2021 CAP Virtual Congress / Congrès virtuel de l'ACP 2021

Session

M2-10 Machine learning in HEP & Novel reconstruction techniques I (PPD) / Apprentissage automatique en PHE et nouvelles techniques de reconstruction I (PPD)

M2-10

7 Jun 2021, 12:45

Underline Conference System

Conveners

M2-10 Machine learning in HEP & Novel reconstruction techniques I (PPD) / Apprentissage automatique en PHE et nouvelles techniques de reconstruction I (PPD)

• Eric Drechsler (Simon Fraser University (CA))

545. Machine learning techniques for improving water Cherenkov event reconstruction

Nick Prouse (TRIUMF)

07/06/2021, 12:45

Particle Physics / Physique des particules (PPD)

Oral (Non-Student) / Orale (non-étudiant(e))

Hyper-Kamiokande is the next generation water-Cherenkov neutrino experiment, building on the success of its predecessor Super-Kamiokande. To match the increased precision and reduced statistical errors of the new detectors, improvements to event reconstruction and event selection are required to suppress backgrounds and reduce systematic errors. Machine learning has the potential to provide...

139. (G*) Muon Track Reconstruction for the P-ONE Neutrino Telescope

Dilraj Ghuman (Queens University)

07/06/2021, 13:00

Particle Physics / Physique des particules (PPD)

Oral Competition (Graduate Student) / Compétition orale (Étudiant(e) du 2e ou 3e cycle)

The neutrino, a fundemental particle, offers the potential to image parts of the universe never before seen and can provide an early warning for cosmic events. With their ability to carry information across the universe unperturbed, neutrinos offer a clear image of the cosmos and can provide insight into its nature with relative ease. Learning from successful neutrino telescopes such as...

58. (G*) Machine Learning for Energy Reconstruction at ATLAS

Lucas Alexander Polson (University of Victoria (CA))

07/06/2021, 13:10

Particle Physics / Physique des particules (PPD)

Oral Competition (Graduate Student) / Compétition orale (Étudiant(e) du 2e ou 3e cycle)

A crucial task of the ATLAS calorimeter is energy measurement of detected particles. In the liquid argon (LAr) calorimeter subdetector of ATLAS, electromagnetically and hadronically interacting particles are detected through LAr ionization. Special electronics convert drifting electrons into a measurable current. The analytical technique presently used to extract energy from the measured...

199. Particle identification at the CERN NA62 experiment using Convolutional Neural Networks

Bob Velghe (TRIUMF (CA))

07/06/2021, 13:20

Particle Physics / Physique des particules (PPD)

Oral (Non-Student) / Orale (non-étudiant(e))

The rare $K^+ \to \pi^+ \nu \bar{\nu}$ decay is an ideal probe for beyond the Standard Model (BSM) physics contributions to the flavor sector. It is heavily suppressed in the SM and its branching ratio is predicted, with remarkable precision for a second order weak process involving hadrons, to be $\left(8.4 \pm 1.0\right) \times 10^{-11}$.

The NA62 experiment at the CERN SPS is designed...

386. (G*) Parameter estimation of gravitational waves over non-Gaussian transient noise

Yannick Lecoeuche (University of British Columbia)

07/06/2021, 13:30

Particle Physics / Physique des particules (PPD)

Oral Competition (Graduate Student) / Compétition orale (Étudiant(e) du 2e ou 3e cycle)

The Laser Interferometer Gravitational-Wave Observatory (LIGO) is expected to begin its fourth observing run in 2022, with a large projected improvement in detector sensitivity. This sensitivity boost increases the gravitational wave (GW) detection rate, but also increases the likelihood of GW events overlapping with transient, non-Gaussian detector noise, or glitches. This project aims to...