Joint Survey Data Mining for Dark Matter-Exoplanet Correlations

Joint survey processing (Chary et al. 2019) enables pixel-level dataset fusion, but its potential for cross-domain discovery is untapped. This research would mine combined datasets (e.g., dark matter maps from LSST + exoplanet orbits from TESS/PLATO) to test whether exoplanets in high dark matter density regions exhibit anomalous traits (e.g., eccentricities, compositions). Techniques like contrastive learning could align features across domains, while accounting for biases via the FAIR challenge framework (Campolongo et al. 2025). Unlike Bugueño et al.'s (2018) supervised exoplanet classification, this would be unsupervised and hypothesis-generating. For instance, it might reveal that exoplanets near galaxy clusters have higher heavy-element content—potentially due to dark matter-enhanced accretion. This creates a novel synthesis: using cosmology to explain exoplanet diversity, and vice versa.

References:

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