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Related Experiment Video

Updated: Jul 2, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Cosmic Ray Background Removal With Deep Neural Networks in SBND.

R Acciarri¹, C Adams², C Andreopoulos^3,4

¹Fermi National Accelerator Laboratory, Batavia, IL, United States.

Frontiers in Artificial Intelligence

|September 10, 2021

Summary

This summary is machine-generated.

Deep learning effectively removes cosmic particle backgrounds in surface-level liquid argon time projection chambers. This technique distinguishes cosmic-induced noise from genuine neutrino interactions using full detector images.

Keywords:

SBN program SBND UNet deep learning liquid Ar detectors neutrino physics

Related Experiment Videos

Last Updated: Jul 2, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

Particle Physics
Detector Technology
Artificial Intelligence

Background:

Surface-level liquid argon time projection chambers (LArTPCs) face significant background noise from cosmic muons and particles.
This cosmic interference dominates event triggers and data, obscuring rare neutrino interactions.

Purpose of the Study:

To develop and demonstrate a deep learning method for mitigating cosmic ray backgrounds in LArTPC data.
To accurately differentiate between cosmic particle activity and neutrino-induced events within detector images.

Main Methods:

Application of deep learning techniques to full detector images from the SBND detector.
Pixel-level analysis to classify recorded activity as either cosmic or neutrino-origin.

Main Results:

Successful identification and removal of cosmic particle signals from LArTPC data.
Demonstration of deep learning's capability to enhance neutrino event detection in noisy environments.

Conclusions:

Deep learning offers a powerful solution for background reduction in surface-based neutrino experiments.
This method significantly improves the purity of neutrino interaction data, crucial for physics analyses.