2024/10/27 by Ranit Das, David Shih, Das, Ranit +1 · 1 citation
Computer Science · #Algorithms and Data Compression #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #Machine Learning (cs.LG) #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2410.20537
openalex publication_date 2024/10/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
A key step in any resonant anomaly detection search is accurate modeling of the background distribution in each signal region. Data-driven methods like CATHODE accomplish this by training separate generative models on the complement of each signal region, and interpolating them into their corresponding signal regions. Having to re-train the generative model on essentially the entire dataset for each signal region is a major computational cost in a typical sliding window search with many signal regions. Here, we present SIGMA, a new, fully data-driven, computationally-efficient method for estimating background distributions. The idea is to train a single generative model on all of the data and interpolate its parameters in sideband regions in order to obtain a model for the background in the signal region. The SIGMA method significantly reduces the computational cost compared to previous approaches, while retaining a similar high quality of background modeling and sensitivity to anomalous signals.