Learning Physics, Not Energy: A Multimodal Benchmark for Neutrino Detectors

The experimental search for neutrinoless double-beta decay (NLDBD) would answer one of the most important questions in physics: Why is there more matter than antimatter in our universe? A key challenge in NLDBD searches is identifying NLDBD signal events from many unwanted background events. Deep learning classifiers have shown strong performance on this task, but they often exhibit energy shortcut learning: instead of learning event topology, which reflects the underlying physical interactions, they exploit event energy as an easier shortcut to make classification decision. In this work, we conducted a comprehensive multimodal benchmark for NLDBD event classification. This work makes three contributions: First, we coordinated the public release of four NLDBD datasets spanning three modalities, including time series, point clouds, and sparse 2D images. Second, we designed two complementary metrics to quantitatively characterize topology-based classification power and the severity of energy shortcut learning. Third, we designed 13 models to benchmark across four datasets, including both native-representation models and a unified Transformer backbone adapted through different tokenization and positional encoding. Our results identify the Region + MLP Transformer as the strongest overall model across all datasets, establishing a baseline architecture and evaluation framework toward multimodal foundation models for NLDBD experiments.