Online-compatible Unsupervised Non-resonant Anomaly Detection

• URL: http://arxiv.org/abs/2111.06417v1
• Date: Thu, 11 Nov 2021 19:01:09 GMT
• Title: Online-compatible Unsupervised Non-resonant Anomaly Detection
• Authors: Vinicius Mikuni, Benjamin Nachman, David Shih
• Abstract summary: We propose the first complete strategy for unsupervised detection of non-resonant anomalies. Our technique is built out of two simultaneously-trained autoencoders that are forced to be decorrelated from each other. This method can be deployed offline for non-resonant anomaly detection and is also the first complete online-compatible anomaly detection strategy.
• Score: 0.4297070083645048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is a growing need for anomaly detection methods that can broaden the search for new particles in a model-agnostic manner. Most proposals for new methods focus exclusively on signal sensitivity. However, it is not enough to select anomalous events - there must also be a strategy to provide context to the selected events. We propose the first complete strategy for unsupervised detection of non-resonant anomalies that includes both signal sensitivity and a data-driven method for background estimation. Our technique is built out of two simultaneously-trained autoencoders that are forced to be decorrelated from each other. This method can be deployed offline for non-resonant anomaly detection and is also the first complete online-compatible anomaly detection strategy. We show that our method achieves excellent performance on a variety of signals prepared for the ADC2021 data challenge.

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