RIDDLE: Revealing Irregularities through Distributional Discrepancies in Latent-space via Stein-Witness Estimation

RIDDLE is a new method for resonant anomaly detection that uses a scalar Stein-witness function to identify discrepancies between unlabeled signal-region events and a background distribution inferred from dijet-mass sidebands. The witness is optimized in a latent representation of auxiliary jet observables against a differentiable background reference, without requiring an explicit signal model. The method is evaluated using simulated LHC Olympics dijet events and compared with idealized and supervised benchmarks that share the same statistical construction but have access to additional signal or background information. RIDDLE demonstrates strong signal–background discrimination comparable to the idealized benchmark, while largely preserving the background dijet-mass spectrum under anomaly-score selection. Tests incorporating additional angular information and deliberately mass-correlated features further demonstrate the method’s robustness under the studied conditions. Together, these results highlight the potential of RIDDLE as a robust, data-driven approach for enhanced resonance searches that combines effective anomaly discrimination with background-shape preservation.