Machine-learning techniques for model-independent searches in dijet final states

TL;DR

This paper presents machine learning-based anomaly detection methods for model-independent searches in dijet final states at the LHC, demonstrating improved sensitivity and the ability to identify boosted top quarks in proton-proton collision data.

Contribution

It introduces and evaluates novel machine learning techniques for anomaly detection in high-energy physics, enhancing model-independent search capabilities at the LHC.

Findings

Methods improve sensitivity to new phenomena

Effective in identifying boosted top quarks

Demonstrated on real LHC data

Abstract

Anomaly detection methods used in a recent search for new phenomena by CMS at the CERN LHC are presented. The methods use machine learning to detect anomalous jets produced in the decay of new massive particles. The effectiveness of these approaches in enhancing sensitivity to various signals is studied and compared using data collected in proton-proton collisions at a center-of-mass energy of 13 TeV. In an example analysis, the capabilities of anomaly detection methods are further demonstrated by identifying large-radius jets consistent with Lorentz-boosted hadronically decaying top quarks in a model-agnostic framework.