Image of Cosmic Ray Background Removal With Deep Neural Networks in SBND

Text

Cosmic Ray Background Removal With Deep Neural Networks in SBND



In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons, and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded. In practice, this means that data from surface liquid argon time projection chambers will be dominated by cosmic particles, both as a source of event triggers and as the majority of the particle count in true neutrino-triggered events. In this work, we demonstrate a novel application of deep learning techniques to remove these background particles by applying deep learning on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, on a pixel-by-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.


Availability

No copy data


Detail Information

Series Title
-
Call Number
-
Publisher Frontiers in Artificial Intelligence : Switzerland.,
Collation
006
Language
English
ISBN/ISSN
2624-8212
Classification
NONE
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Specific Detail Info
-
Statement of Responsibility

Other Information

Accreditation
Scopus Q3

Other version/related

No other version available


File Attachment



Information


Web Online Public Access Catalog - Use the search options to find documents quickly