Grant: Subatomic Physics Envelope - Project

In the quest to understand the universe through its elementary constituents, CERN's Large Hadron Collider (LHC) has delivered important results confirming the current theory of particle physics, the Standard Model (SM). The discovery of the Higgs boson was a remarkable achievement: it explained how elementary particles acquired their masses. Despite being a very accurate description of the fundamental particle physics laws at the quantum scale, the SM fails to explain major cosmological phenomena in our universe. Finding a sign of "new physics" extending beyond the SM would be a ground-breaking discovery, and a guide toward a more complete description of nature. A promising gateway to probe for signatures of new physics is the measurement of the Higgs boson's interaction with the heaviest particle of matter: the top quark. The interaction strength, called Yukawa coupling, is very sensitive to new physics, and any deviation with respect to the theoretical prediction would uncover non-SM phenomena. I led the effort on the background evaluation towards the observation of the Higgs boson production in association with a top quark pair, or ttH, with the multipurpose ATLAS detector at the LHC. The ttH production mode is the physics process that can give direct access to the top quark Yukawa coupling. However, extracting the ttH signal from the LHC data is extremely challenging; the main background processes are very difficult to accurately model. The simulations, traditionally performed using Monte Carlo (MC) techniques, are intrinsically biased due to theoretical limitations. I propose to deploy advanced machine learning tools to measure with better precision the top quark Yukawa coupling in order to exploit the full potential of the incoming LHC Run 3 (2021 - 2023). Generative Adversarial Networks (GANs) are promising deep learning models with relevant applications in high energy physics. I propose a gradual approach to implement GANs for mitigating the large theoretical uncertainties and make the ttH analysis less reliant on MC simulations. This is in preparation for the High-Luminosity LHC program starting 2026, where the needs of MC with high statistics is almost multiplied by ten. GAN-generated collisions would be faster to produce than MC and much less computationally intensive. Preparing for the High-Luminosity LHC also requires detector upgrades and I propose to use my expertise in particle detector characterization to join the effort of the York University/Toronto cluster, which is in charge of producing and testing 500 sensor units (silicon and electronics) of the future ATLAS inner tracker. York University is best suited to be the dedicated site for detailed evaluation and recovery of components failing standard quality assurance tests. I propose to design and construct the evaluation laboratory, and oversee the reevaluation and recovery efforts during production.