Unlocking Insights from Telescope Data: Advancements and Challenges in Characterizing Extrasolar Atmospheres

With the increasing detection of exoplanets and brown dwarfs and spectroscopic characterization of their atmospheres, our knowledge of extrasolar systems is expanding significantly. We are on the verge of a revolutionary era in space exploration, thanks to advancements in telescopes and probes such as the James Webb. These remarkable instruments collect an enormous amount of data from extrasolar atmospheres. However, it requires an accurate understanding of the chemistry and physics relevant to their environments to integrate it into our radiative models for interpreting observational spectra. Without an accurate interpretation of this data, space missions will not be fully accomplished.

In this context, we provide an overview of the current challenges and advancements in the theoretical and laboratory astrophysics field. This includes molecular spectroscopic measurements, theoretical ab initio studies, laboratory simulation of gases and clouds, radiative transfer modeling, and recent initiatives involving deep neural networks to extract insights from observational data. Recent attempts at developing systematic machine learning and deep learning models for characterizing and quantifying the chemical composition and (exo-)planetary parameters of extrasolar objects will be discussed as well.