Exploring exoplanets has transformed our understanding of the universe by
revealing many planetary systems that defy our current understanding. To study
their atmospheres, spectroscopic observations are used to infer essential
atmospheric properties that are not directly measurable. Estimating atmospheric
parameters that best fit the observed spectrum within a specified atmospheric
model is a complex problem that is difficult to model. In this paper, we
present a multi-target probabilistic regression approach that combines deep
learning and inverse modeling techniques within a multimodal architecture to
extract atmospheric parameters from exoplanets. Our methodology overcomes
computational limitations and outperforms previous approaches, enabling
efficient analysis of exoplanetary atmospheres. This research contributes to
advancements in the field of exoplanet research and offers valuable insights
for future studies.

本研究利用深度学习和反向建模技术相结合的多模态架构，提出一种多目标概率回归方法，从外行星中提取大气参数，克服了计算限制，胜过先前的方法，在外行星大气的高效分析上做出了重要贡献，并为未来研究提供了有价值的见解。