Cumulative theoretical uncertainties in lithium depletion boundary age

Abstract

We performed a detailed analysis of the main theoretical uncertainties affecting the age at the lithium depletion boundary (LDB). To do that we computed almost 12000 pre-main sequence models with mass in the range [0.06, 0.4] M_sun by varying input physics (nuclear reaction cross-sections, plasma electron screening, outer boundary conditions, equation of state, and radiative opacity), initial chemical elements abundances (total metallicity, helium and deuterium abundances, and heavy elements mixture), and convection efficiency (mixing length parameter, alpha_ML). As a first step, we studied the effect of varying these quantities individually within their extreme values. Then, we analysed the impact of simultaneously perturbing the main input/parameters without an a priori assumption of independence. Such an approach allowed us to build for the first time the cumulative error stripe, which defines the edges of the maximum uncertainty region in the theoretical LDB age. We found that the cumulative error stripe is asymmetric and dependent on the adopted mixing length value. For alpha_ML = 1.00, the positive relative age error ranges from 5 to 15 per cent, while for solar-calibrated mixing length, the uncertainty reduces to 5-10 per cent. A large fraction of such an error (about 40 per cent) is due to the uncertainty in the adopted initial chemical elements abundances.

LDB tables for several [Fe/H] values are available at the url: http://astro.df.unipi.it/stellar-models/ldb

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Cited by in corpus (6)

• The Gaia-ESO Survey: Empirical estimates of stellar ages from lithium equivalent widths (EAGLES)
• Lithium depletion boundary, stellar associations, and Gaia
• Gaia-ESO Survey: Role of magnetic activity and starspots on pre-main sequence lithium evolution
• Bayesian calibration of the mixing length parameter ${\alpha }_{ML}$ and of the helium-to-metal enrichment ratio $\Delta Y/\Delta Z$ with open clusters: the Hyades test-bed
• Theoretical predictions of surface light element abundances in protostellar and pre-Main Sequence phase
• Using a neural network approach and starspots dependent models to predict effective temperatures and ages of young stars