adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020A&A...633A..44G (fulltext)
B ). The trend is still visible with a lower slope 0.23 ms −1 d −1 . We present the RV and corrected RV of HD 186704A in Fig. 3 . The difference of one order of magnitude on the slope of the trend between Nidever et al. (2002) observations and ours can be explained by the fact that our observations were made when the SB was closer to the periastron or apoastron of its orbit. 3.1.6 HD 181321 We confirm that HD 181321 is an SB. Nordström et al. (2004) report a variation of 2.3 km s −1 over 9 yr, Guenther Esposito (2007) report a trend with a slope of 1.4 km s −1 yr −1 . Our observations spread on 3757 days (10.3 yr) cover at least two orbital periods. The star is active and show BVS variations with a period of 2−2.5 days that should correspond to the rotational period of the star. We use yorbit to fit the RV with two Keplerian models, one to fit the stellar activity variation and an another to fit the binary variations. We find two possible solutions for the companion, one with a period of 1600 days (2.7 au) and a minimum mass of 0.1 M ⊙ , and a second with a period of 3200 days (4.4 au) and a minimum mass of 0.18 M ⊙ . In both cases, the eccentricity is ~0.5. We present those two solutions in Fig. 4 . More data are needed in order to distinguish between those two solutions. Fig. 4 Solutions of HD 181321 RV fit by the sum of two Keplerian ( top ) and their residuals ( bottom ). Left : 1600 days solution. Right : 3200 days solution. 3.1.7 HD 206893 HD 206893 is a F5V star that hosts a directly imaged BD companion at a separation of 270 mas ( Milli et al. 2017 ; Delorme et al. 2017 ). We recently reported in Grandjean et al. (2019) a long-term trend in the star RV coupled with pulsations with periods slightly less than one day. We performed an MCMC on both the RV trend, imaging data, and H IPPARCOS - Gaia proper motion measurements to constrain the orbit and dynamical mass of the BD. We concluded that the trend can not be attributed to the BD as it leads to dynamical masses incompatible with the object’s spectra. The presence of an inner companion that contributes significantly to the RV trend is suggested with a mass of 15 M Jup , and a period between 1.6 and 4 yr. 3.1.8 HD 217987 HD 217987 is a M 2V, high proper motion star and exhibits a long-term RV trend induced by its secular acceleration. We therefore corrected the RV from the star secular acceleration in its proper motion we deduced from its paralax and proper motion reported in Gaia Collaboration (2018) (see Fig. 5 ). The corrected RV presents short period variations. The RV and BVS are correlated which indicate that the signal might be dominated by spots or plages (see Fig. 5 ). We observe a long-term variation in log , FWHM, and RV which are better fitted by a second degree polynomial model than with a linear model (see Fig. 5 ). This variation could be then attributed to a magnetic cycle of the star with a period greater than 5000 days. An analysis on a large number of M dwarf was
Paper: 2017MNRAS.465.1645H (acknowledgments)
Acknowledgments The authors would like to thank Sami Dib for valuable and constructive comments. Also M. Hosseinirad thanks Najmeh Mohammad-Salehi and Maryam Samadi for useful discussions. S. Abbassi acknowledges support from the International Center for Theoretical Physics (ICTP) for a visit through the regular associateship scheme. We also like to thank the anonymous referee for his/her helpful comments which improved the paper.
Paper: 2018MNRAS.479.5335V (acknowledgments)
ACKNOWLEDGEMENTS We thank the anonymous referee for their comments and suggestions that improved the analysis and the presentation of the results. FV and WNB thank NASA XMM–Newton grant 80NSSC18K0487 and the NASA ADAP program for support. BL acknowledges financial support from the National Key RD Program of China grant 2016YFA0400702 and National Natural Science Foundation of China grant 11673010. We warmly thank J. Runnoe and Z. Shang for useful discussion on UV SED shape of quasars.
Paper: 2016MNRAS.462.1012B (acknowledgments)
JRB and CAH were supported by the STFC under the grant ST/L000776/1. DS was supported by an STFC studentship. We would like to thank the anonymous referee for reading and providing constructive feedback to help improve the manuscript.
Paper: 2020A&A...635A..37C (acknowledgments)
Acknowledgements A.C. acknowledges support by CFisUC strategic project (UID/FIS/04564/2019), ENGAGE SKA (POCI-01-0145-FEDER-022217), and PHOBOS (POCI-01-0145-FEDER-029932), funded by COMPETE 2020 and FCT, Portugal. V.B. acknowledges support by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (project Four Aces grant agreement No 724427). J.-B.D. acknowledges support by the Swiss National Science Foundation (SNSF). This work has, in part, been carried out within the framework of the National Centre for Competencein Research PlanetS supported by SNSF.
Paper: 2019MNRAS.483.3255L (fulltext)
from low SNR, individual exposures. At high SNR, we obtain abundances precise to ≈0.01 to and even at low SNR (SNR ≈ 50) we get precisions of ≈0.02 to for most elements. These precisions are confirmed by looking at the scatter in the abundances within open clusters, which we find to be . We also recovered the expected abundance trends in globular clusters, but found that the censoring in the network is crucial to obtain this, because training on the full spectrum for all elements causes the NN to depend too strongly on correlations between elements within the training set and therefore to fail when these correlations are absent, like in globular clusters. We also demonstrated that a large NN can work well with a limited amount of training data, finding barely degraded performance for training sets that only consists of thousands of spectra. The speed and flexibility of NN s mean that they are a highly useful tool for spectroscopic data analysis. They allow the results obtained from a detailed analysis of a small calibration set to be transferred to an entire large data set of millions of spectra in a matter of minutes and can thus be a great aid in the development of the next generation of spectroscopy tools. They could also be of use in earlier stages of the data processing, for example, for homogenizing spectra taken with different instruments (e.g. the northern and southern APOGEE spectrographs) or for removing instrumental systematics such as persistence (e.g. Jahandar et al. 2017 ). Such applications would be easy to pursue with the astroNN software package described in the Appendix. APPENDIX: ASTRONNN: A PYTHON LIBRARY FOR DEEP LEARNING IN ASTRONOMY Data-driven tools are becoming more and more popular in astronomy and deep learning in particular is becoming a popular technique to deal with big data sets. When we started on the research for this paper, we realized that a python package that (i) has a focus on deep learning, (ii) contains tools and data sets that are relevant to astronomers, (iii) is easy to use and well tested against errors, and (iv) acts as a platform to share astronomy-oriented NN s is needed to advance research in this area. astroNN is a python package for deep learning in astronomy designed to fulfill the points stated above. astroNN relies heavily on Tensorflow (Abadi et al. 2016 ), a standard deep learning python library, and is easy to set up on all common platforms. astroNN employs custom methods for storing and sharing models: models are saved in a folder and consist of the NN itself in HDF5 format, the training history in CSV format, model parameters, and the names of output neurons as well as normalization parameters in JSON format. Users simply have to give the reduced data as numpy array(s) to astroNN and get outputs back as numpy array(s); normalization of the inputs and outputs is handled internally. At this point, astroNN as used in this paper consists of about 8300 lines of code in the modules, 1200
Paper: 2017MNRAS.469.1809R (acknowledgments)
Acknowledgments We thank Volker Springel for allowing the use of his code pgadget3 and the anonymous referee for their suggestions that improved the presentation of our results. We acknowledge support by the Deutsche Forschungsgemeinschaft through the SFB 956, The Conditions and Impact of Star Formation and the TRR 33 The Dark Universe. MB thanks the Bonn-Cologne Graduate School for Physics and Astronomy for support. The authors acknowledge that the results of this research have been achieved using the PRACE-3IP project (FP7 RI-312763), using the computing resources (Cartesius) at SURF/SARA, the Netherlands.
Paper: 2018MNRAS.474..102A (fulltext)
the region. They found that for the region of interest studied here, mostly located outside the Galactic plane, the difference between the standard LAT diffuse model and their refined model is negligible. However, a more complete analysis of the effect of the uncertainties in the background emission model was carried out following Acero et al. ( 2016b ), who used a set of 8 alternative models 3 developed in Ackermann et al. ( 2012 ) by changing some crucial parameters that determine the intensity and distribution of the Galactic gamma-ray emission: the cosmic ray source distribution, the height of the cosmic ray propagation halo and the HI spin temperature. This is important since the standard diffuse emission models distributed for LAT data analysis are optimized for analysis of point sources and compact extended sources (Acero et al. 2016a ), and the analysis must be validated using different models. The estimation of the systematic errors was done as in Acero et al. ( 2016b ) by comparing the source parameters obtained with each alternative model and those with the standard model. The significant detection of G350.6-4.7 (above 15σ for all alternative models considered) gives confidence that the emission is indeed associated to a new extended high-energy source. Systematic errors related to the imperfect knowledge of the effective area were estimated following the LAT team recommendations. 4 Since this uncertainty and the resulting uncertainty from the background model are independent, the total systematic error was obtained by adding them in quadrature. 3 DISCUSSION It is reasonable to attribute the origin of the gamma-rays to the shell of a previously unknown SNR. If located at the distance to the pulsar PSR J1747-4036 (3.4 kpc), the object size would be around 200 pc and the centre of the disc would be located around 250 pc from the plane of the Galaxy. SNRs with diameters of the order of a few degrees are known and a significant number have been seen as extended objects by the LAT (Acero et al. 2016b ). Well-known examples are the Cygnus Loop (Katagiri et al. 2011 ) and RX J0852.0-4622 (Tanaka et al. 2011 ). However, not many SNRs are known to have a diameter greater than 100 pc, although large objects could not have detected due to observational selection effects (Green 1991 ). Dynamic considerations of the expansion and evolution of an SNR can give a physical justification for an upper limit in their size (e.g. Cioffi, McKee Bertschinger 1988 ; Slavin Cox 1993 ). It is possible then that if this new source is an SNR it is much closer than 3.4 kpc and hence unrelated to PSR J1747-4036. This pulsar is classified as a millisecond pulsar (e.g. Abdo et al. 2013 ), and thus if related to the G350.6-4.7, the SNR would also be too old to show a GeV spectrum as the one found here (see e.g. Acero et al. 2016b ). The Cygnus Loop SNR is located at about 540 pc (Blair, Sankrit Raymond 2005 ) and has a diameter of ∼35 pc. If the same physical
Paper: 2017ApJ...844...20M (acknowledgments)
I gratefully thank George M. Fuller, Yang Sun, and Surja K. Ghorui for fruitful discussions. I also owe gratitude to Projjwal Banerjee, Alice Shih, and Joe Semmelrock for their input in writing this manuscript. This research at Shanghai Jiao Tong University is supported by the National Natural Science Foundation of China (No. 11575112), by the National Key Program for ST Research and Development (No. 2016YFA0400501), and by the 973 Program of China (No. 2013CB834401).
Paper: 2018MNRAS.474.3857C (fulltext)
a population of stars is present, and some studies suggest they may be somewhat common (Lauer et al. 2005 ) – further increases the TDE rate. 4.4 Other sources of emission: winds and accretion on to the primary From Fig. 1 , we see that accretion on to the secondary often proceeds at a super-Eddington rate. While most of the time the rate is only mildly super-Eddington, satisfying with N ≃ few, in some cases, and particularly towards the peak in the accretion rate, N can approach and even exceed 100. While these hyper-Eddington periods are predicted analytically (Rees 1988 ; Evans Kochanek 1989 ), the peak accretion rates in Fig. 1 can actually exceed the analytical one (shown by the dashed curve in the top-left panel in this figure), which is a consequence of self-gravity (Coughlin Nixon 2015 ) and the non-zero energy of the stellar centre of mass. In these highly supercritical regimes, one might expect winds (Strubbe Quataert 2009 ; Metzger Stone 2016 ) or, in the more extreme cases, jets (Giannios Metzger 2011 ; Coughlin Begelman 2014 ; Tchekhovskoy et al. 2014 ; Coughlin Begelman 2015 ) to be launched from the vicinity of the accreting SMBH. From these outflows one would expect an additional source of radiation, with properties distinct from the disc. While it is unlikely that this scenario explains the rebrightening seen in ASASSN-15lh – the accretion rate would have been super-Eddington prior to the increase, and the constancy of the X-ray emission (Margutti et al. 2017 ) suggests that there was no change in the source of harder photons – changes in the luminosity induced by a sudden super-Eddington surge of accretion would be possible in other systems. The disk feeding the SMBH should also become geometrically thicker once the Eddington limit is surpassed, and could perhaps inflate to a quasi-spherical envelope once L ≫ L Edd (Loeb Ulmer 1997 ; Coughlin Begelman 2014 ). The simulations performed here would not capture the important physical interactions between the radiation and the plasma that would lead to such an inflated disc structure (see Sa̧dowski et al. 2016 , who do find quasi-spherical envelopes from their simulations of TDEs). Nevertheless, if the photosphere of the disk coincides with the photon trapping radius (Begelman 1979 ), then we can estimate the size of the envelope as (equation 17 of Coughlin Begelman 2014 ) where in the final line we set the opacity equal to the value for electron scattering, κ = κ es = 0.34 g cm −2 . Interestingly, this radius is of the order of the Hill sphere of the secondary for the binaries considered here; thus, the discs from super-Eddington TDEs in tight binaries could fill their Roche Lobes, resulting in accretion on to the primary. This accretion would then provide an additional source of radiation, and it would also modify the structure of the envelope around the secondary and, conceivably, its accretion rate. While it did not occur in any of our simulations, one can also imagine
Paper: 2019ApJ...874...23D (fulltext)
and the horizontal axis shows the log of the effective temperature T eff . Each square represents one model. From left to right, the panels are evaluated for m 0 = 10 3 , 10 4 , and 10 5 , respectively. The dashed diagonal lines represent curves of constant Eddington ratio ( from left to right) and horizontal dashed curves show lines of constant mass ( from top to bottom) evaluated for the hottest annulus in the disk model. The white dashed curve in the left panel shows the variation as a function of radius for a relativistic α -disk model with a = 0, , and . The thick black solid curve corresponds to an analytical estimate (Equation ( 6 )) of where the annulus becomes photon starved. Note that we cap the color bar at f = 4 to better show the variation at lower values of f , but several of the hardest spectral models exceed this value. The black dashed lines show the values of Q and T eff computed at the radius where T eff reaches its maximum value in a relativistic accretion disk model for various values of the accretion rate and mass for a nonspinning black hole (Novikov Thorne 1973 ; Shakura Sunyaev 1973 ). For black hole spin a = 0, R max = 9.5 r g , where is the gravitational radius and G is Newton’s constant. We can then compute T eff , Q , and m 0 in the radiation pressure dominated limit via Here is the accretion rate scaled to , is the electron scattering opacity, is the Stefan–Boltzmann constant, η is the spin dependent radiative efficiency, α is the stress prescription parameter and . T rel , Q rel , and are relativistic correction factors (Novikov Thorne 1973 ). The diagonal lines in each panel correspond (from left to right) to , 0.1, and 1. The horizontal lines correspond (from top to bottom) to . Since the spectral hardening factor of a global disk model correlates closely with the value of f in the hottest annulus, this provides a good estimate of how f will vary for a global disk. Increasing the black hole spin shifts the diagonal lines to the right (i.e., toward higher T eff for a given M and ). In the left panel, we also show the variation of T eff and Q with radius in an accretion disk for and as a white, dashed curve. This shows that for a single disk model, f is generally largest at the radius with the highest T eff . Figure 4 shows the same quantities, but for lower values of m 0 . Here the ranges of T eff and Q with valid spectral models are narrower due to difficulties with convergence at these lower surface densities. The diagonal and horizontal dashed lines have the same meaning as in Figure 3 , but only cover masses of and 1000. Comparison with Figure 3 shows an accelerating trend for f to increase with decreasing m 0 . Equation ( 5 ) shows that the value of m 0 in the α -disk model is a function radius, mass, spin, and accretion rate. It is also sensitive to the details of the assumed stress prescription (Done Davis 2008 ) and thus likely to be the least robustly estimated parameter. Figure 4. Table of best-fit
Paper: 2016MNRAS.455..199D (acknowledgments)
The author would like to acknowledge research support from the Australian Research Council through Discovery Grant programmes DP120101237 and DP150103294. He is also grateful for the support received during an extended visit to the Institute for Astronomy, University of Edinburgh during which this paper was largely written. Thanks also to Ken Freeman, Russell Cannon and Elizabeth Maunder who conducted the 2010 December observations, and to Andrew Cole for providing a copy of the line profile fitting code (whose heritage can be traced back to that of Armandroff Da Costa ‘plus ça change, plus c'est la même chose’). This research has made use of the WEBDA data base, operated at the Institute for Astronomy of the University of Vienna, and of the VizieR catalogue access tool, CDS, Strasbourg, France. The original description of the VizieR service was published in AAS, 143, 23.
Paper: 2017ApJ...835L..27L (fulltext)
UFD1. There are 12 resolved stars brighter than I = 28.5 mag in the net CMDs (Figures 2 (c) and (d)). The number of field contaminants with I 28.5 mag in the area covered by the UFD field is estimated to be 4.8 ± 0.3, while there are 15 stars in the area of UFD. Thus, the net number of the member stars in the area of the UFD is estimated to be 10 ± 4, which is consistent with the number derived after CMD decontamination. Eight of the 12 stars have a narrow range of colors with , showing a vertical sequence, while three of them show much redder colors than the others. Therefore, these eight stars are considered to show the RGB of Fornax UFD1, while three redder stars are probably foreground or background sources. Because of the small sample of the RGB stars in the CMD, it is not easy to derive an accurate distance to Fornax UFD1. We estimate approximately the distance to this UFD, assuming that the mean magnitude of the two brightest blue RGB stars in the CMD corresponds to the magnitude of the TRGB. The mean magnitude and color of these two stars are I = 27.38 ±0.14 mag and . This magnitude value is 0.24 mag fainter than the value of Virgo UFD1, I = 27.14 ±0.04 mag (Jang Lee 2014 ; see the lower panels in Figure 2 ). Applying the TRGB calibration in Jang Lee ( 2017 ) to this TRGB magnitude, we derive a value for the distance to Fornax UFD1, d = 19.0 ± 1.3 Mpc (). This value is consistent with the distance to NGC 1316. This shows that Fornax UFD1 is likely to belong to Fornax. In Figure 2 , we overlaid 12 Gyr isochrones for[ α /Fe] = 0.2 (Fabrizio et al. 2015 ) and [Fe/H] = −2.4 to −0.4 in the Dartmouth model (Dotter et al. 2008 ), which were shifted according to the distance and foreground reddening for Fornax UFD1. Although the number of stars is small, the RGB of UFD1 is, on average, bluer and narrower compared with the RGB of NGC 1316 in the control field. This shows that the mean metallicity of this new UFD is much lower than that of the NGC 1316 halo. The CMD of these RGB stars is matched roughly by the RGB part of the isochrones for low metallicity. It is noted that the RGB of Fornax UFD1 is even bluer than the RGB of the lowest metallicity with [Fe/H] ≈ −2.4. We estimate roughly the mean metallicity of the RGB stars to be [Fe/H] ≈ −2.4 ± 0.4. 3.3. Basic Parameters of Fornax UFD1 We constructed radial profiles of surface brightness and cumulative integrated magnitude of Fornax UFD1 using IRAF/ELLIPSE, as plotted in Figures 3 (a) and (b). We masked out cosmic rays and background galaxies in the original image and ran the ELLIPSE task adopting a smoothing option of 3 × 3 pixel binning. The radial profiles of surface brightness in Figure 3 show a flat core in the inner region at r 1′′ and decline rapidly in the outer region, showing that this UFD is a dynamically relaxed system like typical old star clusters. The radial profiles of surface brightness for r 4′′ are fitted well by a Sérsic law (Sérsic 1963 ) with an index n = 0.54 ± 0.06 for
Paper: 2018ApJ...860..132Y (acknowledgments)
The author thanks the anonymous referee for helpful comments that helped improve this work, A. P. Milone for providing the observed data of NGC 1866, and Daniel Kister for help in improving the English, and also acknowledges the support from the NSFC 11773005, U1631236, and 11273012.
Paper: 2018MNRAS.473.2355S (acknowledgments)
Acknowledgements We thank Catherine Heymans, Joe Zuntz, Linda Blot, Tom Kitching, Matthias Bartelmann and Shahab Joudaki for support, provision of simulation products and answering detailed questions. We thank an anonymous referee for peer review. ES is funded by a DAAD research fellowship of the German Academic Exchange Service. Cosmosis can be found at https://bitbucket.org/joezuntz/cosmosis/wiki/Home . Computations for the [Clone] N -body simulations were performed on the TCS supercomputer at the SciNet HPC Consortium. SciNet is funded by the Canada Foundation for Innovation under the auspices of Compute Canada; the Government of Ontario; Ontario Research Fund – Research Excellence; and the University of Toronto.