The CoRoT‐GES Collaboration: Improving red giants spectroscopic surface gravitity and abundances with asteroseismology

Abstract

Nowadays large spectroscopic surveys, like the Gaia-ESO Survey (GES), provide unique stellar databases for better investigating the formation and evolution of our Galaxy. Great attention must be paid to the accuracy of the basic stellar properties derived: large uncertainties in stellar parameters lead to large uncertainties in abundances, distances and ages. Asteroseismology has a key role in this context: when seismic information is combined with information derived from spectroscopic analysis, highly precise constraints on distances, masses, extinction and ages of Red Giants can be obtained. In the light of this promising joint-action, we started the CoRoT-GES collaboration. We present a set of 1,111 CoRoT stars, observed by GES from December 2011 to July 2014, these stars belong to the CoRoT field LRc01, pointing at the inner Galactic Disk. Among these stars, 534 have reliable global seismic parameters. By combining seismic informations and spectroscopy, we derived precise stellar parameters, ages, kinematic and orbital parameters and detailed element abundances for this sample of stars. We also show that, thanks to asteroseismology, we are able to obtain a higher precision than what can be achieved by the standard spectroscopic means. This sample of CoRoT Red Giants, spanning Galactocentric distances from 5 to 8 kpc and a wide age interval (1-13 Gyrs), provides us a representative sample for the inner disk population.

Systematic differences in the spectroscopic analysis of red giants

“…Instead of fixing the log g value during our spectroscopic analysis, we used it as a reference when choosing our preferred combination of line list and equivalent width measurement program to make the spectroscopic results self consistent and in agreement with asteroseismology. Doyle et al (2017) investigated the effect of fixing log g in the spectroscopic analysis of a set of FGK stars, that already had accurate parameters determined from other methods, and found that fixing the surface gravity did not improve the precision on the other spectroscopic parameters, they find an average difference in determined T eff = 3±13 K. This is however in contrast to Hawkins et al (2016) and Valentini et al (2016) who both find better precision on the other atmospheric parameters when fixing the log g to the asteroseismic.…”

“…Asteroseismology provides log g values with much higher precision than what can normally be achieved with spectroscopy, which can greatly help constrain the spectroscopic analysis (see e.g. Valentini et al 2016;Pinsonneault et al 2014, where asteroseismology has been used to calibrate the spectroscopic surface gravity by RAVE and APOGEE, respectively). It has however been shown that when doing a fully differential spectroscopic analysis of very high quality data, precision levels comparable to asteroseismology are achievable.…”

Photo-astrometric distances, extinctions, and astrophysical parameters for Gaia DR2 stars brighter than G = 18

“…15 show an analogous comparison for our secondary output parameters log g and [M/H]. As for T eff , also spectroscopic surface gravity values suffer from some level of systematics (Holtzman et al 2015;Valentini et al 2016). In DR14 and subsequent releases, however, the raw ASPCAP values have been carefully calibrated using precise log g values delivered by the CoRoT and Kepler asteroseismic missions (see Holtzman et al 2018 for details).…”

“…One of the most precise and widely used anchors in the context of spectroscopic surveys is the asteroseismic surface gravity scale defined by the seismic scaling relations for red giant stars (e.g. Holtzman et al 2015;Valentini et al 2016Valentini et al , 2017.…”

The K2 Galactic Caps Project – going beyond the Kepler field and ageing the Galactic disc

“…The use of multiple sources of spectroscopic data is excellent for maximising the yield of targets, but comes with its own complexities. There is often little consistency between survey observations, with observations of different spectral domains and resolutions common (Valentini et al 2016;Jofré et al 2018). In addition, each survey has a set of unique selection biases that need to be considered, which can manifest in systematic parameter trends (e.g.…”