Bridging Observation, Data, and Theory for Advancing Exoplanet Detection and Interpretation

Exoplanet science has undergone a profound paradigm shift, evolving from its discovery-driven youth into a data-rich and precision-oriented mature era defined by statistical analysis and detailed characterization. With over 6,000 confirmed planets and the rapid expansion of candidates enabled by machine learning, next-generation facilities like the James Webb Space Telescope (JWST) and upcoming extreme precise radial velocity (EPRV) spectrographs are pushing detection boundaries. However, fundamental challenges remain unresolved, including the aliasing of stellar magnetic activity, systematic biases introduced by telluric lines, Transit Timing Variations (TTV) dynamical inversions, and observational selection effects. Addressing these issues requires synergistic research that integrates precise observations with theoretical modeling within a unified physical framework. Multidimensional studies that fuse methodological innovation, statistical inference, and physical modeling are thus poised to enable the next generation of breakthroughs in exoplanetary research.This Research Topic aims to establish a platform that bridges observations, data analysis, and theoretical modeling to advance exoplanetary science. We focus on four interconnected directions: (1) Methodological breakthroughs in signal-noise decomposition, specifically mitigating stellar magnetic activity and systematic effects to achieve cm/s-level detection precision for rocky planets; (2) Multi-method synergy, integrating radial velocity, transits, TTVs, astrometry, direct imaging, and microlensing for robust planetary characterization; (3) Statistical inference of formation and evolution mechanisms based on the expanding exoplanet census, examining correlations with host star properties and environmental factors; and (4) Physical modeling of habitability and biosignatures, including star-planet magnetic interactions and atmospheric retention.By highlighting methodological innovations and multi-scale integrated research, this topic strives to drive the field's transition from being discovery-centric to characterization- and interpretation-oriented. We particularly encourage the organic integration of data-driven approaches (such as machine learning and Bayesian frameworks) with physics-based modeling, fostering a comprehensive understanding that connects individual system analyses with population-level trends. We welcome original research, perspectives, and reviews covering all aspects of exoplanet detection, characterization, and stellar magnetic activity analysis. Topics of interest include, but are not limited to:1) EPRV Methodologies: Stellar activity mitigation (e.g., Gaussian Processes) and high-precision calibration.2) Stellar Physics: Activity cycles, rotation-magnetic-wind coupling, and impacts on habitability.3) Statistical Studies: Planet-star-environment correlations and occurrence rates.4) Theoretical Modeling: Planet formation, migration, and orbital evolution compared with observed distributions.5) Atmospheric Characterization: Spectroscopy (e.g., JWST), phase curves, and 3D circulation.6) Extreme Systems: Ultra-short period, free-floating planets, and exotic hosts.7) Data-Driven Methods: Machine learning, Bayesian inference, and synergy with physical models.By integrating methodological innovation with multi-scale research, this Research Topic seeks to accelerate the field’s transformation toward characterization-focused exoplanet science. Topic editors: Dong-Yang Gao, Fei Zhao, Xiang-Ning Su Source: https://www.frontiersin.org/research-topics/81677/bridging-observation-data-and-theory-for-advancing-exoplanet-detection-and-interpretation