Speaker
Description
Dark matter (DM) constitutes the majority of the universe’s mass but remains undetected on Earth. The New Experiments with Spheres - Gas (NEWS-G) experiment is designed to directly detect low-mass dark matter candidates using a spherical proportional counter filled with light noble gases, enabling sensitivity to single electrons. At these low energies, the coherent elastic scattering of solar neutrinos (CE𝜈NS) will pose an irreducible background, creating the so-called solar neutrino floor, limiting conventional searches. Directional sensitivity offers a powerful strategy to discriminate between dark-matter-induced events and solar neutrino backgrounds, as these signals originate from distinct directions.
In this presentation, I will present advanced computational and modelling tools developed for reconstructing particle direction in the NEWS-G detector, leveraging its 11-anode sensor geometry. I will explain how spatial and temporal distributions of charge collected on multiple anodes are used to infer the trajectories of ionizing particles. Additionally, I will introduce machine learning techniques for event direction reconstruction based on detector observables, and discuss performance results that help identify optimal detector configurations for directional sensitivity. This simulation and computational work will establish a robust framework, enabling precise comparisons with experimental data to go beyond the solar neutrino floor with the NEWS-G experiment.
| Keyword-1 | Dark Matter Experiment |
|---|---|
| Keyword-2 | Directional Reconstruction |
| Keyword-3 | Simulation and Modelling |