SIGMA: Single Interpolated Generative Model for Anomalies

TL;DR

SIGMA introduces a single, interpolated generative model that efficiently estimates background distributions for anomaly detection, reducing computational costs while maintaining high modeling quality.

Contribution

The paper proposes a novel method that trains one generative model on all data and interpolates parameters for background estimation, improving efficiency over prior methods.

Findings

Reduces computational cost compared to previous methods.

Maintains high background modeling quality.

Retains sensitivity to anomalous signals.

Abstract

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.