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arXiv · 2111.12119

Event-based anomaly detection for new physics searches at the LHC using machine learning

S. V. Chekanov·W. Hopkins
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Abstract

This paper discusses model-agnostic searches for new physics at the Large Hadron Collider (LHC) using anomaly-detection techniques for the identification of event signatures that deviate from the Standard Model (SM). We investigate anomaly detection in the context of machine-learning approaches using autoencoders, and illustrate expected shapes of invariant masses in the outlier region using Monte Carlo simulations. Challenges and conceptual limitations of this approach are discussed.

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S. V. Chekanov, W. Hopkins. 2021-11-23. Event-based anomaly detection for new physics searches at the LHC using machine learning. https://doi.org/10.3390/universe8100494

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