SIGMA: Single Interpolated Generative Model for Anomalies

Ranit Das; David Shih

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arXiv · 2410.20537

SIGMA: Single Interpolated Generative Model for Anomalies

Ranit Das·David Shih

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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.

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Ranit Das, David Shih. 2024-10-27. SIGMA: Single Interpolated Generative Model for Anomalies. https://arxiv.org/abs/2410.20537

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