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).

通过结合多变量高斯模型和均匀分位数模型，使用集成的均匀分布和多变量高斯分布来预测 7 个太阳系外行星特征的后验分布，取得了第三名的成绩 (最终得分为 681.57)。