Empirical calibrations of optical absorption line indices based on the stellar library MILES

TL;DR

This paper presents new empirical calibration functions for 25 optical absorption line indices based on the MILES stellar library, improving accuracy and providing tools for stellar population analysis.

Contribution

It introduces updated fitting functions for optical line indices using MILES, with better sampling, higher signal-to-noise, and flux calibration, enhancing stellar population modeling accuracy.

Findings

Errors in individual index measurements are smaller in MILES than in Lick/IDS.

Residuals are mainly due to stellar parameter uncertainties.

Offsets between flux-calibrated libraries and Lick/IDS vary by index.

Abstract

Stellar population models of absorption line indices are an important tool for the analysis of stellar population spectra. They are most accurately modelled through empirical calibrations of absorption line indices with the stellar parameters effective temperature, metallicity, and surface gravity, the so-called fitting functions. Here we present new empirical fitting functions for the 25 optical Lick absorption line indices based on the new stellar library MILES. The major improvements with respect to the Lick/IDS library are the better sampling of stellar parameter space, a generally higher signal- to-noise, and a careful flux calibration. In fact we find that errors on individual index measurements in MILES are considerably smaller than in Lick/IDS. Instead we find the rms of the residuals between the final fitting functions and the data to be dominated by errors in the stellar parameters. We provide fitting functions for both Lick/IDS and MILES spectral resolutions, and compare our results with other fitting functions in the literature. A Fortran 90 code is available online in order to simplify the implementation in stellar population models. We further calculate the offsets in index measurements between the Lick/IDS system to a flux calibrated system. For this purpose we use the three libraries MILES, ELODIE, and STELIB. We find that offsets are negligible in some cases, most notably for the widely used indices Hbeta, Mgb, Fe5270, and Fe5335. In a number of cases, however, the difference between flux calibrated library and Lick/IDS is significant with the offsets depending on index strengths. Interestingly, there is no general agreement between the three libraries for a large number of indices, which hampers the derivation of a universal offset between the Lick/IDS and flux calibrated systems.