Cross-Domain Anomaly Detection Framework for Cosmology-Exoplanet Synergies

Current anomaly detection methods operate in isolation—Springer et al.'s EagleEye (2025) excels in localized density anomalies, while Muthukrishna et al. (2021) focus on transient light curves. This research would integrate these approaches, creating a cross-domain anomaly detection system that identifies correlated deviations. For example, unexpected CMB cold spots (Wang 2016) could be cross-referenced with exoplanet orbital anomalies in the same sky region, potentially revealing shared underlying physics (e.g., dark matter interactions or interstellar medium effects). Unlike existing single-domain tools, this would use graph neural networks to model spatial and temporal dependencies across datasets, leveraging the FAIR challenges (Campolongo et al. 2025) for training. The novelty lies in treating cosmological and exoplanetary anomalies as interconnected signals, not isolated events—potentially uncovering new fundamental physics.

References:

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