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arXiv · 2601.08872

Explainable Galaxy Interaction Prediction with Hybrid Attention Mechanisms

Sathwik Narkedimilli·Satvik Raghav·Om Mishra·Mohan Kumar·Aswath Babu H·Tereza Jerabkova·Manish M·Sai Prashanth Mallellu
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Abstract

Galaxy interaction classification remains challenging due to complex morphological patterns and the limited interpretability of deep learning models. We propose an attentive neural ensemble that combines AG-XCaps, H-SNN, and ResNet-GRU architectures, trained on the Galaxy Zoo DESI dataset and enhanced with LIME to enable explainable predictions. The model achieves Precision = 0.95, Recall = 1.00, F1 = 0.97, and Accuracy = 96%, outperforming a Random Forest baseline by significantly reducing false positives (23 vs. 70). This lightweight (0.45 MB) and scalable framework provides an interpretable and efficient solution for large-scale surveys such as Euclid and LSST, advancing data-driven studies of galaxy evolution.

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Sathwik Narkedimilli, Satvik Raghav, Om Mishra, Mohan Kumar, Aswath Babu H, Tereza Jerabkova, Manish M, Sai Prashanth Mallellu. 2026-01-12. Explainable Galaxy Interaction Prediction with Hybrid Attention Mechanisms. https://arxiv.org/abs/2601.08872

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