Enhancing the hunt for new phenomena in dijet final-states using anomaly detection filters at the High-Luminosity Large Hadron Collider

Abstract

In the realm of dijet searches in high-energy physics, a significant challenge has emerged: with experiments producing more and more data, the traditional methods of using analytic functions to describe dijet mass spectra start to fail. To address this, we suggest the application of an anomaly detection approach to eliminate less interesting background events based on event final states. This method not only bypasses the limitations of conventional background models but also significantly enhances our ability to detect potential signals of new physics. Through simulations that mimic the conditions of the upcoming High-Luminosity Large Hadron Collider, we demonstrate the strength and efficiency of this approach in dealing with large data volumes. The integration of unsupervised machine learning into our experimental framework paves the way for a promising avenue to unveil hidden physics discoveries within the overwhelming influx of data.

14 pages, 9 figures, 1 table

References in corpus (13)

• PYTHIA 6.4 Physics and Manual
• LHAPDF6: parton density access in the LHC precision era
• Extending the Bump Hunt with Machine Learning
• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
• Classifying Anomalies THrough Outer Density Estimation (CATHODE)
• Simulation-Assisted Decorrelation for Resonant Anomaly Detection
• Bump Hunting in Latent Space
• Online-compatible Unsupervised Non-resonant Anomaly Detection
• FETA: Flow-Enhanced Transportation for Anomaly Detection
• Self-supervised Anomaly Detection for New Physics
• Search for New Phenomena in Dijet Angular Distributions in Proton-Proton Collisions at $\sqrt{s}=8$ TeV Measured with the ATLAS Detector
• Event-based anomaly detection for new physics searches at the LHC using machine learning
• Estimation of the chances to find new phenomena at the LHC in a model-agnostic combinatorial analysis

Cited by in corpus (4)

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• Non-resonant Anomaly Detection with Background Extrapolation
• Unsupervised and lightly supervised learning in particle physics
• ADFilter -- A Web Tool for New Physics Searches With Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks