Neutrino Physics and Machine Learning 2024

Session

Day 1 - Afternoon

Jun 25, 2024, 1:35 PM

HCI J4 (ETH Zurich)

Description

ML based data reconstruction and analysis in NOvA, MicroBooNE, and ICARUS

11. Identifying Neutrino Final States in MicroBooNE with a New Deep-Learning Based LArTPC Reconstruction Framework

Matthew Rosenberg (Tufts University)

6/25/24, 1:35 PM

MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the ${\nu }_{\mu }$-dominated Booster Neutrino Beam at Fermilab, has been studying ${\nu }_e$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and...

22. Using CNN for a Dark Trident Search at MicroBooNE

Luis Mora Lepin

6/25/24, 2:00 PM

We present a first search for dark-trident scattering in a neutrino beam using a data set taken with the MicroBooNE detector at Fermilab. Proton interactions in the neutrino target at the Main Injector produce neutral mesons, which could decay into dark-matter (DM) particles mediated via a dark photon A′. A convolutional neural network (CNN) is trained to identify interactions of the DM...

21. Data-Driven Light Model for the MicroBooNE Experiment

Polina Abratenko (Tufts University)

6/25/24, 2:25 PM

MicroBooNE is a short baseline neutrino oscillation experiment that employs a Liquid Argon Time Projection Chamber (LArTPC) together with an array of Photomultiplier Tubes (PMTs), which detect scintillation light. This light detection is necessary for providing a means to reject cosmic ray background and trigger on beam-related interactions. Thus, accurate modeling of the expected optical...

1. Particle Trajectory Reconstruction and Euclidian Equivariant Neural Networks

Omar Alterkait (Tufts University / IAIFI)

6/25/24, 2:55 PM

Training neural networks for analyzing three-dimensional trajectories in particle detectors presents challenges due to the high combinatorial complexity of the data. Incorporating networks with Euclidean Equivariance could significantly reduce the reliance on data augmentation. To achieve Euclidean Equivariance, we construct neural networks that primarily represent data and perform...

50. Deep Learning applications for electron neutrino reconstruction in the ICARUS experiment

Dae Heun Koh (SLAC), Drielsma Francois (SLAC)

6/25/24, 3:25 PM

The ICARUS T600 detector is a liquid argon time projection chamber (LArTPC) installed at Fermilab, aimed towards a sensitive search for possible electron neutrino excess in the 200-1000 MeV region. To investigate nue appearance signals in ICARUS, a fast and accurate algorithm for selecting electron neutrino events from a background of cosmic interactions is required. We present an application...

54. Muon Neutrino Reconstruction and Neutral Pion Calibration with Machine Learning Techniques at the ICARUS Detector

Daniel Carber (Colorado State University, Fort Collins)

6/25/24, 4:30 PM

The ICARUS T600 Liquid Argon Time Projection Chamber (LArTPC) detector is the far detector of the Short Baseline Neutrino (SBN) Program located at Fermilab National Laboratory (FNAL). The data collection for ICARUS began in May 2021, utilizing neutrinos from the Booster Neutrino Beam (BNB) and the Neutrinos at the Main Injector off-axis beam (NuMI). The SBN Program has been designed to...

27. Deep Learning in NOvA

BARBARA YAEGGY (University of Cincinnati)

6/25/24, 5:05 PM

The NOvA experiment uses the ~1 MW NuMI beam from Fermilab to study neutrino oscillations: electron neutrino appearance and muon neutrino disappearance in a baseline of 810 km, with a 300-ton near detector and a 14-kiloton far detector. NOvA was the first experiment in high-energy physics to apply convolutional neural networks to classify neutrino interactions and composite particles in a...

35. Transformer Network for Event/Particle Identification and Interpretability at NOvA

Jianming Bian (University of California, Irvine)

6/25/24, 5:30 PM

The NOvA experiment is a long-baseline accelerator neutrino experiment utilizing Fermilab's upgraded NuMI beam. It measures the appearance of electron neutrinos and the disappearance of muon neutrinos at its Far Detector in Ash River, Minnesota. NOvA is the first neutrino experiment to use convolutional neural networks (CNNs) for event identification and reconstruction. Recently, we introduced...