Kameron Gausling

San Francisco State University

“A Calibrated Macroturbulence Relationship for Precise Stellar Properties”

I present the preliminary results of the spectral analysis of >300 slowly rotating FGKM stars utilizing Python Spectroscopy Made Easy (PySME). Well-fitting models of stellar spectra are crucial for obtaining precise stellar abundances, constraining the physical properties of exoplanets, and investigating the chemical evolution of the Galaxy. The precision of these parameters and of information such as stellar obliquity and stellar metallicity can be limited by degeneracies between v sini and v_mac. The selected stars are ideal for breaking this degeneracy, as the velocity broadening of slowly rotating stars is dominated by v_mac. All spectra were recorded using the EXPRES spectrograph at the Lowell Discovery Telescope. To date, I have developed a spectral fitting pipeline that utilizes PySME to fit a synthetic spectrum to the stellar spectra and estimate stellar parameters. In future work, I will determine a calibrated empirical relationship between v_mac and T_eff to improve constraints on v sini and v_mac. This relationship would allow for the derivation of more precise stellar parameters for stars with non-negligible rotation.

ABSTRACT

Precisely estimating the properties of stars, such as temperature and composition, is imperative in understanding the formation of exoplanets, as well as the chemical evolution of our Galaxy. My work focuses on breaking the parameter degeneracy between macroturbulent broadening and rotational velocity by analyzing the spectra of slowly rotating stars. This will help to place tighter constraints on the stellar properties obtained through spectral fitting.
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