I am a second-year PhD at the Ludwig Maximilian University (University Observatory) Munich in the Theoretical Astrophysics of Extrasolar Planets group. I’m currently working on the application of atmospheric retrieval methods involving machine learning techniques to the atmospheres of brown dwarfs and directly imaged exoplanets. In doing so, I’m explicitly interested in combining Brown Dwarf theory with astrostatistics methods like Bayesian statistics.
My ADS library
Retrieval Study of Brown Dwarfs Across the L-T Sequence
We performed a large suite of atmospheric retrievals on a curated sample of 19 brown dwarfs spanning the entire L-T spectral types to look for trends in their properties and investigate issues with the current techniques used to examine emission spectra.
Intercomparison of Brown Dwarf Model Grids and Atmospheric Retrieval Using Machine Learning
Using the supervised machine learning method of the random forest, we studied the information content of 14 previously published model grids of brown dwarfs (from 1997 to 2021) and explored the parameter space of generated posteriors for our curated dataset including benchmark brown dwarfs (Gl 570D,
epsilon Indi Ba and Bb) as well as a sample of 19 L and T dwarfs; this sample was previously analyzed in Lueber et al. (2022) using traditional Bayesian methods (nested sampling).