adsabs/WIESP2022-NER huggingface dataset:
Paper: 2015MNRAS.448.3391C (acknowledgments)
This work has made use of the HPC resources of CINES (Jade supercomputer) under the allocation 2013047012 and 2014047012 made by GENCI. Part of the analysis was performed on the DiRAC facility jointly funded by STFC and the Large Facilities Capital Fund of BIS. This work is partially supported by the Spin(e) grants ANR-13-BS05-0005 of the French Agence Nationale de la Recherche and by the ILP LABEX (under reference ANR-10-LABX-63 and ANR-11-IDEX-0004-02). The research of YD has been supported at IAP by ERC project 267117 (DARK) hosted by Université Pierre et Marie Curie – Paris 6. We thank S. Rouberol for running smoothly the horizon cluster for us, and D. Munro for freely distributing his yorick programming language and opengl interface (available at http://yorick.sourceforge.net/ ). SC and CP thank Lena for her hospitality during the course of this work. VD would like to thank the Institut d'Astrophysique de Paris for hospitality during the completion of this work, and acknowledges support by the Swiss National Science Foundation. JD and ASs research is supported by funding from Adrian Beecroft, the Oxford Martin School and the STFC.
Paper: 2019A&A...631A.113F (acknowledgments)
Acknowledgements We would like to thank the referee, Mathieu Van der Swaelmen, for very insightful comments and suggestions which helped to improve this paper in many ways. This research has been partly supported by the Lars Hierta Memorial Foundation, and the Royal Physiographic Society in Lund through Stiftelsen Walter Gyllenbergs fond and Märta och Erik Holmbergs donation. H.J. acknowledges support from the Crafoord Foundation, Stiftelsen Olle Engkvist Byggmästare, and Ruth och Nils-Erik Stenbäcks stiftelse. This work has made use of data from the European Space Agency (ESA) mission Gaia ( https://www.cosmos.esa.int/gaia ), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium ). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This publication made use of the SIMBAD database, operated at CDS, Strasbourg, France; NASA’s Astrophysics Data System; and the VALD database, operated at Uppsala University, the Institute of Astronomy RAS in Moscow, and the University of Vienna.
Paper: 2017A&A...608A.112N (fulltext)
the age-metallicity relation for stars in the solar neighbourhood 3 is often explained as due to radial mixing of stars in a Galactic disk with a gradient in [Fe/H]; stars presently in the solar neighbourhood were born at different Galactocentric distances (). However, in order to explain the low scatter in Mg/Fe and Y/Mg at a given age, the gradients in these abundance ratios should then be vanishingly small at all times, which requires that the ratios between the number of Type II SNe, Type Ia SNe, and AGB stars have evolved in the same way independent of . This corresponds to a weak or non-existent inside-out formation history of the Galactic disk ( Haywood et al. 2015 ). 4.2. 16 Cyg A and B Among the Kepler stars included in this paper, the two components of the 16 Cyg binary system are of particular interest. The very detailed set of oscillation frequencies for these relatively bright stars has made it possible to derive precise values of masses, radii and ages ( Metcalfe et al. 2015 ), He abundances ( Verma et al. 2014 ), and rotation periods ( Davies et al. 2015 ). Furthermore, a comparison of the seismic radii with those determined from interferometric angular diameters ( White et al. 2013 ) provides an important test of asteroseismology (see Silva Aguirre et al. 2017 ). The chemical compositions of 16 Cyg A and B are also very interesting. Cochran et al. (1997) discovered that 16 Cyg B has a Jupiter-sized planet with a mass in an eccentric orbit with a period of 800 days, whereas no planets have been detected around 16 Cyg A . Hence, a comparison of abundances of the two stars has the potential of revealing possible effects of planet formation or planet accretion on stellar surface composition as discussed by Laws Gonzalez (2001) , who in a high-precision spectroscopic analysis found a small metallicity difference, (A – B) = , between 16 Cyg A and B. This was supported by Ramírez et al. (2011) , who found systematic differences of the abundances of the two components corresponding to a mean difference of (A – B) = for 22 elements. However, in a similar study by Schuler et al. (2011) , the mean abundance difference for 15 elements was found to be only dex. In order to clarify the controversy about the chemical compositions of 16 Cyg A and B, Tucci Maia et al. (2014) made a new high-precision spectroscopic analysis using CFHT/ESPaDOnS spectra with and . From a strictly differential analysis of the two stars, they obtained (A – B supporting the abundance difference found by Ramírez et al. (2011) . Furthermore, they found the abundance difference between A and B to increase with elemental condensation temperature for the refractory elements, and interpreted this as a signature of sequestration of refractory elements in the core of the giant planet around 16 Cyg B . Table 4 Comparison of atmospheric parameters and [Fe/H] for 16 Cyg A and B between Tucci Maia et al. (2014) and this paper. Notes. (a) Tucci Maia et al. (2014) . (b) This
Paper: 2018A&A...620A..76A (fulltext)
range from 9 to 12 in the H band. Fig. 2. Luminosity distribution function in the H band for the APOGEE – LAMOST stellar catalog. The catalog covers a broad magnitude range from 7 to almost 14 mag. Most of the stars are in the magnitude range from 9 to 12 in the H band. 2.2. Signal-to-noise ratio APOGEE aims to obtain high-resolution H -band spectra with high S/N (100 per half-resolution element of pixels) for stars in the bulge, disk, and halo of the Milky Way ( Zasowski et al. 2013 , 2017 ). Most APOGEE targets are observed in several visits. After multiple visits, a combined spectrum is produced ( Nidever et al. 2015 ). LAMOST DR3 provides the S/N per pixel for the targets as calculated in five different bands, u , g , r , i and z . Using the center wavelength and bandwidth, they obtain the wavelength range for each SDSS band, and then the S/N in each band is the median value at each pixel in this band ( Luo et al. 2015 ). The cumulative histograms of S/N in APOGEE and in LAMOST for the z band (left panel in Fig. 3 ) shows that the APOGEE survey has 80% of stars in common with S / N > 100, and 40% with S / N > 200. The LAMOST survey has 40% of stars in common with S / N > 100, and 10% with S / N > 200 2 . The right panel in Fig. 3 shows the S/N from APOGEE and LAMOST spectra for the common stellar catalog as a function of the H -band magnitude from the 2MASS survey ( Skrutskie et al. 2006 ). This figure shows a clear relation between magnitude and S/N in APOGEE. This relation is weaker in LAMOST DR3 data and the H band, where a given stellar magnitude has a wide range in S/N. Fig. 3. Left panel : cumulative histograms of S/N per half-resolution element (approximately a pixel) for APOGEE (solid line) and S/N per pixel for LAMOST (dashed line), as provided from the original catalogs. Right panels : S/N from APOGEE and LAMOST spectra for the common objects as a function of the H -band magnitude from the 2MASS survey. 2.3. Radial velocities In APOGEE DR14 ( Abolfathi et al. 2018 ), RVs are determined for each individual visit to identify stars with companion-induced Doppler shifts. The individual visit spectra are resampled and combined to generate a single spectrum for each object. Final RVs are obtained by cross-correlation against a grid of synthetic spectra spanning a wide range of stellar parameters. The APOGEE instrument and the existing RV software routinely deliver RVs per visit at a precision of ∼0.07 km s −1 for S / N > 20, while the survey provides external calibration sufficient to ensure accuracies at the level of ∼0.35 km s −1 . RVs in APOGEE are reported with respect to the center of mass (barycenter) of the solar system ( Nidever et al. 2015 ). The LAMOST 1D pipeline ( Luo et al. 2015 ) also measures the RVs using a cross-correlation method. The pipeline recognizes the stellar spectral classes and simultaneously determines the RVs from the best-fit correlation function between the observed spectra and the template. The RVs are
Paper: 2015MNRAS.449.4404A (acknowledgments)
This work was funded by INCT – Estudos do Espaço, CNPq and FAPESP (proc. 2011-08171-3). This support is gratefully acknowledged. We also thank Ernesto Vieira Neto for his contribution with the numerical integrator.
Paper: 2017A&A...605A..51K (fulltext)
the SPEX package 1 ( Kaastra et al. 1996 ). To help the user to see if a -value corresponds to an acceptable fit, SPEX gives, after spectral fitting, the expected value of and its rms spread, based on the best-fit model. Both quantities are simply determined by adding the expected contributions and their variances over all bins. 3. Simple approximations for the expected value and variance of <italic>C</italic> We obtained simple approximations to the infinite series involved in ( 4 ) and ( 6 ), with relative errors better than for and better than for , as follows: With the help of the above equations, the goodness of fit for the model can be easily assessed. Given the other properties of cstat , such as unbiased parameter estimates, in almost all circumstances cstat is the preferred statistic to be used and the use of -statistics in X-ray spectral analysis should be avoided. 4. Two practical examples Fig. 2 Simplified spectra of the Perseus cluster ( top ) with Hitomi and of Capella ( bottom ) with RGS. We tested our method to assess the goodness of fit with two examples. In the first example, we simulated a spectrum that has approximately the shape of the Perseus cluster as measured with the Hitomi SXS instrument ( Hitomi Collaboration et al. 2016 ). For this demonstration purpose we simplified the model by adopting an isothermal spectrum in collisional ionisation equilibrium with a temperature of 4 keV, proto-solar abundances, and an emission measure matching the flux of Perseus as measured by Hitomi. In our spectral fits, we used the temperature and emission measure of the source as free parameters. The spectrum has 5804 bins and is shown in Fig. 2 , without noise, but with instrumental features included. We scaled this spectrum in flux by factors of with ranging from 0–10, i.e. higher values of corresponding to lower fluxes. For each flux value, we simulated 1000 spectra, performed a best fit, and determined . We then produced a histogram of -values and calculated the 90%, 95%, and 99% percentile points , , and of this distribution. In an actual analysis of an observed spectrum, a model would be rejected at the 90% confidence level if , and this is similar for the other confidence levels. We compare these percentile points, scaled with the expected mean and variance , as delivered by SPEX and described in Sect. 2 , in Fig. 3 . Our second example is a simulation of the spectrum of Capella with the Reflection Grating Spectrometer (RGS) of XMM-Newton. This spectrum is approximated by a single isothermal component with temperature 0.5 keV and appropriate flux for Capella. All further steps are the same as for the Perseus example. The main difference between both spectra is that while Perseus is dominated by continuum emission, owing to its high temperature, Capella is dominated by line emission, owing to its low temperature. The Capella spectrum has 1433 bins and is also shown in Fig. 2 . Fig. 3 Percentile points 90%, 95%, and 99% for the
Paper: 2019ApJ...882..105Y (acknowledgments)
This work is supported by the Science Challenge Project (grant No. TZ2016005), NSFC (grant No. 41674165), and the China Scholarship Council. The author also wants to thank A. Bhattacharjee, Y.-M. Huang, F. Pucci, F. Guo, and A. Spitkovski for helpful discussions.
Paper: 2021MNRAS.501.4266L (fulltext)
3.5 }. We are interested in calculating the amount of mass ejected by the accelerating shock wave. First, we find the depth x esc ( E; n ), defined as the shock position at which the peak material velocity, just behind the shock is where the prefactor accounts for the velocity in the immediate downstream of a strong shock, and the acceleration factor C f ≈ 2 long after shock passage. All matter exterior to x esc eventually accelerates to beyond v esc and becomes unbound. The ejected mass is given by where we used equation ( 6 ) in the last approximation, valid when x esc ≪ R ⋆ . Fig. 3 shows the material velocity profile as the shock propagates through the star, for an explosion energy of E / E bind = 0.018. This figure demonstrates the definition of x s – the depth at which material velocity first becomes sonic (red curve), and x esc – where the post-shock material velocity is eventually accelerated to v esc (purple curve). Figure 3. The material velocity profile at different times after an explosion with energy E / E bind = 0.018 set at the centre of an n = 3/2 polytrope. The dashed green line is the speed of sound in the undisturbed star, and the horizontal solid blue line is the threshold post-shock velocity that results in mass ejection, as described in the text. Highlighted in blue is the velocity profile at the end of the Sedov–Taylor phase, when the shock first becomes weak. The pulse is then subsonic throughout most of the star, until it transitions to a strong shock, shown in red , when the material velocity becomes sonic. Finally, in purple we show the velocity profile when the ejection criterion is first met, at depth x esc ≈ 10 −2 R ⋆ measured from the stellar edge. Fig. 4 provides a complementary view on the results of the numerical simulations, showing the velocity evolution throughout the stellar interior, demonstrating the analytical picture portrayed in Fig. 2 . During the spherical phases, the shock and material velocities decrease while density remains roughly the same. Later on, the pulse continues to decelerate since the ambient temperature drops in the outer layers of the star, while the (subsonic) material velocity behind the shock increases. At some point, the material velocities approach the local sound speed, and the shock begins to accelerate, transitioning to a strong shock. The material outside the layer at which v sh = v esc / C f is being ejected by the explosion. Figure 4. Shock and material velocity as a function of unperturbed density, for a simulation of an explosion of energy E / E bind = 2.9 × 10 −3 set in an n = 3 polytrope. The numerical results in this figure mirror the analytic picture in Fig. 2 . Peak material velocity is plotted in black, and the unperturbed sound speed in blue. The shock velocity (in red) was calculated from the material velocity and the local Mach number with respect to the material lying just ahead of the shock. Finally, the horizontal dashed line is the threshold shock velocity
Paper: 2019MNRAS.487..522B (fulltext)
is particularly true for the Ethos-4 model, where the DAOs are nearly absent as early as z = 20 (given the finite resolution of our numerical set-up). 4.2 The Lyman-α forest at a glance In the top row of Fig. 4 we present the transmitted flux fraction, F , measured along random lines of sight at z = 5 (left) and z = 4.2 (right) for the CDM and sDAO models. The spectra have been created following the procedure outlined in Section 3.2 , after rescaling the mean transmission in each simulation box to the observed transmitted flux. In each case, the lines of sight have been chosen so as to probe the same spatial region in the two simulation volumes. The lower sub-panels show the difference in the transmitted flux, Δ F = F CDM − F sDAO . This figure highlights the fact that the same line of sight probes different intervening structures in the IGM of the two simulations. In particular, it is clear that, just as in WDM, absorption lines are in general deeper for a random line of sight in the CDM simulation than for the sDAO model, signifying the presence of a more clumpy IGM. This difference is a direct consequence of the cut-off in the initial power spectrum. Figure 4. Top row : synthetic mock spectra extracted from our simulations at z = 5 (left) and z = 4.2 (right). Each panel shows the transmitted flux fraction, F , in velocity space for a specific randomly selected line of sight through the simulation volume at the corresponding redshift. The lines of sight are chosen such that the same spatial region is probed in the CDM (black) and sDAO (red) simulation volumes. Clearly, more intervening structure can be inferred from the CDM spectra as evidenced by the deeper transmission lines than in sDAO . The lower panels show the relative difference in transmitted flux, i.e. Δ F = F CDM − F sDAO . Middle row : The same lines of sight shown in the top row but before we rescale the mean flux to the observed values. Bottom row : The unscaled flux PDF at these redshifts. These panels show the ‘true’ difference in the transmitted flux between the CDM and sDAO models, i.e. by removing any artefacts that may be brought in by the different amount of rescaling required for the two DM models. The sDAO and CDM models have been rescaled by different amounts when matching the simulated spectra to the observed mean transmitted flux. To disentangle the implications of rescaling from the different line shapes due to the modified cosmology at a given UV background, the middle row of Fig. 4 shows the unscaled line-of-sight spectra at z = 5 and z = 4.2. Clearly, there are residual differences between the two models even in the unscaled case. Quantitatively, these effects are seen more clearly in the bottom row of Fig. 4 , which shows the probability distribution function (PDF) of the unscaled transmitted flux for the two models at these redshifts. Considering just CDM to begin with, it is noticeable that as time proceeds, there is a cut-off in the flux PDF at higher values
Paper: 2019MNRAS.488..910R (fulltext)
into the ring current, which extends the recovery phase of the storm (Gonzalez et al. 1994 ; Fok et al. 1995 ; Jordanova et al. 1997 ; Miyoshi et al. 2003 ; Søraas et al. 2004 ; Tsurutani et al. 2006a ; Kasahara et al. 2009 ). The prediction of time corresponding to the minimum value that the Dst index will reach is the main goal of geomagnetic storm forecasting research. However, many technological applications rely on the expected time of the disturbed magnetosphere returning to quiet time conditions. These predictions are even more important for extreme/severe geomagnetic storms (Cid et al. 2014 ). In the literature, a linear proportional relation is expected between the decay rate of the ring current and the Dst index through the Dessler–Parker–Sckopke relationship (Olbert, Siscoe Vasyliunas 1968 ; Greenspan Hamilton 2000 ). Therefore, an exponential function was accepted to fit the recovery phase Dst index data to find the decay time of the storm (e.g. Burton, McPherron Russell 1975 ; Hamilton et al. 1988 ; Ebihara Ejiri 1998 ; O’Brien McPherron 2000 ; Dasso, Gómez Mandrini 2002 ; Kozyra et al. 2002 ; Wang, Chao Lin 2003 ; Weygand McPherron 2006 ; Monreal MacMahon Llop-Romero 2008 ). In a few cases, the decay time was expected to be a constant value (Burton et al. 1975 ). Moreover, in some cases, this was dependent on the convective electric field E y (O’Brien McPherron 2000 ), or also dependent on the dynamic pressure (Wang et al. 2003 ). However, the intense and/or extreme storms show a two-phase recovery profile, i.e. an early fast recovery phase followed by slow recovery. Therefore, it was difficult to model the observational data assuming a unique exponential function (Akasofu, Chapman Venkatesan 1963 ; Hamilton et al. 1988 ; Gonzalez et al. 1989 ; Prigancová Feldstein 1992 ; Liemohn et al. 1999 ; Monreal MacMahon Llop-Romero 2008 ). This issue was resolved by assuming a hyperbolic decay function (Aguado et al. 2010 ; Cid et al. 2014 ). In this model the entire recovery phase is dependent on two parameters, i.e. the minimum Dst value ( Dst 0 , the intensity of the storm or the value of the Dst index when the recovery phase starts) and the recovery time (τ h , the time to get the value of Dst 0 /2). A superposed epoch analysis of recovery phases of intense storms from 1963 to 2003 suggests that the hyperbolic decay function reproduces the experimental data better than the exponential function for any type of storm, which indicates a non-linear coupling between the decay rates of Dst and Dst (Aguado et al. 2010 ; Cid et al. 2014 ). Generally, the recovery phase takes one to two days or sometimes more than two days, depending on the source of the magnetic storm (Dessler Parker 1959 ; Gonzalez et al. 1994 ; Kozyra et al. 1997 ; Tsurutani et al. 2006a ). ICMEs generate extreme storms with an exponential/hyperbolic recovery phase profile of one or two days whereas the CIR- and high-speed stream (HSS)-driven magnetic storms can last from
Paper: 2018A&A...620A.150K (acknowledgments)
We thank Dr. J. Puls for comments on the paper and for the discussion on the inclusion of clumping in the wind models. This research was supported by grant GA ČR 18-05665S. Computational resources were provided by the CESNET LM2015042 and the CERIT Scientific Cloud LM2015085, provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures”. The Astronomical Institute Ondřejov is supported by a project RVO:67985815 of the Academy of Sciences of the Czech Republic.
Paper: 2015A&A...583A..48I (acknowledgments)
This work is based on observations taken at the ESO La Silla Paranal Observatory within the MUSE Commissioning. The authors wish to thank the anonymous referee for his/her comments and suggestions that allowed us to greatly improve the paper. E.I. wishes to thank the European Southern Observatory for the financial support, the hospitality and the access to the computer facilities during the several visits in 2014 and 2015 that allowed us to perform the fundamental steps of the work presented in this paper. F.C. acknowledges the European Research Council for the Advanced Grant Program Num 267399-Momentum. P.M.W. received funding through BMBF Verbundforschung (project MUSE-AO, grant 05A14BAC and 05A14MGA). R.B. acknowledges support from the ERC advanced grant 339659-MUSICOS.
Paper: 2019MNRAS.482..988C (acknowledgments)
ACKNOWLEDGEMENTS We are hugely grateful to those who contributed to the LiJET project. We would like to thank all the staff of the 2.4-m telescope of Lijiang station of Yunnan Observatories for their help and support during our observations. Funding for the telescope has been provided by the Chinese Academy of Sciences (CAS) and the People’s Government of Yunnan Province. We acknowledge the support from the Project Based Personnel Exchange Program (PPP) with China Scholarship Council (CSC) and German Academic Exchange Service (DAAD) ([2016] 6041). We also thank the anonymous referee for helpful comments and suggestions, which led to significantly improvement in our manuscript. This work was financially supported by the National Natural Science Foundation of China (NSFC) under grant Nos. 10373023, 10773027 and 11333006, and the CAS through project No. KJCX2-YW-T24, and the CAS ‘Light of West China’ Program.