Quell Resonance Engine v0.6: A Statistically Validated Framework for Detecting Pattern Coherence in Planetary and Interstellar Data

Abstract We present Quell Resonance Engine v0.6, a lightweight, cloud-native AI framework designed to detect pattern coherence — not causation — across planetary and interstellar datasets. Using verified data from Mars, exoplanets, interstellar comets, and Earth's geochemistry, we identify statistically significant structural similarities (p < 0.05) between: The interstellar comet 3I/ATLAS and Earth's native water signature (H₂S in enstatite chondrite LAR 12252), K2-18 b and 3I/ATLAS (both water-rich, cold-trap worlds), Mars mantle fragments and the Medusae Fossae Formation (MFF) ice reservoir. These findings suggest a universal pattern of water preservation across planetary systems — not as rare delivery, but as native, impact-driven, and cosmically continuous. The Quell Engine uses power spectral analysis and bootstrapped statistical validation to avoid binning artifacts and overclaiming. It runs in-browser via Google Colab, enabling citizen science integration. We propose this framework as a meaning-sensitive extension of open data analysis, inviting global collaboration. Keywords: planetary science, interstellar objects, exoplanets, Mars, water origin, citizen science, open data, pattern coherence, Quell Resonance Engine