Spectroscopic Parameters and atmosphEric ChemIstriEs of Stars (SPECIES) is a code meant to compute stellar parameters and abundances by using high resolution echelle spectra. The whole calculation is done automatically, with the stellar spectrum being the only mandatory input. It handles data from several spectrographs (HARPS, FEROS, UVES, HIRES, PFS, CORALIE so far) and more than one star at the same time.
Please cite Soto & Jenkins 2018, http://adsabs.harvard.edu/abs/2018A%26A...615A..76S if your use SPECIES for your work.
Authors: Maritza Soto and James Jenkins.
(SPECIES went through some major changes. If you installed SPECIES before November 2017, please update all the packages and make sure you include the MOOGPATH in your bash file (see the User manual). It is not necessary to reinstall ARES nor MOOG).
Computation of parameters
The atmospheric parameters (temperature, metallicity, surface gravity and microturbulence velocity) are computed by measuring the equivalent widths of several iron lines, done using ARES (Sousa et al. 2008). These are then given to MOOG (Sneden 1973), which solves the radiative transfer equation assuming local thermodynamic equilibrium (LTE) conditions. The atmospheric parameters are then derived through an iterative process that stops when no correlation is found between the line abundances with the excitation potential and the equivalent width. The atmospheric models are obtained from interpolation through a grid of ATLAS9 models (Castelli & Kurucz 2004).
Chemical abundances are obtained for 11 elements: Na, Mg, Al, Si, Ca, Ti, Cr, Mn, Ni, Cu and Zn. Rotational and macroturbulence velocity are found by temperature relations, and line fitting, measuring the profiles of five absorption lines.
Finally, mass and age are computed by interpolating throught a grid of Darthmouth isochrones, using the metallicity, temperature and surface gravity found previously, as well as their uncertainties, as priors. It uses a bayesian approach to obtain the final values, which are taken as the mean and standard deviation of the Gaussian profile adjusted to the resulting chains.
More detail about the method and results from SPECIES can be found in Soto & Jenkins 2017, in prep.
Installation and use
SPECIES is written mostly in Python, with the exception of ARES and MOOG, written in C++ and fortran, respectively. Installation instructions for ARES and MOOG, as well as required packages, are found in the User Manual, included in this module. In the User Manual is also explained the usage of SPECIES, as well as the input and output files.