Paper ID: 2410.20537 • Published Oct 27, 2024
SIGMA: Single Interpolated Generative Model for Anomalies
Ranit Das, David Shih
TL;DR
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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.