Estimates Of Stellar Parameters Research Articles

Beyond Spectroscopy. II. Stellar Parameters for over 20 Million Stars in the Northern Sky from SAGES DR1 and Gaia DR3

We present precise photometric estimates of stellar parameters, including effective temperature, metallicity, luminosity classification, distance, and stellar age, for nearly 26 million stars using the methodology developed in the first paper of this series, based on the stellar colors from the Stellar Abundances and Galactic Evolution Survey (SAGES) Data Release 1 and Gaia Early Data Release 3. The optimal design of stellar-parameter sensitive uv filters by SAGES has enabled us to determine photometric-metallicity estimates down to −3.5, similar to our previous results with the SkyMapper Southern Survey (SMSS), yielding a large sample of over five million metal-poor ([Fe/H] ≤ −1.0) stars and nearly one million very metal-poor ([Fe/H] ≤ −2.0) stars. The typical precision is around 0.1 dex for both dwarf and giant stars with [Fe/H] > −1.0, and 0.15–0.25/0.3–0.4 dex for dwarf/giant stars with [Fe/H] < −1.0. Using the precise parallax measurements and stellar colors from Gaia, effective temperature, luminosity classification, distance, and stellar age are further derived for our sample stars. This huge data set in the Northern sky from SAGES, together with similar data in the Southern sky from SMSS, will greatly advance our understanding of the Milky Way, in particular its formation and evolution.

The GAPS Programme at TNG

Context. Great strides have been made in recent years in the understanding of the mechanisms involved in the formation and evolution of planetary systems. Despite this, many observational findings have not yet been corroborated by astrophysical explanations. A fine contribution to the study of planetary formation processes comes from the study of young, low-mass planets, with short orbital periods (&lt;100 days). In the last three years, the NASA/TESS satellite has identified many planets of this kind and their characterization is clearly necessary in order to understand how they formed and evolved. Aims. Within the framework of the Global Architecture of Planetary System (GAPS) project, we performed a validation and characterization (radius and mass) of the ultra-short period planet TOI-1807 b, which orbits its young host star BD+39 2643 (~300 Myr) in only 13 h. This is the youngest ultra-short period planet discovered so far. Methods. Thanks to a joint modeling of the stellar activity and planetary signals in the TESS light curve and in new HARPS-N radial-velocity measurements, combined with accurate estimation of stellar parameters, we validated the planetary nature of TOI-1807 b and measured its orbital and physical parameters. Results. By using astrometric, photometric, and spectroscopic observations, we found that BD+39 2643 is a young, active K dwarf star and a member of a 300 ± 80 Myr old moving group. Furthermore, it rotates in Prot = 8.8 ± 0.1 days. This star hosts an ultra-short period planet, exhibiting an orbital period of only Pb = 0.54937 ± 0.00001 days. Thanks to the exquisite photometric and spectroscopic series, along with the accurate information on its stellar activity, we measured both the radius and the mass of TOI-1807 b with high precision, obtaining PP,b = 1.37 ± 0.09 R⊕ and MP,b = 2.57 ± 0.50 M⊕. These planet parameters correspond to a rocky planet with an Earth-like density (ρb = 1.0 ± 0.3 ρ⊕) and no extended H/He envelope. From the analysis of the age-RP distribution for planets with well measured ages, we inferred that TOI-1807 b may have already lost a large part of its atmosphere over the course of its 300 Myr lifetime.

Stellar labels for hot stars from low-resolution spectra

We set out to determine stellar labels from low-resolution survey spectra of hot stars, specifically OBA stars with Teff ≳ 7500 K. This fills a gap in the scientific analysis of large spectroscopic stellar surveys such as LAMOST, which offers spectra for millions of stars at R ~ 1800 and covers 3800 Å ≤ λ ≤ 9000 Å. We first explore the theoretical information content of such spectra to determine stellar labels via the Cramér-Rao bound. We show that in the limit of perfect model spectra and observed spectra with signal-to-noise ratio ~50–100, precise estimates are possible for a wide range of stellar labels: not only the effective temperature, Teff, surface gravity, log g, and projected rotation velocity, vsin i, but also the micro-turbulence velocity,vmic, helium abundance, NHe/Ntot, and the elemental abundances [C/H], [N/H], [O/H], [Si/H], [S/H], and [Fe/H]. Our analysis illustrates that the temperature regime of Teff ~ 9500 K is challenging as the dominant Balmer and Paschen line strengths vary little with Teff. We implement the simultaneous fitting of these 11 stellar labels to LAMOST hot-star spectra using the Payne approach, drawing on Kurucz’s ATLAS12/SYNTHE local thermodynamic equilibrium spectra as the underlying models. We then obtain stellar parameter estimates for a sample of about 330 000 hot stars with LAMOST spectra, an increase by about two orders of magnitude in sample size. Among them, about 260 000 have good Gaia parallaxes (ω/σω &gt; 5), and their luminosities imply that ≳95% of them are luminous stars, mostly on the main sequence; the rest are evolved lower luminosity stars, such as hot subdwarfs and white dwarfs. We show that the fidelity of the results, particularly for the abundance estimates, is limited by the systematics of the underlying models as they do not account for nonlocal thermodynamic equilibrium effects. Finally, we show the detailed distribution of vsin i of stars with 8000–15 000 K, illustrating that it extends to a sharp cutoff at the critical rotation velocity, vcrit, across a wide range of temperatures.

Photo-astrometric distances, extinctions, and astrophysical parameters for Gaia EDR3 stars brighter than G = 18.5

We present a catalogue of 362 million stellar parameters, distances, and extinctions derived from Gaia’s Early Data Release (EDR3) cross-matched with the photometric catalogues of Pan-STARRS1, SkyMapper, 2MASS, and AllWISE. The higher precision of the Gaia EDR3 data, combined with the broad wavelength coverage of the additional photometric surveys and the new stellar-density priors of the StarHorse code, allows us to substantially improve the accuracy and precision over previous photo-astrometric stellar-parameter estimates. At magnitude G = 14 (17), our typical precisions amount to 3% (15%) in distance, 0.13 mag (0.15 mag) in V-band extinction, and 140 K (180 K) in effective temperature. Our results are validated by comparisons with open clusters, as well as with asteroseismic and spectroscopic measurements, indicating systematic errors smaller than the nominal uncertainties for the vast majority of objects. We also provide distance- and extinction-corrected colour-magnitude diagrams, extinction maps, and extensive stellar density maps that reveal detailed substructures in the Milky Way and beyond. The new density maps now probe a much greater volume, extending to regions beyond the Galactic bar and to Local Group galaxies, with a larger total number density. We publish our results through an ADQL query interface (gaia.aip.de) as well as via tables containing approximations of the full posterior distributions. Our multi-wavelength approach and the deep magnitude limit render our results useful also beyond the next Gaia release, DR3.

BESTP — An automated Bayesian modeling tool for asteroseismology

Asteroseismic observations are crucial to constrain stellar models with precision. Bayesian Estimation of STellar Parameters (BESTP) is a tool that utilizes Bayesian statistics and nested sampling Monte Carlo algorithm to search for the stellar models that best match a given set of classical and asteroseismic constraints from observations. The computation and evaluation of models are efficiently performed in an automated and multi-threaded way. To illustrate the capabilities of BESTP, we estimate fundamental stellar properties for the Sun and the red-giant star HD 222076. In both cases, we find models that are consistent with observations. We also evaluate the improvement in the precision of stellar parameters when the oscillation frequencies of individual modes are included as constraints, compared to the case when only the large frequency separation is included. For the solar case, the uncertainties of estimated masses, radii and ages are reduced by 0.7%, 0.3% and 8% respectively. For HD 222076, they are reduced even more noticeably by 2%, 0.5% and 4.7% respectively. We also note an improvement of 10% for the age of HD 222076 when the Gaia parallax is included as a constraint compared to the case when only the large separation is included as a constraint.

Dynamical Surface Imaging of λ Andromedae

Abstract We present temperature maps of RS CVn star λ Andromedae, reconstructed from interferometric data acquired in 2010 and 2011 by the Michigan Infra-Red Combiner instrument at the Center for High Angular Resolution Astronomy Array. To constrain the stellar parameters required for this imaging task, we first modeled the star using our GPU-accelerated code SImulation and Modeling Tool for Optical Interferometry. The stellar surface was then imaged using our open source interferometric imaging code ROTational Image Reconstruction, in the process further refining the estimation of stellar parameters. We report that the measured angular diameter is 2.742 ± 0.010 mas with a limb-darkening coefficient of 0.231 ± 0.024. While our images are consistent with those of prior works, we provide updated physical parameters for λ Andromedae (R ⋆ = 7.78 ± 0.05 R ⊙, M ⋆ = 1.24 ± 0.72 M ⊙, and log L/L ⊙ = 1.46 ± 0.04).

Consistent and accurate estimation of stellar parameters from HARPS-N Spectroscopy using Deep Learning

Consistent and accurate estimation of stellar parameters is of great importance for information retrieval in astrophysical research. The parameters span a wide range from effective temperature to rotational velocity. We propose to estimate the stellar parameters directly from spectral signals coming from the HARPS-N spectrograph pipeline before any spectrum-processing steps are applied to extract the 1D spectrum. We propose an attention-based model to estimate the stellar parameters, which estimate both mean and uncertainty of the stellar parameters through estimation of the parameters of a Gaussian distribution. The estimated distributions create a basis to generate data-driven Gaussian confidence intervals for the estimated stellar parameters. We show that residual networks and attention-based models can estimate the stellar parameters with high accuracy for low Signal-to-noise ratio (SNR) compared to previous methods. With an observation of the Sun from the HARPS-N spectrograph, we show that the models can estimate stellar parameters from real observational data.

SPECIES

Context. As part of the search for planets around evolved stars, we can understand planet populations around significantly higher-mass stars than the Sun on the main sequence. This population is difficult to study any other way, such as using radial-velocities to measure planet masses and orbital mechanics, since the stars are too hot and rotate too fast to present the quantity of narrow stellar spectral lines that is necessary for measuring velocities at the level of a few m s−1. Aims. Our goal is to estimate stellar parameters for all of the giant stars from the EXPRESS project, which aims to detect planets orbiting evolved stars, and study their occurrence rate as a function of stellar mass. Methods. We analysed the high-resolution echelle spectra of these stars and computed their atmospheric parameters by measuring the equivalent widths for a set of iron lines, using an updated method implemented during this work. Physical parameters, such as mass and radius, were computed by interpolating through a grid of stellar evolutionary models, following a procedure that carefully takes into account the post-main sequence evolutionary phases. The atmospheric parameters, as well as the photometric and parallax data, are used as constraints during the interpolation process. The probabilities of the star being in the red giant branch (RGB) or the horizontal branch (HB) are estimated from the derived distributions. Results. We obtained atmospheric and physical stellar parameters for the whole EXPRESS sample, which comprises a total of 166 evolved stars. We find that 101 of them are most likely first ascending the RGB phase, while 65 of them have already reached the HB phase. The mean derived mass is 1.41 ± 0.46 M⊙ and 1.87 ± 0.53 M⊙ for RGB and HB stars, respectively. To validate our method, we compared our derived physical parameters with data from interferometry and asteroseismology studies. In particular, when comparing to stellar radii derived from interferometric angular diameters, we find: ΔRinter = −0.11 R⊙, which corresponds to a 1.7% difference. Similarly, when comparing with asteroseismology, we obtain the following results: Δ log g = 0.07 cgs (2.4%), ΔR = −0.12 R⊙ (1.5%), ΔM = 0.08 M⊙ (6.2%), and Δage = −0.55 Gyr (11.9%). Additionally, we compared our derived atmospheric parameters with previous spectroscopic studies. We find the following results: ΔTeff = 22 K (0.5%), Δ log g = −0.03 (1.0%) and Δ[Fe/H] = −0.04 dex (2%). We also find a mean systematic difference in the mass with respect to those presented in the EXPRESS original catalogue of ΔM = −0.28 ± 0.27 M⊙, corresponding to a systematic mean difference of 16%. For the rest of the atmospheric and physical parameters we find a good agreement between the original catalogue and the results presented here. Finally, we find excellent agreement between the spectroscopic and trigonometric log g values, showing the internal consistency and robustness of our method. Conclusions. We show that our method, which includes a re-selection of iron lines and changes in the interpolation of evolutionary models, as well as Gaia parallaxes and newer extinction maps, can greatly improve the estimates of stellar parameters for giant stars compared to those presented in our previous work. This method also results in smaller mass estimates, an issue that has been described in results for giant stars from spectroscopy studies in the literature. The results provided here will improve the physical parameter estimates of planetary companions found orbiting these stars and give us insights into their formation and the effect of stellar evolution on their survival.

Revisiting the mass- and radius–luminosity relations for FGK main-sequence stars

Context. Scaling relations are very useful tools for estimating unknown stellar quantities. Within this framework, eclipsing binaries are ideal for this goal because their mass and radius are known with a very good level of accuracy, leading to improved constraints on the models. Aims. We aim to provide empirical relations for the mass and radius as function of luminosity, metallicity, and age. We investigate, in particular, the impact of metallicity and age on those relations. Methods. We used a multi-dimensional fit approach based on the data from DEBCat, an updated catalogue of eclipsing binary observations such as mass, radius, luminosity, effective temperature, gravity, and metallicity. We used the PARAM web interface for the Bayesian estimation of stellar parameters, along with the stellar evolutionary code MESA to estimate the binary age, assuming a coeval hypothesis for both members. Results. We derived the mass and radius–luminosity–metallicity–age relations using 56 stars, with metallicity and mass in the range −0.34 &lt; [Fe/H] &lt; 0.27 and 0.66 &lt; M/M⊙ &lt; 1.8. With that, the observed mass and radius are reproduced with an accuracy of 3.5% and 5.9%, respectively, which is consistent with the other results in literature. Conclusions. We conclude that including the age in such relations increases the quality of the fit, particularly in terms of the mass, as compared to the radius. On the other hand, as otherss authors have noted, we observed an higher dispersion on the mass relation than in that of the radius. We propose that this is due to a stellar age effect.

Coadded Spectroscopic Stellar Parameters and Abundances from the LAMOST Low Resolution Survey

Abstract I combine duplicate spectroscopic stellar parameter estimates in the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Data Release 6 Low Resolution Spectral Survey A, F, G, and K Type stellar parameter catalog. Combining repeat measurements results in a factor of two improvement in the precision of the spectroscopic stellar parameter estimates. Moreover, this trivializes the process of performing coordinate-based cross-matching with other catalogs. Similarly, I combine duplicate stellar abundance estimates for the Xiang et al. catalog which was produced using LAMOST Data Release 5 Low Resolution Spectral Survey data. These data have numerous applications in stellar, galactic, and exoplanet astronomy. The catalogs I produce are available as machine-readable tables at https://doi.org/10.7281/T1/QISGRU.

The CARMENES search for exoplanets around M dwarfs

Existing and upcoming instrumentation is collecting large amounts of astrophysical data, which require efficient and fast analysis techniques. We present a deep neural network architecture to analyze high-resolution stellar spectra and predict stellar parameters such as effective temperature, surface gravity, metallicity, and rotational velocity. With this study, we firstly demonstrate the capability of deep neural networks to precisely recover stellar parameters from a synthetic training set. Secondly, we analyze the application of this method to observed spectra and the impact of the synthetic gap (i.e., the difference between observed and synthetic spectra) on the estimation of stellar parameters, their errors, and their precision. Our convolutional network is trained on synthetic PHOENIX-ACES spectra in different optical and near-infrared wavelength regions. For each of the four stellar parameters,Teff, logg, [M/H], andvsini, we constructed a neural network model to estimate each parameter independently. We then applied this method to 50 M dwarfs with high-resolution spectra taken with CARMENES (Calar Alto high-Resolution search for M dwarfs with Exo-earths with Near-infrared and optical Échelle Spectrographs), which operates in the visible (520–960 nm) and near-infrared wavelength range (960–1710 nm) simultaneously. Our results are compared with literature values for these stars. They show mostly good agreement within the errors, but also exhibit large deviations in some cases, especially for [M/H], pointing out the importance of a better understanding of the synthetic gap.

KELT-17: a chemically peculiar Am star and a hot-Jupiter planet

Context. There is very little information to be found in the literature regarding the detection of planets orbiting chemically peculiar stars. Aims. Our aim is to determine the detailed chemical composition of the remarkable planet host star KELT-17. This object hosts a hot-Jupiter planet with 1.31 MJup detected by transits, and it is one of the more massive and rapidly rotating planet hosts seen to date. We set out to derive a complete chemical pattern for this star, in order to compare it with those of chemically peculiar stars. Methods. We carried out a detailed abundance determination in the planet host star KELT-17 via spectral synthesis. Stellar parameters were estimated iteratively by fitting Balmer line profiles and imposing the Fe ionization balance using the SYNTHE program together with plane-parallel ATLAS12 model atmospheres. Specific opacities for an arbitrary composition and microturbulence velocity vmicro were calculated through the opacity sampling (OS) method. The abundances were determined iteratively by fitting synthetic spectra to metallic lines of 16 different chemical species using SYNTHE. The complete chemical pattern of KELT-17 was compared to the recently published average pattern of Am stars. We estimated the stellar radius using two methods: a) comparing the synthetic spectral energy distribution with the available photometric data and the Gaia parallax, and b) using a Bayesian estimation of stellar parameters using stellar isochrones. Results. We found over-abundances of Ti, Cr, Mn, Fe, Ni, Zn, Sr, Y, Zr, and Ba, together with subsolar values of Ca and Sc. Notably, the chemical pattern agrees with those recently published for Am stars, making KELT-17 the first exoplanet host whose complete chemical pattern is unambiguously identified with this class. The stellar radius derived by two different methods agrees to each other and with those previously obtained in the literature.

Scaling Relations for Solar-Like Oscillations: A Review

The scaling relations for solar-like oscillations provide a translation of the features of the stochastic low-degree modes of oscillation in the Sun to predict the features of solar-like oscillations in other stars with convective outer layers. This prediction is based on their stellar mass, radius and effective temperature. Over time, the original scaling relations have been reversed in their use from predicting features of solar-like oscillations to deriving stellar parameters. Updates to the scaling relations as well as their reference values have been proposed to accommodate for the different requirements set by the change in their use. In this review the suggestions for improving the accuracy of the estimates of stellar parameters through the scaling relations for solar-like oscillations are presented together with a discussion of pros and cons of different approaches.

Bayesian inference of stellar parameters based on 1D stellar models coupled with 3D envelopes

ABSTRACT Stellar models utilizing 1D, heuristic theories of convection fail to adequately describe the energy transport in superadiabatic layers. The improper modelling leads to well-known discrepancies between observed and predicted oscillation frequencies for stars with convective envelopes. Recently, 3D hydrodynamic simulations of stellar envelopes have been shown to facilitate a realistic depiction of superadiabatic convection in 1D stellar models. The resulting structural changes of the boundary layers have been demonstrated to impact not only the predicted oscillation spectra but evolution tracks as well. In this paper, we quantify the consequences that the change in boundary conditions has for stellar parameter estimates of main-sequence stars. For this purpose, we investigate two benchmark stars, Alpha Centauri A and B, using Bayesian inference. We show that the improved treatment of turbulent convection makes the obtained 1D stellar structures nearly insensitive to the mixing length parameter. By using 3D simulations in 1D stellar models, we hence overcome the degeneracy between the mixing length parameter and other stellar parameters. By lifting this degeneracy, the inclusion of 3D simulations has the potential to yield more robust parameter estimates. In this way, a more realistic depiction of superadiabatic convection has important implications for any field that relies on stellar models, including the study of the chemical evolution of the Milky Way Galaxy and exoplanet research.

A Data-driven Technique for Measuring Stellar Rotation

Abstract Measuring stellar rotational velocities is a powerful way to probe the many astrophysical phenomena that drive, or are driven by, the evolution of stellar angular momentum. In this paper, we present a novel data-driven approach to measuring the projected rotational velocity, v sin i . Rather than directly measuring the broadening of spectral lines, we leverage the large information content of high-resolution spectral data to empirically estimate v sin i . We adapt the framework laid down by The Cannon, which trains a generative model of the stellar flux as a function of wavelength using high-fidelity reference data, and can then produce estimates of stellar parameters and abundances for other stars directly from their spectra. Instead of modeling the flux as a function of wavelength, however, we model the first derivative of the spectra, as we expect the slopes of spectral lines to change as a function of v sin i . This technique is computationally efficient and provides a means of rapidly estimating v sin i for large numbers of stars in spectroscopic survey data. We analyze Sloan Digital Sky Survey Apache Point Observatory Galactic Evolution Experiment (APOGEE) spectra, constructing a model informed by high-fidelity stellar parameter estimates derived from high-resolution California Kepler Survey spectra of the same stars. We use the model to estimate v sin i up to 15 km s − 1 for 27,000 APOGEE spectra, in fractions of a second per spectrum. Our estimates agree with the APOGEE v sin i estimates to within 1.2 km s − 1 .

Data-driven stellar parameters for southern TESS FGK targets

Abstract We present stellar parameter estimates for 939 457 southern FGK stars that are candidate targets for the Transiting Exoplanet Survey Satellite (TESS) mission. Using a data-driven method similar to the CANNON, we build a model of stellar colours as a function of stellar parameters. We then use these in combination with stellar evolution models to estimate the effective temperature, gravity, metallicity, mass, radius, and extinction for our selected targets. Our effective temperature estimates compare well with those from spectroscopic surveys and the addition of Gaia DR2 parallaxes allows us to identify subgiant interlopers into the TESS sample. We are able to estimate the radii of TESS targets with a typical uncertainty of 9.3 per cent. This catalogue can be used to screen exoplanet candidates from TESS and provides a homogeneous set of stellar parameters for statistical studies.

The radius inflation problem in short-period low-mass binaries: a large sample analysis

Due to the recent increase in the availability of photometric time-series databases, the characterisation of low-mass eclipsing binaries for the study of their orbital and physical parameters is now possible in large samples and with good precision. We have identified and photometrically characterised a sample of 230 detached close-orbiting eclipsing binaries with low-mass main-sequence components in the Catalina Sky Survey. These low-mass stars have masses of $M \leq 1.0\ M_{\odot}$ and orbital periods shorter than $2$ days. The adopted method provided a robust estimate of stellar parameters (as mass and fractional radius) by using only light curves and photometric colours, since no spectroscopic information was available for these objects. A SDSS-2MASS ten-colour grid of composite synthetic and observed colours and the K-Nearest Neighbours method were employed to identify main-sequence stars and to estimate their effective temperatures, typically of $T_{\rm eff}\leq 5720$ K. Each light curve was modelled with the JKTEBOP code together with an asexual genetic algorithm to obtain the most coherent values for the fitted parameters. The present work provides an unprecedented number of homogeneous estimates of main stellar parameters in short-period low-mass binary systems. The distribution of the components of the investigated detached eclipsing binaries in the mass-radius diagram supports a trend of radius inflation on low-mass main-sequence stars. A relative increase of inflation for lower masses is also found and our results suggest that the secondaries are more inflated, i.e. they present larger radii than the primary components of same mass, when compared to stellar evolutionary models.

Precise radial velocities of giant stars

Context. The determination of accurate stellar parameters of giant stars is essential for our understanding of such stars in general and as exoplanet host stars in particular. Precise stellar masses are vital for determining the lower mass limit of potential substellar companions with the radial velocity method, but also for dynamical modeling of multiplanetary systems and the analysis of planetary evolution. Aims. Our goal is to determine stellar parameters, including mass, radius, age, surface gravity, effective temperature and luminosity, for the sample of giants observed by the Lick planet search. Furthermore, we want to derive the probability of these stars being on the horizontal branch (HB) or red giant branch (RGB), respectively. Methods. We compare spectroscopic, photometric and astrometric observables to grids of stellar evolutionary models using Bayesian inference. Results. We provide tables of stellar parameters, probabilities for the current post-main sequence evolutionary stage, and probability density functions for 372 giants from the Lick planet search. We find that 81% of the stars in our sample are more probably on the HB. In particular, this is the case for 15 of the 16 planet host stars in the sample. We tested the reliability of our methodology by comparing our stellar parameters to literature values and find very good agreement. Furthermore, we created a small test sample of 26 giants with available asteroseismic masses and evolutionary stages and compared these to our estimates. The mean difference of the stellar masses for the 24 stars with the same evolutionary stages by both methods is only ΔM = 〈Mtrk. − MAst.〉 = 0.01 ± 0.20 M⊙. Conclusions. We do not find any evidence for large systematic differences between our results and estimates of stellar parameters based on other methods. In particular we find no significant systematic offset between stellar masses provided by asteroseismology to our Bayesian estimates based on evolutionary models.

Gaia Data Release 2

Context. The Gaia Data Release 2 (DR2) contains the first release of radial velocities complementing the kinematic data of a sample of about 7 million relatively bright, late-type stars. Aims. This paper provides a detailed description of the Gaia spectroscopic data processing pipeline, and of the approach adopted to derive the radial velocities presented in DR2. Methods. The pipeline must perform four main tasks: (i) clean and reduce the spectra observed with the Radial Velocity Spectrometer (RVS); (ii) calibrate the RVS instrument, including wavelength, straylight, line-spread function, bias non-uniformity, and photometric zeropoint; (iii) extract the radial velocities; and (iv) verify the accuracy and precision of the results. The radial velocity of a star is obtained through a fit of the RVS spectrum relative to an appropriate synthetic template spectrum. An additional task of the spectroscopic pipeline was to provide first-order estimates of the stellar atmospheric parameters required to select such template spectra. We describe the pipeline features and present the detailed calibration algorithms and software solutions we used to produce the radial velocities published in DR2. Results. The spectroscopic processing pipeline produced median radial velocities for Gaia stars with narrow-band near-IR magnitude GRVS ≤ 12 (i.e. brighter than V ~ 13). Stars identified as double-lined spectroscopic binaries were removed from the pipeline, while variable stars, single-lined, and non-detected double-lined spectroscopic binaries were treated as single stars. The scatter in radial velocity among different observations of a same star, also published in Gaia DR2, provides information about radial velocity variability. For the hottest (Teff ≥ 7000 K) and coolest (Teff ≤ 3500 K) stars, the accuracy and precision of the stellar parameter estimates are not sufficient to allow selection of appropriate templates. The radial velocities obtained for these stars were removed from DR2. The pipeline also provides a first-order estimate of the performance obtained. The overall accuracy of radial velocity measurements is around ~200–300 m s−1, and the overall precision is ~1 km s−1; it reaches ~200 m s−1 for the brightest stars.

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the Extended Baryon Oscillation Spectroscopic Survey and from the Second Phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.