from Willis et al. ( 2013 ) and matches the red-sequence evolution of the XMM–LSS distant cluster sample. Furthermore, as demonstrated by van der Burg et al. ( 2014 ), the stellar mass in SpARCS distant clusters is dominated by M * galaxies located on the red sequence. The evolving spectrum was realized using the galaxev 2003 stellar population synthesis code (Bruzual Charlot 2003 ). Computing theMIRluminosityof a galaxy cluster within a spatial aperture effectively involves integrating the cluster galaxy luminosity function (LF) down to a sensitivity limit which itself may be expected to be a function of redshift. In order to determine whether this is likely to be an important bias in the aperture luminosity values computed for this , we performed the following analysis: taking the 3.6 m flux limit of the optical–MIR catalogue as 3.7 μJy, we computed the corresponding K -band luminosity of a galaxy with this flux over the redshift interval 0.8 z 1.6 (the extent of the SpARCS sample). We next considered this luminosity as the limiting value as a function of redshift to which we integrated the K -band cluster galaxy LF presented by De Propris et al. ( 2007 ). The relative change in
Paper: 2015ApJ...799...19Q (acknowledgments)
We thank Drs. Yu Bai, Yi Hu Song Wang, and Hang Gong for very helpful discussions. J.F.L. acknowledges support from the National Science Foundation of China through NSFC grants 11233004 and 11333004. W.W. is partially supported by NSFC grants 11203035 and 11233004 for this work.
Paper: 2018MNRAS.474.5598R (acknowledgments)
Acknowledgements We thank the engineers and staff at the Mullard Radio Astronomical Observatory for maintenance and operation of AMI, which is supported by Cambridge and Oxford Universities. We are grateful for IT knowledge exchange with the SKA project. The AMI telescope, AMS and TC gratefully acknowledge support from the European Research Council under grant ERC-2012-StG-307215 LODESTONE. We thank our anonymous referee for constructive comments that have improved our paper. CJR acknowledges an OCE Fellowship from CSIRO. IH and MLB are supported by a European Research Council Starting Grant (grant number 280127). MLB is an STFC Advanced/Halliday fellow (grant number ST/I005129/1). YCP acknowledges support from a Trinity College Junior Research Fellowship. This research has made use of data from the OVRO 40-m monitoring program (Richards et al. 2011 ) that is supported in part by NASA grants NNX08AW31G, NNX11A043G, and NNX14AQ89G and NSF grants AST-0808050 and AST-1109911. This work has also made use of the NASA/IPAC Extragalactic Database (NED), operated by JPL under contract with NASA, and NASA's Astrophysics Data System (ADS).
Paper: 2015ApJ...811...95P (acknowledgments)
This work was supported in part by NASA-ADAP grant NNX12AI49G and NSF grant 1411773. This research made use of Astropy 16 16 astropy.org (Astropy Collaboration et al. 2013 ), TOPCAT, 17 17 www.star.bris.ac.uk/∼mbt/topcat (Taylor 2005 ), and STILTS 18 18 www.star.bris.ac.uk/∼mbt/stilts (Taylor 2006 ). All figures made use of matplotlib 19 19 matplotlib.org (Hunter et al. 2007 ). Figures 1 , 3 , 8 , 11 , and 20 were made using densityplot 20 20 github.com/CKrawczyk/densityplot (Krawczyk Peters 2014 ). Special thanks go to Alex Gray for developing the code for the NBC KDE algorithm and to Erica Smith for carefully reviewing the manuscript. Funding for the SDSS and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the U.S. 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 website is http://www.sdss.org/ . Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy Office of Science. The SDSS-III website is http://www.sdss3.org/ .
Paper: 2016A&A...595A..74E (acknowledgments)
T.E., T.K.G., M.J.R. and C.D.W. were visiting astronomers at the InfraRed Telescope Facility, which is operated by the University of Hawaii under Cooperative Agreement no. NNX-08AE38A with the NationalAeronautics and Space Administration, Science Mission Doctorate, Planetary Astronomy Program. We thank the IRTF staff for the support of TEXES observations. Observations with TEXES were supported by NSF Grants NNX-14AG-34G for T.K.G. and AST-0708074 for M.J.R. T.K.G. acknowledges support by NASA Grant NNX14AG34G. T.E. and B.B. acknowledge support from CNRS and Programme National de Planétologie (INSU), and T.F. aknowledges support from UPMC. T.W. acknowledges support from the University of Versailles-St-Quentin and the European Commission Framework Program FP7 under Grant Agreement 606798 (Project EuroVenus). T.E. acknowledges support from the Jet Propulsion Laboratory as a Distinguished Visiting Scientist. We are grateful to Emmanuel Marcq for helpful comments regarding the discussion.