<tt>MSTAT</tt>: A Python Tool for Estimating Spectral Types of Stellar Companions from Differential Photometry

Abstract A python-based tool for reading the table of absolute magnitudes for dwarf (O-Y) stars as compiled by M. J. Pecaut &amp; E. E. Mamajek and using this table to determine the most likely spectral type for a companion star has been developed. The tool is designed to take differential magnitudes between the primary star and the secondary star and return the most likely spectral type associated with secondary star. Primarily designed for use with companion detection done by high resolution imaging as part of the TESS follow-up observation program, the python code ( https://github.com/mfelsmann/mamajek_spectral_analysis ) can be run from a notebook or from the command line and has been made available to community for public use.

Authors

Publication Details

Journal
Research Notes of the AAS
Published
2025-08-29
DOI
https://doi.org/10.3847/2515-5172/adff76
Primary Topic
Stellar, planetary, and galactic studies
Type
article
Field-Weighted Citation Impact
0.00
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article

<tt>MSTAT</tt>: A Python Tool for Estimating Spectral Types of Stellar Companions from Differential Photometry

David R. Ciardi, Miranda Felsmann
Research Notes of the AAS
Stellar, planetary, and galactic studies
article

<tt>MSTAT</tt>: A Python Tool for Estimating Spectral Types of Stellar Companions from Differential Photometry

David R. Ciardi, Miranda Felsmann
article en

Abstract

Abstract A python-based tool for reading the table of absolute magnitudes for dwarf (O-Y) stars as compiled by M. J. Pecaut &amp; E. E. Mamajek and using this table to determine the most likely spectral type for a companion star has been developed. The tool is designed to take differential magnitudes between the primary star and the secondary star and return the most likely spectral type associated with secondary star. Primarily designed for use with companion detection done by high resolution imaging as part of the TESS follow-up observation program, the python code ( https://github.com/mfelsmann/mamajek_spectral_analysis ) can be run from a notebook or from the command line and has been made available to community for public use.

Research Notes of the AASVol. 9(8)
Openalex Percentile: Top 84%
Stellar, planetary, and galactic studies
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