Distance determination for RAVE stars using stellar models

Burnett, B.; Binney, James; Sharma, Sanjib; Williams, M.; Zwitter, T.; Bienaymé, O.; Bland-Hawthorn, Joss; Freeman, K. C.; Fulbright, Jon P.; Gibson, Brad K.; Gilmore, G.; Grebel, Eva K.; Helmi, Amina; Munari, U.; Navarro, Julio F.; Parker, Q. A.; Seabroke, G. M.; Siebert, A.; Siviero, A.; Steinmetz, Matthias; Watson, Fred; Wyse, Rosemary F. Ġ.

doi:10.1051/0004-6361/201116715

Cited by 62 publications

(84 citation statements)

References 30 publications

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“…These quantities are compared to predictions from stellar evolutionary models. The comparison between model and measured parameters follows a statistical approach that is similar to previous works (Burnett & Binney 2010;Burnett et al 2011;Binney et al 2014a). …”

Section: The Methodsmentioning

confidence: 99%

“…For >200 000 of these stars, Burnett et al (2011) have been able to derive spectrophotometric distances with spectroscopic parameters from the RAVE pipeline (Kordopatis et al 2011) as well as near-infrared photometry from 2MASS (Skrutskie et al 2006;Cutri et al 2003). Their method has provided the background for this, which is largely based on the Bayesian approach proposed by Burnett et al (2011). More recently, Binney et al (2014a) presented an improved version of the method.…”

Section: Ravementioning

confidence: 99%

“…Burnett & Binney (2010) make use of a comprehensive set of measured parameters and their estimated uncertainties to infer, for each star, the probability distribution that a set of chosen stellar models generate the data. The method was further refined by Burnett et al (2011) and Binney et al (2014a), and later used to study Galactic chemo-dynamics in, e.g. Boeche et al (2013a,b), Binney et al (2014b) or Kordopatis et al (2015).…”

Section: Introductionmentioning

confidence: 99%

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Spectro-photometric distances to stars: A general purpose Bayesian approach

Santiago

Brauer

Anders

et al. 2015

A&A

108

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Context. Determining distances to individual field stars is a necessary step towards mapping Galactic structure and determining spatial variations in the chemo-dynamical properties of stellar populations in the Milky Way. Aims. In order to provide stellar distance estimates for various spectroscopic surveys, we have developed a code that estimates distances to stars using measured spectroscopic and photometric quantities. We employ a Bayesian approach to build the probability distribution function over stellar evolutionary models given these data, delivering estimates of model parameters (including distances) for each star individually. Our method provides several alternative distance estimates for each star in the output, along with their associated uncertainties. This facilitates the use of our method even in the absence of some measurements. Methods. The code was first tested on simulations, successfully recovering input distances to mock stars with 1% bias. We found the uncertainties scale with the uncertainties in the adopted spectro-photometric parameters. The method-intrinsic random distance uncertainties for typical spectroscopic survey measurements amount to around 10% for dwarf stars and 20% for giants, and are most sensitive to the quality of log g measurements. Results. The code was then validated by comparing our distance estimates to parallax measurements from the H mission for nearby stars (<300 pc), to asteroseismic distances of CoRoT red giant stars, and to known distances of well-studied open and globular clusters. The photometric data of these reference samples cover both optical and infrared wavelengths. The spectroscopic parameters are also based on spectra taken at various wavelengths, with varying spectral coverage and resolution: the Sloan Digital Sky Survey programs SEGUE and APOGEE, as well as various ESO instruments. Conclusions. External comparisons confirm that our distances are subject to very small systematic biases with respect to the fundamental H scale (+0.4% for dwarfs, and +1.6% for giants). The typical random distance scatter is 18% for dwarfs, and 26% for giants. For the CoRoT-APOGEE sample, which spans Galactocentric distances of 4−14 kpc, the typical random distance scatter is 15% both for the nearby and farther data. Our distances are systematically larger than the CoRoT distances by about +9%, which can mostly be attributed to the different choice of priors. The comparison to known distances of star clusters from SEGUE and APOGEE has led to significant systematic differences for many cluster stars, but with opposite signs and substantial scatter. Finally, we tested our distances against those previously determined for a high-quality sample of giant stars from the RAVE survey, again finding a small systematic trend of +5% and an rms scatter of 30%. Efforts are underway to provide our code to the community by running it on a public server.

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Section: The Methodsmentioning

confidence: 99%

Section: Ravementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spectro-photometric distances to stars: A general purpose Bayesian approach

Santiago

Brauer

Anders

et al. 2015

A&A

108

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“…Distances derived by the SDSS-III Brazilian Participation Group (BPG; Santiago et al 2016) were computed using the Bayesian methodology explained in Burnett & Binney (2010), Burnett et al (2011), andBinney et al (2014). From the measured spectroscopic parameters coupled with 2MASS photometry, they obtained the posterior distance probablility distribution function (PDF) for each star over a grid of PARSEC (Bressan 2012) stellar evolutionary models.…”

Section: Distancesmentioning

confidence: 99%

Chemical trends in the Galactic halo from APOGEE data

Fernández-Alvar

Carigi

Prieto

et al. 2016

Mon. Not. R. Astron. Soc.

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The galaxy formation process in the Λ-Cold Dark Matter scenario can be constrained from the analysis of stars in the Milky Way's halo system. We examine the variation of chemical abundances in distant halo stars observed by the Apache Point Galactic Evolution Experiment (APOGEE), as a function of distance from the Galactic center (r) and iron abundance ([M/H]), in the range 5 r 30 kpc and −2.5 < [M/H] < 0.0. We perform a statistical analysis of the abundance ratios derived by the APOGEE pipeline (ASPCAP) and distances calculated by several approaches. Our analysis reveals signatures of a different chemical enrichment between the inner and outer regions of the halo, with a transition at about 15 kpc. The derived metallicity distribution function exhibits two peaks, at [M/H] ∼ −1.5 and ∼ −2.1, consistent with previously reported halo metallicity distributions. We obtain a difference of ∼ 0.1 dex for α-element-to-iron ratios for stars at r > 15 kpc and [M/H] > −1.1 (larger in the case of O, Mg and S) with respect to the nearest halo stars. This result confirms previous claims for low-α stars found at larger distances. Chemical differences in elements with other nucleosynthetic origins (Ni, K, Na, and Al) are also detected. C and N do not provide reliable information about the interstellar medium from which stars formed because our sample comprises RGB and AGB stars and can experience mixing of material to their surfaces.

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“…Distance estimates Zwitter et al 2010;Burnett et al 2011;Binney et al 2013) enable one to locate the stars in three-dimensional space. Proper motions are listed in a variety of catalogues: Tycho2 (Høg et al 2000), the PPM-Extended catalogues PPMX and PPMXL (Roeser et al 2008(Roeser et al , 2010, and the second and third US Naval Observatory CCD Astrograph Catalog UCAC2 and UCAC3 (Zacharias et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Chemical gradients in the Milky Way from the RAVE data

Boeche

Siebert

Piffl

et al. 2013

A&A

Self Cite

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Aims. We aim at measuring the chemical gradients of the elements Mg, Al, Si, and Fe along the Galactic radius to provide new constraints on the chemical evolution models of the Galaxy and Galaxy models such as the Besançon model. Thanks to the large number of stars of our RAVE sample we can study how the gradients vary as function of the distance from the Galactic plane. Methods. We analysed three different samples selected from three independent datasets: a sample of 19 962 dwarf stars selected from the RAVE database, a sample of 10 616 dwarf stars selected from the Geneva-Copenhagen Survey (GCS) dataset, and a mock sample (equivalent to the RAVE sample) created by using the GALAXIA code, which is based on the Besançon model. The three samples were analysed by using the very same method for comparison purposes. We integrated the Galactic orbits and obtained the guiding radii (R g ) and the maximum distances from the Galactic plane reached by the stars along their orbits (Z max ). We measured the chemical gradients as functions of R g at different Z max . Results. We found that the chemical gradients of the RAVE and GCS samples are negative and show consistent trends, although they are not equal: at Z max < 0.4 kpc and 4.5 < R g (kpc) < 9.5, the iron gradient for the RAVE sample is d[Fe/H]/dR g = −0.065 dex kpc −1 , whereas for the GCS sample it is d[Fe/H]/dR g = −0.043 dex kpc −1 with internal errors of ±0.002 and ±0.004 dex kpc −1 , respectively. The gradients of the RAVE and GCS samples become flatter at larger Z max . Conversely, the mock sample has a positive iron gradient of d[Fe/H]/dR g = +0.053 ± 0.003 dex kpc −1 at Z max < 0.4 kpc and remains positive at any Z max . These positive and unrealistic values originate from the lack of correlation between metallicity and tangential velocity in the Besançon model. In addition, the low metallicity and asymmetric drift of the thick disc causes a shift of the stars towards lower R g and metallicity which, together with the thin-disc stars with a higher metallicity and R g , generates a fictitious positive gradient of the full sample. The flatter gradient at larger Z max found in the RAVE and the GCS samples may therefore be due to the superposition of thin-and thick-disc stars, which mimicks a flatter or positive gradient. This does not exclude the possibility that the thick disc has no chemical gradient. The discrepancies between the observational samples and the mock sample can be reduced by i) decreasing the density; ii) decreasing the vertical velocity; and iii) increasing the metallicity of the thick disc in the Besançon model.

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Distance determination for RAVE stars using stellar models

Cited by 62 publications

References 30 publications

Spectro-photometric distances to stars: A general purpose Bayesian approach

Spectro-photometric distances to stars: A general purpose Bayesian approach

Chemical trends in the Galactic halo from APOGEE data

Chemical gradients in the Milky Way from the RAVE data

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