Malena Rice

Stellar spectroscopy


Accurate measurements of stellar properties are critical to identify trends and correlations that inform our understanding of how planetary systems form. These stellar properties can be obtained with high fidelity through forward modeling with programs such as Spectroscopy Made Easy (SME), with the caveat that such programs can be computationally expensive and thus are not always tractable for use with very large datasets.

In my work, I have applied the generative modeling code The Cannon to classify stellar spectra in order to better understand their properties. The Cannon efficiently derives ''labels'' (stellar properties and abundances) from stellar spectra using supervised machine learning methods. I am especially interested in datasets that can help us to learn more about stars that have been part of planet search campaigns, with an ultimate goal of providing large, uniformly determined sets of stellar labels for use in demographic studies of exoplanet systems.

To learn more about my past work in this subfield, read my research highlight here.

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