David, Claire

Claire David

Assistant Professor
Ph.D. (Victoria)
claired@yorku.ca or cdavid@fnal.gov

Department(s): Physics and Astronomy

Office: 246 Petrie Science and Engineering Building (PSE)
Phone: (416)736-2100 ext. 22855

Lab: 241 Petrie Science and Engineering Building (PSE)

Research Fields
High Energy and Particle Physics

Graduate Program Appointment
Full Member: Eligible to supervise M.Sc. or Ph.D.

Research Types
Computational, Experimental

Research specialization
Higgs boson’s coupling to top quark; neutrino interactions and mixing; astronomical neutrinos; machine learning for high-energy physics; particle detector development; silicon tracking detectors; data acquisition systems for high-energy physics

Understanding how elementary particles interact with each other is a massive challenge. Literally. Particles of matter obtain their masses via interaction with the Higgs field, present everywhere and acting as a slowing down mechanism. Its associated particle, the Higgs boson, interacts with fermions proportionally to their masses. So far the majority of measurements of Higgs boson properties is in agreement with the Standard Model (SM) of particle physics. This widely accepted theory yields very accurate predictions, yet it fails to explain why for instance the mass of the Higgs boson itself is so light.
One aspect of my research is to perform precise studies of the Higgs boson’s interaction with the heaviest particle of matter: the top quark. The parameter of interest, called interaction strength or coupling, is very sensitive to new physics and any deviation with respect to the theoretical prediction would uncover non-​SM phenomena. At the Large Hadron Collider (LHC) at CERN in Switzerland, it is possible to access this parameter via a rare Higgs boson production mode that has recently been discovered: the Higgs boson production in association with a top quark pair, or “ttH”. I contributed to the discovery of ttH by the ATLAS experiment, one of the LHC’s detectors. I am now interested in deploying advanced machine learning techniques to increase the sensitivity of the ttH analysis, leading to a more precise assessment that could uncover new physics.

On the other end of the spectrum, the lightest particles of matter are the mysterious neutrinos. They are expected to be massless in the SM yet they oscillate, that is to say they change flavors over time, and this ‘mixing’ is only possible if their mass is non-zero. A leading-edge experiment for neutrino science hosted by Fermilab in the United States is being built to operate starting in 2026: the Deep Underground Neutrino Experiment (DUNE). It will consist of two massive state-of-the-art neutrino detectors, one at Fermilab in Illinois and one at the Sanford Underground Research Facility in South Dakota, 1.5 km underground. The Long-Baseline Neutrino Facility (LBNF) will supply intense beams of neutrinos and antineutrinos with energy spectra broader than any other current experiment. DUNE is designed both for big potential discoveries as well as precision measurements. An exciting feature of DUNE is its sensitivity to neutrinos coming from the cosmos, especially stellar explosions: supernovae. As a member of the DUNE Collaboration, my initial interests focus on developing a robust data acquisition system to store the enormous amount of information to record neutrinos from supernovae. This will enable us to understand more about neutrinos and at the same time about the cosmological phenomenon of core-collapsing stars.

Besides data analysis, I am involved in hardware work related to the construction of the future ATLAS inner tracking detector designed for the High-Luminosity phase of the LHC that will run 2026-3037. The collision rate (and radiation damage) will be at least 7 times larger than it has been up until now. I plan to set up test benches of the data acquisition system of this future tracker and translate this expertise to benefit the DUNE experiment.