J/MNRAS/523/802 EW(Li) and ages of < 1 M☉ 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.
<Mon. Not. R. Astron. Soc. 523, 802-824 (2023)>
=2023MNRAS.523..802J 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 Å
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 6708Å
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 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 γ and τ indices) were rejected as
probable giant stars, and anything with EW(Li) < -300 mÅ,
EWLi > 800 mÅ or an uncertainty in EW(Li) > 300 Å 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 τ 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
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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).
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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 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 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 2022MNRAS.509.1664J, Cat. J/MNRAS/509/1664)
and Franciosini et al.
(2022A&A...659A..85F 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 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 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 (GBP-GRP)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 χ2 of best-fitting isochrone
of EWm relative to the the measured EW(Li)
using model values of dispersion in EW and
ρm_ (Reduced_Χ2)
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History:
From electronic version of the journal
License: CC-BY-4.0
(End) Luc Trabelsi [CDS] 31-Jul-2026