TRIUMF Research Scientist
Patrick de Perio obtained his B.Sc. in mathematical physics from the University of Waterloo in 2008 and his Ph.D. in physics from the University of Toronto in 2014. As an NSERC Vanier fellow, his CAP award-winning PhD thesis “Joint Three-Flavour Oscillation Analysis of Muon Neutrino Disappearance and Electron Neutrino Appearance in the T2K Neutrino Beam” contributed to a first hint of CP violation in the lepton sector, building off the discovery of electron neutrino appearance. He focused on contributions to proton beamline monitoring, neutrino interaction and detector modeling, event reconstruction, big data, and statistical analyses. He was a co-recipient of the Suwa Prize in Japan and the Breakthrough Prize in Fundamental Physics for these contributions.
As an NSERC postdoctoral fellow at Columbia University from 2014, he performed a direct search for the elusive dark matter (DM). He brought the XENON100 experiment to a close as the analysis co-coordinator, then assisted in the realization of the XENON1T experiment. He was involved in the construction of the experiment, the first to operate with over 3 tonnes of liquid xenon, as a xenon purification group co-leader and cryogenics expert. He served as the analysis co-coordinator for the most sensitive DM search using the largest xenon exposure and lowest background dataset in the world, resulting in the most stringent constraint on DM interactions.
Since 2018, Patrick de Perio joined TRIUMF as a research scientist focusing on the future Hyper-Kamiokande experiment that aims to discover CP violation in accelerator beam and atmospheric neutrino oscillations, a potentially significant piece of the matter-antimatter asymmetry puzzle of the universe. Furthermore, Super-Kamiokande and Hyper-Kamiokande are sensitive to nucleon decay (constraining GUTs), supernovae, and other multi-messenger astronomy. To achieve design sensitivity for these physics, he is leading critical developments for water Cherenkov detectors including precision calibration systems, deep learning techniques, new photosensors, and prototype beam tests.