SIGMA: Single Interpolated Generative Model for Anomalies
This work addresses a computational bottleneck in anomaly detection for physics research, offering an incremental improvement over existing data-driven methods.
The paper tackles the computational inefficiency of training separate generative models for each signal region in resonant anomaly detection by introducing SIGMA, a method that trains a single model on all data and interpolates its parameters, achieving similar background modeling quality and sensitivity while significantly reducing computational cost.
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.