A5036487152- Dark Matter and Cosmic Phenomena
- Particle physics theoretical and experimental studies
- Particle Detector Development and Performance
- Radiation Detection and Scintillator Technologies
- COVID-19 Pandemic Impacts
- COVID-19 epidemiological studies
- Astronomy and Astrophysical Research
- Data-Driven Disease Surveillance
- Agricultural risk and resilience
- Scientific Research and Discoveries
Queen's University
2022-2025
Spherical proportional counters (SPCs) are gaseous particle detectors sensitive to single ionization electrons in their target media, with large detector volumes and low background rates. The $\mbox{NEWS-G}$ collaboration employs this technology search for low-mass dark matter, having previously performed searches at the Laboratoire Souterrain de Modane (LSM), including a recent campaign 135 cm diameter SPC filled methane. While situ calibrations of response were carried out LSM,...
This work introduces the Queen's University Agent-Based Outbreak Outcome Model (QUABOOM). tool is an agent-based Monte Carlo simulation for modelling epidemics and informing public health policy. We illustrate use of model by examining capacity restrictions during a lockdown. find that measures should focus on few locations where many people interact, such as grocery stores, rather than small businesses. also discuss case results can be scaled to larger population sizes, thereby improving...
We present a convolutional autoencoder to denoise pulses from p-type point contact high-purity germanium detector similar those used in several rare event searches. While we focus on training procedures that rely detailed physics simulations, also implementations requiring only noisy train the model. validate our both simulated data and calibration an $^{241}$Am source, latter of which is show denoised are statistically compatible with pulses. demonstrate denoising method able preserve...
We present a convolutional autoencoder to denoise pulses from p-type point contact high-purity germanium detector similar those used in several rare event searches. While we focus on training procedures that rely detailed physics simulations, also implementations requiring only noisy train the model. validate our both simulated data and calibration an 241 Am source, latter of which is show denoised are statistically compatible with pulses. demonstrate denoising method able preserve...
Abstract This work introduces the Queen’s University Agent-Based Outbreak Outcome Model (QUABOOM), a new, data-driven, agent-based Monte Carlo simulation for modelling epidemics and informing public health policy in wide range of population sizes. We demonstrate how model can be used to quantitatively inform capacity restrictions COVID-19 reduce their impact on small businesses by showing that measures should target few locations where many individuals interact rather than interact....