J/MNRAS/523/802 EW(Li) and ages of < 1 M_{sun}_ stars in OpC (Jeffries+, 2023) ================================================================================ The Gaia-ESO Survey: empirical estimates of stellar ages from lithium equivalent widths (EAGLES). Jeffries R.D., Jackson R.J., Wright N.J., Weaver G., Gilmore G., Randich S., Bragaglia A., Korn A.J., Smiljanic R., Biazzo K., Casey A.R., Frasca A., Gonneau A., Guiglion G., Morbidelli L., Prisinzano L., Sacco G.G., Tautvaisiene G., Worley C.C., Zaggia S. =2023MNRAS.523..802J (SIMBAD/NED BibCode) ================================================================================ ADC_Keywords: Clusters, open ; Stars, G-type ; Stars, K-type ; Stars, M-type ; Abundances, [Fe/H] ; Stars, ages ; Positional data ; Equivalent widths ; Spectroscopy ; Optical ; Effective temperatures ; Photometry Keywords: stars: abundances; stars: evolution; stars: fundamental parameters stars: pre-main-sequence; open clusters and associations: general Abstract: We present an empirical model of age-dependent photospheric lithium depletion, calibrated using a large homogeneously analysed sample of 6200 stars in 52 open clusters, with ages from 2 to 6000 Myr and -0.3 < [Fe/H] < 0.2, observed in the Gaia-ESO spectroscopic survey. The model is used to obtain age estimates and posterior age probability distributions from measurements of the Li I 6708 {AA} equivalent width for individual (pre) main-sequence stars with 3000 < Teff/K < 6500, a domain where age determination from the HR diagram is either insensitive or highly model-dependent. In the best cases, precisions of 0.1 dex in log age are achievable; even higher precision can be obtained for coeval groups and associations where the individual age probabilities of their members can be combined. The method is validated on a sample of exoplanet-hosting young stars, finding agreement with claimed young ages for some, but not others. We obtain better than 10 per cent precision in age, and excellent agreement with published ages, for seven well-studied young moving groups. The derived ages for young clusters (<1 Gyr) in our sample are also in good agreement with their training ages, and consistent with several published model-insensitive lithium depletion boundary ages. For older clusters, there remain systematic age errors that could be as large as a factor of 2. There is no evidence to link these errors to any strong systematic metallicity dependence of (pre) main-sequence lithium depletion, at least in the range -0.29 < [Fe/H] < 0.18. Our methods and model are provided as software - 'Empirical AGes from Lithium Equivalent widthS' (EAGLES). Description: Low-mass stars form the bulk of the Galactic population. Their long lifetimes make them witnesses and tracers of the assembly of the Galaxy, and the dynamical and chemical evolution of its various component structures. Knowing the ages of stars is an essential part of these investigations and also critical for exploring the formation and development of their exoplanetary systems. However, stellar age is not a directly observable parameter; age estimations are made in various ways and each technique has its own advantages, disadvantages and range of applicability. Low-mass stars form the bulk of the Galactic population. Their long lifetimes make them witnesses and tracers of the assembly of the Galaxy, and the dynamical and chemical evolution of its various component structures. Knowing the ages of stars is an essential part of these investigations and also critical for exploring the formation and development of their exoplanetary systems. However, stellar age is not a directly observable parameter; age estimations are made in various ways and each technique has its own advantages, disadvantages and range of applicability. This paper focuses on photospheric lithium, which has a long history as a, mostly intrinsic, age indicator and probe of stellar interiors. More recent reviews of Li as an age indicator. In this paper we exploit the very large, homogeneous set of spectroscopic observations of low-mass stars in open clusters, that were obtained as part of the Gaia-ESO Survey GES. It provides a much improved calibration of the relationship between lithium, Teff and age that is used to construct an empirical model of Li depletion. Our objectives are (i) to quantify how precisely age can be determined for stars of various ages and Teff; (ii) to find how much more precision can be obtained by fitting groups of stars that are assumed to be coeval; (iii) to validate the method by finding the ages for a selection of young stars and associations that were not observed as part of GES; and (iv) to explore to what extent Li depletion is determined only by age and Teff or whether third parameters such as chemical composition might be confounding or contributing factors. The GES data used in this paper come from the sixth internal data release GESiDR6, which includes a library of stacked spectra of cluster targets obtained with the FLAMES-GIRAFFE spectrograph (with HR15N order-sorting filter) and FLAMES-UVES spectrograph (centred at 580 nm) on the 8-m UT2-Kueyen telescope of the Very Large Telescope together with the GESiDR6 Parameter Catalogue. The latter contains values of Teff, metallicity ([Fe/H]), radial velocity (RV), gravity (log g) and Teff- and gravity-sensitive spectral index for a large proportion of targets, which were derived by the GES Working Groups. In this section we select a subset of 6200 stars that were identified as very probable members of the open clusters and associations observed by GES and estimate the equivalent width of their Li i 6708{AA} feature (EWLi) from the GES spectra. These data, referred to as the training data, are used to define an empirical relationship between EW(Li), Teff and age. The training data were drawn from 52 clusters with metallicities in the range -0.3 < [Fe/H] < 0.2 and ages listed in table1.dat. Targets from the designated clusters were selected with 2900 < Teff/K < 6600, a reported value of RV and a probability of cluster membership >0.9 in Jackson et al. (2022MNRAS.509.1664J, Cat. J/MNRAS/509/1664, J22). Note that these members were selected on the basis of their kinematics, not of their chemical (including Li abundance) or photometric properties. Any star with Teff > 4000 K and logg < 3.4 (or its equivalent calculated from the {gamma} and {tau} indices) were rejected as probable giant stars, and anything with EW(Li) < -300 m{AA}, EWLi > 800 m{AA} or an uncertainty in EW(Li) > 300 {AA} were rejected as poor data. These selections left us with 6200 individual targets, of which 6106 have GIRAFFE spectra, 203 have UVES spectra and 109 have both. Details of targets in the training set are shown in table2.dat where the membership probability and Teff are taken from columns headed Teffp and P3D in table 3 of J22 for 7812 targets. For the majority (93 per cent) of targets, Teffp is the effective temperature reported in the GESiDR6 Parameter Catalogue, otherwise it was inferred from the spectroscopic temperature index {tau} measured from the target spectra. The acceptance criteria of P3D > 0.9 gives an average cluster membership probability of P3D > 0.994 and the expected contamination level is only about 37 in the total sample of 6200 cluster stars. Finally as results to model fitting described in section 2 and 3, we present for 40 clusters comparisons between measured and fitted EW and ages in table4.dat File Summary: -------------------------------------------------------------------------------- FileName Lrecl Records Explanations -------------------------------------------------------------------------------- ReadMe 80 . This file table1.dat 52 51 Training data drawn from 52 cluster ages and metallicities of literature references table2.dat 97 7812 *Training data used to calibrate the empirical relations of EWLi as a function of Teff and age for 6200 cluster members observed as part of the Gaia-ESO survey table4.dat 47 40 Training and most probable estimated ages of GES clusters from model and fit work described in section 2 and 3 -------------------------------------------------------------------------------- Note on table2.dat: A total of 109 targets have both GIRAFFE (Filter = 665.0 nm) and UVES (Filter = 580.0 nm) measurements. Also shown are data for 1503 GES targets with low (<0.01) probabilities of cluster membership classified as field stars (see Section 2.3). -------------------------------------------------------------------------------- See also: J/MNRAS/513/5727 : Lithium study with GESiDR6's PMS stars cluster (Binks+,2022) J/MNRAS/509/1664 : Membership study of 70 stars clusters (Jackson+, 2022) J/MNRAS/504/356 : Updated parameters of 1743 open clusters (Dias+, 2021) J/MNRAS/500/1158 : Rotation and lithium depletion of M35 dwarfs (Jeffries+, 2021) J/MNRAS/328/45 : Late-type stars members of young groups (Montes+, 2001) J/A+A/676/A129 : Gaia-ESO catalogue version 5.1 (Hourihane+, 2023) J/A+A/668/A49 : Lithium curves of growth (Franciosini+, 2022) J/A+A/666/A121 : Cluster target Giraffe HR15N RV precision (Randich+, 2022) J/A+A/664/A70 : Lithium depletion boundary in stellar assoc. (Galindo-Guil+, 2022) J/A+A/659/A85 : Membership and lithium of young clusters (Franciosini+,2022) J/A+A/653/A72 : Stellar parameters and Li abundances from GES iDR6 (Romano+, 2021) J/A+A/643/A71 : Members for 20 open clusters (Gutierrez Albarran+, 2020) J/A+A/613/A63 : Lithium content for 148 Pleiades stars (Bouvier+, 2018) J/A+A/566/A50 : Classification of stellar spectra 644-681nm (Damiani+, 2014) J/A+A/564/A90 : M subdwarfs VLT/UVES high resolution spectra (Rajpurohit+, 2014) J/A+A/541/A150 : Lithium in M67 and Hyades (Pace+, 2012) J/A+A/479/141 : Iz photometry, RV and EW(Li) in IC 4665 (Manzi+, 2008) J/A+A/442/961 : Lithium content of the Galactic Halo stars (Charbonnel+, 2005) J/A+A/372/862 : Lithium abundances in IC 2602 and IC 2391 (Randich+, 2001) J/ApJ/762/88 : Young stellar kinematic group candidate members (Malo+,2013) J/AJ/153/128 : WOCS. LXXV. Hyades&Praesepe stellar lithium data (Cummings+, 2017) Byte-by-byte Description of file: table1.dat -------------------------------------------------------------------------------- Bytes Format Units Label Explanations -------------------------------------------------------------------------------- 1- 14 A14 --- Cluster Cluster name from Randich et al. (2022A&A...666A.121R, Cat. J/A+A/666/A121) 16- 20 F5.2 [Sun] [Fe/H] Average metallicity of the cluster from Randich et al. (2022A&A...666A.121R, Cat. J/A+A/666/A121) 22- 24 I3 --- Nstar The number of members in each cluster used in our analysis (No.Stars) 26- 31 F6.1 Myr AgeJ22 Cluster ages from three different sources from (2022MNRAS.509.1664J, Cat. J/MNRAS/509/1664) and Franciosini et al. (2022A&A...659A..85F, Cat. J/A+A/659/A85) (Lit_ages) 33- 38 F6.1 Myr AgeGES Age from table 3 in Randich et al. (2022A&A...666A.121R, Cat. J/A+A/666/A121) (GES_ages) 40- 45 F6.1 Myr AgeDias ? Age from Dias et al. (2021MNRAS.504..356D, Cat. J/MNRAS/504/356) (Dias_ages) 47- 52 F6.1 Myr AgeMean The geometric mean age for each cluster as seen in section 2.1 (Mean_ages) -------------------------------------------------------------------------------- Byte-by-byte Description of file: table2.dat -------------------------------------------------------------------------------- Bytes Format Units Label Explanations -------------------------------------------------------------------------------- 1- 14 A14 --- Cluster Cluster name (CLUSTER) 16- 31 A16 --- Target Target star name (TARGET) 33- 37 F5.1 nm Wave Wavelength filter value GIRAFFE (Filter = 665.0 nm) and UVES (Filter = 580.0 nm) measurements (FILTER) 39- 47 F9.5 deg RAdeg Right ascension (J2000) (RA) 49- 57 F9.5 deg DEdeg Declination (J2000) (DEC) 59- 64 F6.1 Myr Age Estimated stellar age (AGE) 66- 70 F5.3 --- Mem Probability of cluster membership (MEM) 72- 79 F8.3 mag (BP-RP)0 ?=-999 The gaia (G_BP_-G_RP_)_0_ colour magnitude (BP_RPo) 81- 84 I4 K Teff Effective temperature as described in section 2.1 (TEFF) 86- 91 F6.1 10-4nm EW(Li) Measured equivalent width as described in section 2.1 used to build empirical relations as a function of Teff and age (LiEW) 93- 97 F5.1 10-4nm e_EW(Li) Measurement uncertainty of EW(Li) (e_LiEW) -------------------------------------------------------------------------------- Byte-by-byte Description of file: table4.dat -------------------------------------------------------------------------------- Bytes Format Units Label Explanations -------------------------------------------------------------------------------- 1- 14 A14 --- Cluster Cluster name (Cluster) 16- 19 F4.2 [yr] logAge The mean logage of the training data taken from the table1.dat (Logage) 21- 24 F4.2 [yr] e_logAge Uncertainty of logAge 26 A1 --- l_logAgefit Upper limit flag of logAgefit 28- 32 F5.2 [yr] logAgefit Best-fitting age or limiting value from model described in sections 2 and 3 34- 37 F4.2 [yr] E_logAgefit ? Upper uncertainty of logAgefit 39- 42 F4.2 [yr] e_logAgefit ? Lower uncertainty of logAgefit 44- 47 F4.2 --- Chi2 ? Reduced {chi}2 of best-fitting isochrone of EW_m_ relative to the the measured EW(Li) using model values of dispersion in EW and {rho}m_ (Reduced_{Chi}^2^) -------------------------------------------------------------------------------- History: From electronic version of the journal License: CC-BY-4.0 ================================================================================ (End) Luc Trabelsi [CDS] 31-Jul-2026