Atmospheric retrievals (AR) of exoplanets typically rely on a combination of a Bayesian inference technique and a forward simulator to estimate atmospheric properties from an observed spectrum. A key component in simulating spectra is the pressure-temperature (PT) profile, which describes the thermal structure of the atmosphere. Current AR pipelines commonly use ad hoc fitting functions here that limit the retrieved PT profiles to simple approximations, but still use a relatively large number of parameters. In this work, we introduce a conceptually new, data-driven parameterization scheme for physically consistent PT profiles that does not require explicit assumptions about the functional form of the PT profiles and uses fewer parameters than existing methods. Our approach consists of a latent variable model (based on a neural network) that learns a distribution over functions (PT profiles). Each profile is represented by a low-dimensional vector that can be used to condition a decoder network that maps $P$ to $T$. When training and evaluating our method on two publicly available datasets of self-consistent PT profiles, we find that our method achieves, on average, better fit quality than existing baseline methods, despite using fewer parameters. In an AR based on existing literature, our model (using two parameters) produces a tighter, more accurate posterior for the PT profile than the five-parameter polynomial baseline, while also speeding up the retrieval by more than a factor of three. By providing parametric access to physically consistent PT profiles, and by reducing the number of parameters required to describe a PT profile (thereby reducing computational cost or freeing resources for additional parameters of interest), our method can help improve AR and thus our understanding of exoplanet atmospheres and their habitability.
Exoplanetary atmospheres retrieval via a quantum extreme learning machine
Università degli Studi di Palermo, Dipartimento di Fisica e Chimica - Emilio Segrè（意大利帕尔马大学物理与化学系）；Dipartimento di Fisica e Astronomia “Galileo Galilei”, Università degli Studi di Padova（意大利帕多瓦大学伽利略·伽利莱物理与天文学系）；INAF Osservatorio Astronomico di Padova（意大利国家天体物理研究所帕多瓦天文台）
Measurements and predictions of H2 pressure-broadening coefficients of CO2 absorption lines for exoplanet atmosphere studies
Laboratoire de Météorologie Dynamique, IPSL, Sorbonne Université, École Normale Supérieure, Université PSL, École polytechnique, Institut Polytechnique de Paris, CNRS（动态气象实验室，IPSL，索邦大学，巴黎高等师范学院，巴黎文理研究大学，巴黎综合理工学院，巴黎理工学院，法国国家科学研究中心）；Université de Tunis, Laboratoire de Spectroscopie et Dynamique Moléculaire, École Nationale Supérieure d’Ingénieurs de Tunis（突尼斯大学，分子光谱与动力学实验室，突尼斯国立工程师学校）；Univ Paris Est Créteil and Université Paris Cité, CNRS, LISA（马恩河谷大学和巴黎西岱大学，法国国家科学研究中心，大气、海洋和陆地环境观测与研究实验室）
Exoplanet atmospheres and demographics in the 2040s
European Southern Observatory（欧洲南方天文台）；Observatoire de Paris（巴黎天文台）；Leiden Observatory（莱顿天文台）；Max-Planck-Institute for Extraterrestrial Physics（马普地外物理研究所）；University of Exeter（埃克塞特大学）
Pyrat Bay 2.0: an Upgraded Framework for Exoplanet Atmosphere Modeling in the JWST Era
Space Research Institute, Austrian Academy of Sciences（奥地利科学院空间研究所）；INAF – Osservatorio Astrofisico di Torino（意大利国家天体物理研究所都灵天文台）；Department of Physics, New York University Abu Dhabi（阿布扎比纽约大学物理系）；Center for Astrophysics and Space Science (CASS), New York University Abu Dhabi（阿布扎比纽约大学天体物理学与空间科学中心）；Instituto de Astrofísica de Andalucía - CSIC（安达卢西亚天体物理研究所-西班牙国家研究委员会）
Pyrat Bay 2.0框架新增chemcat等功能，经代码验证后用于气态巨行星大气研究，发现JWST数据可准确恢复大气垂直丰度变化，忽略该变化会导致丰度约束偏差。
Influence of CO versus CH$_4$ on organic haze formation in atmospheres of diverse terrestrial exoplanets
University of Science and Technology of China（中国科学技术大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Space Telescope Science Institute（太空望远镜科学研究所）；NASA Goddard Space Flight Center（美国宇航局戈达德太空飞行中心）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）
