Predicting Exoplanetary Features With A Residual Model For Uniform And Gaussian Distributions

The advancement of technology has led to rampant growth in data collection across almost every field, including astrophysics, with researchers turning to machine learning to process and analyze this data. One prominent example of this data in astrophysics is the atmospheric retrievals of exoplanets.

In order to help bridge the gap between machine learning and astrophysics domain experts, the 2023 Ariel Data Challenge was hosted to predict posterior distributions of 7 exoplanetary features. The procedure outlined in this paper leveraged a combination of two deep learning models to address this challenge: a Multivariate Gaussian model that generates the mean and covariance matrix of a multivariate Gaussian distribution, and a Uniform Quantile model that predicts quantiles for use as the upper and lower bounds of a uniform distribution.

Training of the Multivariate Gaussian model was found to be unstable, while training of the Uniform Quantile model was stable. An ensemble of uniform distributions was found to have competitive results during testing (posterior score of 696.43), and when combined with a multivariate Gaussian distribution achieved a final rank of third in the 2023 Ariel Data Challenge (final score of 681.57).

Andrew Sweet

Comments: 19 pages, 7 figures, Conference proceedings for ECML PKDD 2023
Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an)
Cite as: arXiv:2406.10771 [astro-ph.EP] (or arXiv:2406.10771v1 [astro-ph.EP] for this version)
Submission history
From: Andrew Sweet
[v1] Sun, 16 Jun 2024 01:07:15 UTC (1,377 KB)
https://arxiv.org/abs/2406.10771

Astrobiology