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Simulation-Assisted Decorrelation for Resonant Anomaly Detection · Around

2020

Simulation-Assisted Decorrelation for Resonant Anomaly Detection

Benkendorfer, Kees, Pottier, Luc Le, Nachman, Benjamin

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A growing number of weak- and unsupervised machine learning approaches to anomaly detection are being proposed to significantly extend the search program at the Large Hadron Collider and elsewhere.

One of the prototypical examples for these methods is the search for resonant new physics, where a bump hunt can be performed in an invariant mass spectrum.
A significant challenge to methods that rely entirely on data is that they are susceptible to sculpting artificial bumps from the dependence of the machine learning classifier on the invariant mass.
We explore two solutions to this challenge by minimally incorporating simulation into the learning.

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