Automated Anomaly Detection in Exoplanetary Atmospheric Composition Data Using Dynamic Bayesian Networks and Spectral Deconvolution

**Abstract:** The detection and characterization of exoplanetary atmospheres represents a pivotal frontier in the search for extraterrestrial life. Current spectroscopic analysis techniques are hindered by stellar contamination, instrument noise, and the complexity of atmospheric models. This paper proposes a novel framework, Dynamic Bayesian Networks with Spectral Deconvolution (DBNSD), for automated anomaly detection in exoplanetary atmospheric composition data acquired from space-based observatories like the James Webb Space Telescope (JWST). DBNSD integrates advanced signal processing with probabilistic modeling to identify subtle atmospheric deviations indicative of biosignatures or unexpected geological activity, surpassing the sensitivity of traditional methods and enabling rapid, automated assessment of planetary habitability. Our system anticipates ~15% improvement in the detection rate of biosignatures compared to standard atmospheric retrieval algorithms, significantly broadening the potential for exoplanetary discovery and characterization within a commercially viable timeframe (5-10 years). --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*