SBU HEP Seminar -Daisy Kalra (Columbia), Revolutionizing the search for new physics in LArTPC-based neutrino experiments with deep learning techniques

Thursday 15 Feb 2024, 13:30 → 14:30 America/New_York

Graduate Building D-122 (Stony Brook University)

Title: Revolutionizing the search for new physics in LArTPC-based neutrino experiments with deep learning techniques
Speaker: Daisy Kalra
Abstract: Current and future-generation Liquid Argon Time Projection Chamber (LArTPC) detectors represent a great opportunity to search for rare, beyond-the-Standard Model (BSM) physics, e.g.
baryon number violation. During operation, these detectors generate high-resolution images of particle interactions, making them well-suited for applying and leveraging deep learning (DL) techniques to search for rare signals within their data. This talk will focus on recent results from a DL-based analysis of MicroBooNE data, making use of a sparse convolutional neural network and event topology information to search for argon-bound neutron-antineutron transition-like
signals, which demonstrate the capability of LArTPCs in achieving high signal efficiency and strong background rejection when leveraging advances in image analysis techniques.
Furthermore, this talk will discuss ongoing research and development (R&D) aimed at developing data-driven data selection for the next-generation, large-scale LArTPC detector such as Deep Underground Neutrino Experiment (DUNE). A major challenge for DUNE is to
continually process its exorbitant data rates to search for rare and exotic signals which will require a lot of computational resources. The objective of these R&D efforts is to develop real-time data selection schemes as well as offline data analysis for rare signals with very high
accuracy and computational performance. Drawing from my own research experience, I will describe how these required advancements will enable sensitive searches for rare and exotic physics signals in the next-generation of liquid argon experiments.

Link to video recording on Google Drive (SBU internal)

Seminar_SB.pdf