Searches for new physics in collision events using a statistical technique for anomaly detection
ORAL
Abstract
We discuss a statistical anomaly-detection method for model-independent searches for new physics in collision events produced at the Large
Hadron Collider (LHC). The method requires calculations of Z-scores for a large number of variables to identify events that deviate from those
expected for the Standard Model (SM). A comparison with the autoencoder method for anomaly detection is discussed.
Hadron Collider (LHC). The method requires calculations of Z-scores for a large number of variables to identify events that deviate from those
expected for the Standard Model (SM). A comparison with the autoencoder method for anomaly detection is discussed.
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Publication: S.V.Chekanov, W.Hopkins, Event-based anomaly detection for new physics searches at the LHC using machine learning, https://arxiv.org/abs/2111.12119, ANL-HEP-17239 (2020)
Presenters
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Jacob E Crosby
Oklahoma State University
Authors
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Sergei Chekanov
Argonne National Laboratory
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Jacob E Crosby
Oklahoma State University