Radial Velocity Experiment Survey Research Articles

The Astrophysical Variance in Gaia–Radial Velocity Spectrometer Spectra

Abstract Large surveys are providing a diversity of spectroscopic observations with Gaia alone set to deliver millions of Ca-triplet-region spectra across the Galaxy. We aim to understand the dimensionality of the chemical abundance information in the Gaia–Radial Velocity Spectrometer (RVS) data to inform galactic archeology pursuits. We fit a quadratic model of four primary sources of variability, described by labels of T eff, log g , [Fe/H], and [α/Fe], to the normalized flux of 10,802 red-clump stars from the Gaia-RVS-like Abundances and Radial velocity Galactic Origins Survey (ARGOS). We examine the residuals between ARGOS spectra and the models and find that the models capture the flux variability across 85% of the wavelength region. The remaining residual variance is concentrated to the Ca-triplet features, at an amplitude up to 12% of the normalized flux. We use principal component analysis on the residuals and find orthogonal correlations in the Ca-triplet core and wings. This variability, not captured by our model, presumably marks departures from the completeness of the 1D LTE label description. To test the indication of low-dimensionality, we turn to abundance-space to infer how well we can predict measured [Si/H], [O/H], [Ca/H], [Ni/H], and [Al/H] abundances from the Gaia-RVS-like Radial Velocity Experiment survey with models of T eff, log g , [Fe/H], and [Mg/Fe]. We find that we can nearly entirely predict these abundances. Using high-precision Apache Point Observatory Galactic Evolution Experiment abundances, we determine that a measurement uncertainty of <0.03 dex is required to capture additional information from these elements. This indicates that a four-label model sufficiently describes chemical abundance variance for an approximate signal-to-noise ratio <200 per pixel, in Gaia-RVS spectra.

The RAdial Velocity Experiment (RAVE): Parameterisation of RAVE spectra based on convolutional neural networks

Context. Data-driven methods play an increasingly important role in the field of astrophysics. In the context of large spectroscopic surveys of stars, data-driven methods are key in deducing physical parameters for millions of spectra in a short time. Convolutional neural networks (CNNs) enable us to connect observables (e.g. spectra, stellar magnitudes) to physical properties (atmospheric parameters, chemical abundances, or labels in general). Aims. We test whether it is possible to transfer the labels derived from a high-resolution stellar survey to intermediate-resolution spectra of another survey by using a CNN. Methods. We trained a CNN, adopting stellar atmospheric parameters and chemical abundances from APOGEE DR16 (resolution R = 22 500) data as training set labels. As input, we used parts of the intermediate-resolution RAVE DR6 spectra (R ∼ 7500) overlapping with the APOGEE DR16 data as well as broad-band ALL_WISE and 2MASS photometry, together with Gaia DR2 photometry and parallaxes. Results. We derived precise atmospheric parameters Teff, log(g), and [M/H], along with the chemical abundances of [Fe/H], [α/M], [Mg/Fe], [Si/Fe], [Al/Fe], and [Ni/Fe] for 420 165 RAVE spectra. The precision typically amounts to 60 K in Teff, 0.06 in log(g) and 0.02−0.04 dex for individual chemical abundances. Incorporating photometry and astrometry as additional constraints substantially improves the results in terms of the accuracy and precision of the derived labels, as long as we operate in those parts of the parameter space that are well-covered by the training sample. Scientific validation confirms the robustness of the CNN results. We provide a catalogue of CNN-trained atmospheric parameters and abundances along with their uncertainties for 420 165 stars in the RAVE survey. Conclusions. CNN-based methods provide a powerful way to combine spectroscopic, photometric, and astrometric data without the need to apply any priors in the form of stellar evolutionary models. The developed procedure can extend the scientific output of RAVE spectra beyond DR6 to ongoing and planned surveys such as Gaia RVS, 4MOST, and WEAVE. We call on the community to place a particular collective emphasis and on efforts to create unbiased training samples for such future spectroscopic surveys.

Local disc model in view of Gaia DR1 and RAVE data

Aims. We test the performance of the semi-analytic self-consistent Just-Jahreiß disc model (JJ model) with the astrometric data from the Tycho-Gaia Astrometric Solution (TGAS) sub-catalogue of the first Gaia data release (Gaia DR1), as well as the radial velocities from the fifth data release of the Radial Velocity Experiment survey (RAVE DR5). Methods. We used a sample of 19 746 thin-disc stars from the TGAS×RAVE cross-match selected in a local solar cylinder of 300 pc radius and 1 kpc height below the Galactic plane. Based on the JJ model, we simulated this sample via the forward modelling technique. First, we converted the predicted vertical density laws of the thin-disc populations into a mock sample. For this we used the Modules and Experiments in Stellar Astrophysics (MESA) Isochrones and Stellar Tracks (MIST), a star formation rate (SFR) that decreased after a peak at 10 Gyr ago, and a three-slope broken power-law initial mass function (IMF). Then the obtained mock populations were reddened with a 3D dust map and were subjected to the selection criteria corresponding to the RAVE and TGAS observational limitations as well as to additional cuts applied to the data sample. We calculated the quantities of interest separately at different heights above the Galactic plane, taking the distance error effects in horizontal and vertical directions into account separately. Results. The simulated vertical number density profile agrees well with the data. An underestimation of the stellar numbers begins at ∼800 pc from the Galactic plane, which is expected as the possible influence of populations from |z| > 1 kpc is ignored during the modelling. The lower main sequence (LMS) is found to be thinner and under-populated by 3.6% relative to the observations. The corresponding deficits for the upper main sequence (UMS) and red giant branch (RGB) are 6% and 34.7%, respectively. However, the intrinsic uncertainty related to the choice of stellar isochrones is ∼10% in the total stellar number. The vertical velocity distribution function f(|W|) simulated for the whole cylinder agrees to within 1σ with the data. This marginal agreement arises because the dynamically cold populations at heights < 200 pc from the Galactic plane are underestimated. We also find that the model gives a fully realistic representation of the vertical gradient in stellar populations when studying the Hess diagrams for different horizontal slices. We also checked and confirm the consistency of our results with the newly available second Gaia data release (DR2). Conclusions. Based on these results and considering the uncertainties in the data selection as well as the sensitivity of the simulations to the sample selection function, we conclude that the fiducial JJ model confidently reproduces the vertical trends in the thin-disc stellar population properties. Thus, it can serve as a starting point for the future extension of the JJ model to other Galactocentric distances.

Correlations between age, kinematics, and chemistry as seen by the RAVE survey

We explore the connections between stellar age, chemistry, and kinematics across a Galactocentric distance of $7.5 < R\,(\mathrm{kpc}) < 9.0$, using a sample of $\sim 12\,000$ intermediate-mass (FGK) turnoff stars observed with the RAdial Velocity Experiment (RAVE) survey. The kinematics of this sample are determined using radial velocity measurements from RAVE, and parallax and proper motion measurements from the Tycho-Gaia Astrometric Solution (TGAS). In addition, ages for RAVE stars are determined using a Bayesian method, taking TGAS parallaxes as a prior. We divide our sample into young ($0 < \tau < 3$ Gyr) and old ($8 < \tau < 13$ Gyr) populations, and then consider different metallicity bins for each of these age groups. We find significant differences in kinematic trends of young and old, metal-poor and metal-rich, stellar populations. In particular, we find a strong metallicity dependence in the mean Galactocentric radial velocity as a function of radius ($\partial {V_{\rm R}}/\partial R$) for young stars, with metal-rich stars having a much steeper gradient than metal-poor stars. For $\partial {V_{\phi}}/\partial R$, young, metal-rich stars significantly lag the LSR with a slightly positive gradient, while metal-poor stars show a negative gradient above the LSR. We interpret these findings as correlations between metallicity and the relative contributions of the non-axisymmetries in the Galactic gravitational potential (the spiral arms and the bar) to perturb stellar orbits.

Very metal-poor stars observed by the RAVE survey

We present a novel analysis of the metal-poor star sample in the complete Radial Velocity Experiment (RAVE) Data Release 5 catalog with the goal of identifying and characterizing all very metal-poor stars observed by the survey. Using a three-stage method, we first identified the candidate stars using only their spectra as input information. We employed an algorithm called t-SNE to construct a low-dimensional projection of the spectrum space and isolate the region containing metal-poor stars. Following this step, we measured the equivalent widths of the near-infrared CaII triplet lines with a method based on flexible Gaussian processes to model the correlated noise present in the spectra. In the last step, we constructed a calibration relation that converts the measured equivalent widths and the color information coming from the 2MASS and WISE surveys into metallicity and temperature estimates. We identified 877 stars with at least a 50% probability of being very metal-poor $(\rm [Fe/H] < -2\,\rm dex)$, out of which 43 are likely extremely metal-poor $(\rm [Fe/H] < -3\,\rm dex )$. The comparison of the derived values to a small subsample of stars with literature metallicity values shows that our method works reliably and correctly estimates the uncertainties, which typically have values $\sigma_{\rm [Fe/H]} \approx 0.2\,\mathrm{dex}$. In addition, when compared to the metallicity results derived using the RAVE DR5 pipeline, it is evident that we achieve better accuracy than the pipeline and therefore more reliably evaluate the very metal-poor subsample. Based on the repeated observations of the same stars, our method gives very consistent results. The method used in this work can also easily be extended to other large-scale data sets, including to the data from the Gaia mission and the upcoming 4MOST survey.

The distribution function of the Galaxy's dark halo

Starting from the hypothesis that the Galaxy's dark halo responded adiabatically to the infall of baryons, we have constructed a self-consistent dynamical model of the Galaxy that satisfies a large number of observations, including measurements of gas terminal velocities and masers, the kinematics of 180 000 giant stars from the RAVE (RAdial Velocity Experiment) survey and star-count data from the Sloan Digital Sky Survey. The stellar disc and the dark halo are both specified by distribution functions of the action integrals. The model is obtained by extending the work of Piffl et al. from the construction of a single model to a systematic search of model space. Whereas the model of Piffl et al. violated constraints on the terminal-velocity curve, our model respects these constraints by adopting a long scalelength Rd = 3.66 kpc for the thin and thick discs. The model is, however, inconsistent with the measured optical depth for microlensing of bulge stars because it attributes too large a fraction of the density at R ≲ 3 kpc to dark matter rather than stars. Moreover, it now seems likely that the thick disc's scalelength is significantly shorter than the model implies. Shortening this scalelength would cause the constraints from the rotation curve to be violated anew. We conclude that we can now rule out adiabatic compression of our Galaxy's dark halo.

Thick disk kinematics from RAVE and the solar motion

Radial velocity surveys such as the Radial Velocity Experiment (RAVE) provide us with measurements of hundreds of thousands of nearby stars most of which belong to the Galactic thin, thick disk or halo. Ideally, to study the Galactic disks (both thin and thick) one should make use of the multi-dimensional phase-space and the whole pattern of chemical abundances of their stellar populations. In this paper, with the aid of the RAVE Survey, we study the thin and thick disks of the Milky Way, focusing on the latter. We present a technique to disentangle the stellar content of the two disks based on the kinematics and other stellar parameters such as the surface gravity of the stars. Using the Padova Galaxy Model, we checked the ability of our method to correctly isolate the thick disk component from the Galaxy mixture of stellar populations. We introduce selection criteria in order to clean the observed radial velocities from the Galactic differential rotation and to take into account the partial sky coverage of RAVE. We developed a numerical technique to statistically disentangle thin and thick disks from their mixture. We deduce the components of the solar motion relative to the Local Standard of Rest (LSR) in the radial and vertical direction, the rotational lag of the thick disk component relative to the LSR, and the square root of the absolute value of the velocity dispersion tensor for the thick disk alone. The analysis of the thin disk is presented in another paper. We find good agreement with previous independent parameter determinations. In our analysis we used photometrically determined distances. In the Appendix we show that similar values can be found for the thick disk alone as derived in the main sections of our paper even without the knowledge of photometric distances.

Spectroscopic distances to late-type stars

AbstractA common approach to determining distances to stars without astrometric information is to compare stellar evolution models with parameters obtained from spectroscopic techniques. This method is routinely applied in the context of large-scale stellar surveys out to distances of several kpc. However, systematic errors may arise because of inaccurate spectroscopic parameters. We explore the effects of non-local thermodynamic equilibrium (NLTE) on the determination of surface gravities and metallicities for a large sample of metal-poor stars within approximately 10 kpc of the Sun. Using the improved Teff scale, we then show that stellar parameters estimated based on the widely used method of 1D LTE excitation-ionization balance of Fe results in distances which are systematically in error. For metal-poor giants, [Fe/H] ~ −2 dex, the distances can be overestimated by up to 70%. We compare the results with those from the Radial Velocity Experiment Survey catalogue (rave) for the stars in common, and find similar offsets.

EXPLORING THE MORPHOLOGY OF RAVE STELLAR SPECTRA

The RAdial Velocity Experiment (RAVE) is a medium resolution R~7500 spectroscopic survey of the Milky Way which already obtained over half a million stellar spectra. They present a randomly selected magnitude-limited sample, so it is important to use a reliable and automated classification scheme which identifies normal single stars and discovers different types of peculiar stars. To this end we present a morphological classification of 350,000 RAVE survey stellar spectra using locally linear embedding, a dimensionality reduction method which enables representing the complex spectral morphology in a low dimensional projected space while still preserving the properties of the local neighborhoods of spectra. We find that the majority of all spectra in the database ~90-95% belong to normal single stars, but there is also a significant population of several types of peculiars. Among them the most populated groups are those of various types of spectroscopic binary and chromospherically active stars. Both of them include several thousands of spectra. Particularly the latter group offers significant further investigation opportunities since activity of stars is a known proxy of stellar ages. Applying the same classification procedure to the sample of normal single stars alone shows that the shape of the projected manifold in two dimensional space correlates with stellar temperature, surface gravity and metallicity.

Distance determination for RAVE stars using stellar models

The RAdial Velocity Experiment (RAVE) is a spectroscopic survey of the Milky Way. We use the subsample of spectra with spectroscopically determined values of stellar parameters to determine the distances to these stars. The list currently contains 235,064 high quality spectra which show no peculiarities and belong to 210,872 different stars. The numbers will grow as the RAVE survey progresses. The public version of the catalog will be made available through the CDS services along with the ongoing RAVE public data releases. The distances are determined with a method based on the work by Breddels et al.~(2010). Here we assume that the star undergoes a standard stellar evolution and that its spectrum shows no peculiarities. The refinements include: the use of either of the three isochrone sets, a better account of the stellar ages and masses, use of more realistic errors of stellar parameter values, and application to a larger dataset. The derived distances of both dwarfs and giants match within ~21% to the astrometric distances of Hipparcos stars and to the distances of observed members of open and globular clusters. Multiple observations of a fraction of RAVE stars show that repeatability of the derived distances is even better, with half of the objects showing a distance scatter of \simlt 11%. RAVE dwarfs are ~300 pc from the Sun, and giants are at distances of 1 to 2 kpc, and up to 10 kpc. This places the RAVE dataset between the more local Geneva-Copenhagen survey and the more distant and fainter SDSS sample. As such it is ideal to address some of the fundamental questions of Galactic structure and evolution in the pre-Gaia era. Individual applications are left to separate papers, here we show that the full 6-dimensional information on position and velocity is accurate enough to discuss the vertical structure and kinematic properties of the thin and thick disks.

Estimation of the tilt of the stellar velocity ellipsoid from RAVE and implications for mass models

We present a measure of the inclination of the velocity ellipsoid at 1 kpc below the Galactic plane using a sample of red clump giants from the RAdial Velocity Experiment (RAVE) Data Release 2. We find that the velocity ellipsoid is tilted towards the Galactic plane with an inclination of ⁠. We compare this value to computed inclinations for two mass models of the Milky Way. We find that our measurement is consistent with a short scalelength of the stellar disc (Rd≃ 2 kpc) if the dark halo is oblate or with a long scalelength (Rd≃ 3 kpc) if the dark halo is prolate. Once combined with independent constraints on the flattening of the halo, our measurement suggests that the scalelength is approximately halfway between these two extreme values, with a preferred range 2.5–2.7 kpc for a nearly spherical halo. Nevertheless, no model can be clearly ruled out. With the continuation of the RAVE survey, it will be possible to provide a strong constraint on the mass distribution of the Milky Way using refined measurements of the orientation of the velocity ellipsoid.

Present state and promises to unravel the structure and kinematics of the Milky Way with the RAVE survey

AbstractThe RAdial Velocity Experiment (RAVE) is an ambitious survey to measure the radial velocities, temperatures, surface gravities, metallicities and abundance ratios for up to a million stars using the 1.2-m UK Schmidt Telescope of the Anglo-Australian Observatory (AAO), over the period 2003–2011. The survey represents a big advance in our understanding of our own Milky Way galaxy. The main data product will be a southern hemisphere survey of about a million stars. Their selection is based exclusively on their I–band colour, so avoiding any colour-induced bias. RAVE is expected to be the largest spectroscopic survey of the Solar neighbourhood in the coming decade, but with a significant fraction of giant stars reaching out to 10 kpc from the Sun. RAVE offers the first truly representative inventory of stellar radial velocities for all major components of the Galaxy. Here we present the first scientific results of this survey as well as its second data release which doubles the number of previously released radial velocities. For the first time, the release also provides atmospheric parameters for a large fraction of the second year data, making it an unprecedented tool to study the formation of the Milky Way. Plans for further data releases are outlined.