English

Survey for Distant Solar Twins (SDST) -- I. EPIC method for stellar parameter measurement

Solar and Stellar Astrophysics 2022-03-02 v1 Instrumentation and Methods for Astrophysics

Abstract

Solar twins are stars of key importance to the field of astronomy and offer a multitude of science applications. Only a small number (200\lesssim200) of solar twins are known today, all of which are relatively close to our Sun (800pc\lesssim800\,pc). The goal of our Survey for Distant Solar Twins (SDST) is to identify many more solar twin and solar analogue stars out to much larger distances (4kpc\sim4\,kpc). In this paper, we present a new method to identify solar twins using relatively low S/NS/N, medium resolving power (R28,000R\sim 28{,}000) spectra that will be typical of such distant targets observed with HERMES on the 3.9m3.9\,m Anglo-Australian Telescope (AAT). We developed a novel approach, namely EPIC, to measure stellar parameters which we use to identify stars similar to our Sun. EPIC determines the stellar atmospheric parameters (effective temperature TeffT_{\mathrm{eff}}, surface gravity logg\log g and metallicity [Fe/H]) using differential equivalent width (EW) measurements of selected spectroscopic absorption features and a simple model, trained on previously analysed spectra, that connects these EWs to the stellar parameters. The reference for the EW measurements is a high S/NS/N solar spectrum which is used to minimise several systematic effects. EPIC is fast, optimised for Sun-like stars and yields stellar parameter measurements with small enough uncertainties to enable spectroscopic identification of solar twin and analogue stars up to 4kpc\sim4\,kpc away using AAT/HERMES, i.e.\ σ(Teff,logg,[Fe/H])=(50K,0.08dex,0.03dex)\sigma\left(T_{\mathrm{eff}}, \log g, \textrm{[Fe/H]}\right) = \left(50\,K, 0.08\,dex, 0.03\,dex\right) on average at S/N=25S/N=25.

Keywords

Cite

@article{arxiv.2202.06469,
  title  = {Survey for Distant Solar Twins (SDST) -- I. EPIC method for stellar parameter measurement},
  author = {Christian Lehmann and Michael T. Murphy and Fan Liu and Chris Flynn and Daniel A. Berke},
  journal= {arXiv preprint arXiv:2202.06469},
  year   = {2022}
}

Comments

Accepted by MNRAS. The current release of the EPIC algorithm is available at https://dx.doi.org/10.5281/zenodo.6029325

R2 v1 2026-06-24T09:34:31.295Z