Inferring the properties of exoplanets from their atmospheres, while confronting low resolution and low signal-to-noise in the context of the quantities we want to derive, poses rigorous demands upon the data collected from observation. Further compounding this challenge is that inferences of exoplanet properties are built from forward models, which can include errors due to incomplete or inaccurate assumptions in atmospheric physics and chemistry. The confluence of observational noise and model error makes developing techniques to identify predictive features that are robust to both low s/n and model error increasingly important for exoplanet science. We demonstrate how both issues can be addressed simultaneously by taking advantage of underutilized multivariate information already present in current atmospheric models, including thermodynamic statistics and reaction network structure. To do so, we provide a case study of the prediction of vertical mixing (parameterized as eddy diffusion) in hot Jupiter atmospheres and show how prediction efficacy depends on what model information is used - e.g. chemical species abundances, network statistics, and/or thermodynamic statistics. We also show how the variables with the most predictive power vary with planetary properties such as temperature and metallicity. Our results demonstrate how inferences built on single metrics do not have utility across all possible use cases. We also show how statistical measures derived from network analyses tend to be better predictors when accounting for the possibility of missing data or observational uncertainty. We discuss future directions applying multivariate and network model information as a framework for increasing confidence in inferences aimed at extracting features relevant to exoplanet atmospheres and future applications to the detection of life on terrestrial worlds.
When is chemical disequilibrium in Earth-like planetary atmospheres a biosignature versus an anti-biosignature? Disequilibria from dead to living worlds
Inferring Late Stage Enrichment of Exoplanet Atmospheres from Observed Interstellar Comets
前两个星际天体的发现意味着，平均而言，每颗恒星都通过将这些天体从宿主系统中喷射出来，为银河系的人口贡献了大量的物质。由于散射是一个混沌的过程，因此应该将相当数量的物质注入到每个系统的内部区域。对于向内运动并与行星相互作用的彗星，这封信估计了作为行星质量和轨道参数函数的吸积或向外散射的物质的比例。这些计算表明，行星的逃逸速度小于其当前的轨道速度将有效地吸积彗星。我们估计了当前太阳系外行星普查成员的吸积效率，并发现行星种群，包括但不限于热和温暖的彗星，次海王星和超级地球，可以有效地捕获彗星。这种彗星富集可能对形成后的大气成分和化学产生重要影响。因此，未来对星际彗星的探测和成分测量将提供对可能富集太阳系外行星子群的物质的直接测量。最后，我们估计的效率，这种富集机制的太阳系外行星，将观察与$\textit{James Webb Space Telescope}$（JWST）。随着JWST目前的运作和这些观测即将到来，它是至关重要的，以调查如何从星际彗星类似物的浓缩可能会影响解释系外行星大气成分。
A Cloudiness Index for Transiting Exoplanets Based on the Sodium and Potassium Lines: Tentative Evidence for Hotter Atmospheres Being Less Cloudy at Visible Wavelengths
Inferring stellar metallicity and elemental abundances from kinematic and spectroscopic data using machine learning -- Implications for exoplanet host stars
Instituto de Astrofísica e Ciências do Espaço, Universidade do Porto（空间科学与天体物理研究所，波尔图大学）；Departamento de Física e Astronomia, Faculdade de Ciências, Universidade do Porto（波尔图大学理学院物理与天文系）；Centro de Astrobiología (CAB), CSIC–INTA（西班牙国家天体生物学中心（CAB），西班牙高等科学研究委员会-国家航空航天技术研究所）；Leibniz-Institüt für Astrophysik Potsdam (AIP)（波茨坦莱布尼茨天体物理研究所）
An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
University of Oxford（牛津大学）；University of Central Florida（中佛罗里达大学）；King’s College London（伦敦国王学院）；Luxembourg Institute of Science and Technology（卢森堡科学技术研究院）；University of Pittsburgh（匹兹堡大学）；NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心）；University of Bern（伯尔尼大学）；Blue Marble Space Institute of Science（蓝大理石空间科学研究所）
Can the Long-Term Impact of Stellar M-Dwarf Flares Alter the Spectral Features of a Giant Gaseous Exoplanet?
SRON, Netherlands Institute for Space Research（荷兰空间研究所）；Institute of Astronomy and Astrophysics, Academia Sinica（中央研究院天文及天文物理研究所）；Leiden Observatory, Leiden University（莱顿大学莱顿天文台）
Flow Matching for Atmospheric Retrieval of Exoplanets: Where Reliability meets Adaptive Noise Levels
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；ETH Zurich（苏黎世联邦理工学院）；ELLIS Institute Tübingen（埃利斯研究所蒂宾根分支）；Max Planck Institute for Gravitational Physics (Albert Einstein Institute)（马克斯·普朗克引力物理研究所（阿尔伯特·爱因斯坦研究所））
A parameterised approach to disequilibrium retrievals in the JWST era: Application to NIRCam observations of HD 189733b
University of Oxford（牛津大学）；University of California, Riverside（加州大学河滨分校）；Institute of Astronomy & Astrophysics, Academia Sinica（中央研究院天文及天文物理研究所）；Université Côte d’Azur（蔚蓝海岸大学）；Observatoire de la Côte d’Azur（蔚蓝海岸天文台）；CNRS（法国国家科学研究中心）；Laboratoire Lagrange（拉格朗日实验室）；Arizona State University（亚利桑那州立大学）
研究系外行星大气非平衡化学对反演结果的影响，通过生成合成 JWST 观测数据，将传输诱导淬灭纳入反演框架，应用于 HD 189733b 观测，发现淬灭及光化学活跃区域初步证据。
