Creating and using large grids of pre-calculated model atmospheres for rapid analysis of stellar spectra

Abstract

We present a database of 45,000 atmospheric models (which will become 80,000 models by the end of the project) with stellar masses between 9 and 120 M${}_{\odot }$, covering the region of the OB main sequence and W-R stars in the H-R diagram. The models were calculated using the ABACUS I supercomputer and the stellar atmosphere code CMFGEN. The parameter space has 6 dimensions: the effective temperature $T_{\mathrm{eff}}$, the luminosity $L$, the metallicity $Z$, and three stellar wind parameters, namely the exponent $\beta$, the terminal velocity $V_{\infty }$, and the volume filling factor $F_{cl}$. For each model, we also calculate synthetic spectra in the UV (900-2000 Angstroms), optical (3500-7000 Angstroms), and near IR (10000-30000 Angstroms) regions. To facilitate comparison with observations, the synthetic spectra were rotationally broaden using ROTIN3, by covering $v$ sin $i$ velocities between 10 and 350 km/s with steps of 10 km/s, resulting in a library of 1 575 000 synthetic spectra. In order to demonstrate the benefits of employing the databases of pre-calculated models, we also present the results of the re-analysis of $\epsilon$ Ori by using our grid.

arXiv comment11 pages, 6 figures. arXiv admin note: substantial text overlap with arXiv:2009.10879

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Cited by in corpus (1)

• Stellar Spectra Models Classification and Parameter Estimation Using Machine Learning Algorithms