Machine learning approach for the search of resonances with topological features at the Large Hadron Collider

TL;DR

This paper introduces a novel machine learning approach combining weak supervision and deep neural networks to enhance the search for topological resonances at the LHC, aiming to detect subtle signals beyond the Standard Model.

Contribution

It proposes a signature and topology-based classification method that improves resonance searches by integrating weak supervision with deep learning, reducing reliance on simulations.

Findings

Effective extraction of SM Higgs signals in various production modes

Enhanced sensitivity in high-mass resonance searches

Demonstrated potential for discovering new physics phenomena

Abstract

The observation of resonances is unequivocal evidence of new physics beyond the Standard Model at the Large Hadron Collider (LHC). So far, inclusive and model dependent searches have not provided evidence of new resonances, indicating that these could be driven by subtle topologies. Here, we use machine learning techniques based on weak supervision to perform searches. Weak supervision based on mixed samples can be used to search for resonances with little or no prior knowledge on the production mechanism. Also, it offers the advantage that sidebands or control regions can be used to effectively model backgrounds with minimal reliance on simulations. However, weak supervision alone is found to be highly inefficient in identifying corners of the multi-dimensional space of interest. Instead, we propose an approach to search for new resonances that involves a classification procedure that is signature and topology based. A combination of weak supervision with Deep Neural Network algorithms are applied following this classification. The performance of this strategy is evaluated on the production of SM Higgs boson decaying to a pair of photons inclusively and in exclusive regions of phase space tailored for specific production modes at the LHC. After verifying the ability of the methodology to extract different SM Higgs boson signal mechanisms, a search for new phenomena in high-mass final states is setup for the LHC.