Anomaly Detection for Resonant New Physics with Machine Learning

2018/05/31 by Jack H. Collins, Kiel Howe, Benjamin Nachman · 3 citations
Physics and Astronomy · #hep-ph #hep-ex

paper · pdf · doi:10.1103/physrevlett.121.241803

published as Phys. Rev. Lett. 121, 241803 (2018) · Replaced with short PRL version. 7 pages, 2 figures. Revised long version will be submitted separately

arxiv created 2018/11/19 · arxiv updated 2019/03/21

Abstract

Despite extensive theoretical motivation for physics beyond the Standard Model (BSM) of particle physics, searches at the Large Hadron Collider (LHC) have found no significant evidence for BSM physics. Therefore, it is essential to broaden the sensitivity of the search program to include unexpected scenarios. We present a new model-agnostic anomaly detection technique that naturally benefits from modern machine learning algorithms. The only requirement on the signal for this new procedure is that it is localized in at least one known direction in phase space. Any other directions of phase space that are uncorrelated with the localized one can be used to search for unexpected features. This new method is applied to the dijet resonance search to show that it can turn a modest 2 sigma excess into a 7 sigma excess for a model with an intermediate BSM particle that is not currently targeted by a dedicated search.

Cited by

• Learning the Standard Model Manifold: Bayesian Latent Diffusion for Collider Anomaly Detection
• Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practice
• Strengthening Anomaly Awareness

Related

• Full Phase Space Resonant Anomaly Detection
• Machine-learning techniques for model-independent searches in dijet final states
• Weakly supervised anomaly detection for resonant new physics in the dijet final state using proton-proton collisions at √(s)=13 TeV with the ATLAS detector
• Weakly Supervised Anomaly Detection in Events with a Higgs Boson and Exotic Physics
• New Physics Searches at the LHC through Event-based Anomaly Detection and Development of ADFilter Web-tool