Welcome to the GSFC Exoplanet Modeling and Analysis Center (EMAC)

EMAC serves as a catalog, repository and integration platform for modeling and analysis resources focused on the study of exoplanet characteristics and environments. EMAC is a key project of the GSFC Sellers Exoplanet Environments Collaboration (SEEC).

If you've used EMAC in any part of your research, please cite our RNAAS paper either in your methods section or in the "Software used" portion of any manuscripts; see the FAQ for more information.

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If you make use of tools linked or hosted on EMAC: please use the following statement in your publication acknowledgements: “This research made use of the NASA Exoplanet Modeling and Analysis Center (EMAC), which is funded by the NASA Planetary Science Division’s Internal Scientist Funding Model.”

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The P.I. is Avi Mandell, and the Deputy P.I. is Eric Lopez; more information on EMAC staffing and organization can be found on Our Team page.

ODUSSEAS: A Machine Learning Tool to Derive Effective Temperature and Metallicity for M Dwarf Stars

A. Antoniadis-Karnavas, S. G. Sousa, E. Delgado-Mena, N. C. Santos, G. D. C. Teixeira, V. Neves

EMAC: 2207-119 EMAC 2207-119 2207-119
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https://emac.gsfc.nasa.gov?cid=2207-119
ODUSSEAS is an automatic computational tool able to quickly and reliably derive the Teff and [Fe/H] of M dwarfs using optical spectra obtained by different spectrographs with different resolutions. It is based on the measurement of the pseudo equivalent widths for more than 4000 stellar absorption lines and on the use of the machine learning Python package “scikit-learn” for predicting the stellar parameters. It is able to derive parameters accurately and with high precision, having precision errors of ~30 K for Teff and ~0.04 dex for [Fe/H]. The results are consistent for spectra with resolutions of between 48000 and 115000 and a signal-to-noise ratio above 20.

Code Language(s): Python3

Last updated: Sep. 24, 2024