Machine Learning Solutions for High Energy Physics: Applications to Electromagnetic Shower Generation, Flavor Tagging, and the Search for di-Higgs Production

TL;DR

This thesis demonstrates how advanced machine learning algorithms can significantly improve various high energy physics tasks, including particle identification, event simulation, and searches for rare processes, by modernizing traditional methods.

Contribution

It introduces novel ML approaches for impact parameter-based flavor tagging, di-Higgs searches, and generative adversarial networks for calorimeter shower simulation in high energy physics.

Findings

Enhanced impact parameter-based flavor tagging performance.

Improved sensitivity in di-Higgs search channels.

Established GAN-based particle shower simulation as a viable tool.

Abstract

This thesis demonstrate the efficacy of designing and developing machine learning (ML) algorithms to selected use cases that encompass many of the outstanding challenges in the field of experimental high energy physics. Although simple implementations of neural networks and boosted decision trees have been used in high energy physics for a long time, the field of ML has quickly evolved by devising more complex, fast and stable implementations of learning algorithms. The complexity and power of state-of-the-art deep learning far exceeds those of the learning algorithms implemented in the CERN-developed \texttt{ROOT} library. All aspects of experimental high energy physics have been and will continue being revolutionized by the software- and hardware-based technological advances spearheaded by both academic and industrial research in other technical disciplines, and the emergent trend of increased interdisciplinarity will soon reframe many scientific domains. This thesis exemplifies this spirit of versatility and multidisciplinarity by bridging the gap between ML and particle physics, and exploring original lines of work to modernize the reconstruction, particle identification, simulation, and analysis workflows. This contribution documents a collection of novel approaches to augment traditional domain-specific methods with modern, automated techniques based on industry-standard, open-source libraries. Specifically, it contributes to setting the state-of-the-art for impact parameter-based flavor tagging and di-Higgs searches in the $\gamma \gamma b\bar{b} $ channel with the ATLAS detector at the LHC, it introduces and lays the foundations for the use of generative adversarial networks for the simulation of particle showers in calorimeters. These results substantiate the notion of ML powering particle physics in the upcoming years and establish baselines for future applications.