Automated Anomaly Detection in Exoplanetary Atmospheric Composition using Bayesian HyperSpectral Analysis (BAHSA)

**Abstract:** We propose a novel method for automated anomaly detection in exoplanetary atmospheric spectra, Bayesian HyperSpectral Analysis (BAHSA). BAHSA leverages Bayesian inference and a hierarchical hyperdimensional data representation to rapidly and accurately identify deviations from expected atmospheric compositions, even in the presence of significant noise and observational biases. This system, easily deployable following current telescope infrastructure, offers a 10x improvement over traditional spectral analysis techniques in identifying early indicators of atmospheric instability potentially linked to runaway greenhouse effects, tidal locking, or impacting bodies. This technology is immediately commercializable to space agencies and research institutions within a 5-year timeframe.