Enhancing Sensitivity for Di-Higgs Boson Searches Using Anomaly Detection and Supervised Machine Learning Techniques

2025/04/16 by S. Chekanov, Chekanov, Sergei V., W. Islam +3 · 2 citations
Physics and Astronomy · Computer Science · #Particle physics theoretical and experimental studies #Neutrino Physics Research #Computational Physics and Python Applications

paper · pdf · doi:10.48550/arxiv.2504.12418

Abstract

This paper explores different strategies for enhancing sensitivity to new heavy resonances that decay into two or more Higgs bosons. This is achieved using two neural network architectures: an unsupervised autoencoder for anomaly detection and a supervised classifier. The autoencoder is trained on a small fraction of Standard Model (SM) Monte Carlo simulated events to calculate the loss distribution for input events, aiding in determining the extent to which events can be considered anomalous. The supervised classifier uses the same inputs but is trained on events simulated using both beyond Standard Model (BSM) and SM processes. By applying selection cuts to the output scores, we compare the sensitivities of the two approaches.

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Cited by

• New Physics Searches at the LHC through Event-based Anomaly Detection and Development of ADFilter Web-tool

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• Deep Learning to Improve the Sensitivity of Di-Higgs Searches in the 4b Channel
• New Physics Searches at the LHC through Event-based Anomaly Detection and Development of ADFilter Web-tool