Gamma Neutron Radioactive Source Identification in Water Cherenkov Detectors

Abstract

Water Cherenkov Detectors (WCDs) are a robust technology widely used in astrophysics, high energy physics, and recently nuclear security applications. They detect high energy interactions through the Cherenkov light emitted by charged particles traveling faster than the speed of light in water. In this work, we demonstrate the feasibility of gamma-neutron discrimination in WCDs using a combined methodology that integrates statistical analysis with machine learning techniques. The experimental setup employs different shielding configurations to isolate gamma and neutron contributions from a \textsuperscript{241}AmBe source, while \textsuperscript{60}Co and \textsuperscript{137}Cs sources are used to establish a signal to energy calibration. A statistical analysis based on a 3σ significance criterion is used to define energy thresholds, enabling a linear relationship between the measured charge spectrum and the deposited energy. Building on this calibration, pulse shape information is further exploited through machine learning methods to improve event classification. An ensemble model based on a soft-voting strategy combining a Bagging classifier, CatBoost, and a Multilayer Perceptron was trained on detector signals acquired under different shielding conditions, achieving an accuracy of 0.816 and an area under the Receiver Operating Characteristic (ROC) curve. The combined approach demonstrates that statistical thresholding provides a physically grounded discrimination baseline across the full energy range, while machine learning enhances classification performance at higher energies by leveraging pulse level information. This integrated strategy improves radiation identification capabilities in water Cherenkov detectors, with potential applications in nuclear security and radiation detection.

A Unified Model for Radioactive Signatures and Source Identifiability in Neutron Star Mergers
Hisham Anwer
arXiv23 Oct 2026PreprintarXiv licenceFull textHigh-energy astrophysics Nuclear physicsarXiv
Disentangling mixed neutron fields: multi-source identification from few detected events
David Breitenmoser, William Heriot, Peter Marleau +2
arXiv24 Sep 2026PreprintCC BY 4.0Full textApplied physics & instrumentation Computational physicsarXiv
Using Geometric Deep Learning for the Detection of the Diffuse Supernova Neutrino Background in Water-Cherenkov Detectors
Matthieu Melennec, Andrew D Santos, Thomas Mueller +1
HAL8 Oct 2026PreprintCC BY 4.0Full textHigh-energy & particle physicsHALarXiv
Reactor operation induced thermal effects on neutron flux measurements using ³He neutron detectors at a TRIGA Mk II research reactor
S. Dorer, C. Trunner, E. Jericha +5
arXiv15 Sep 2026PreprintCC BY 4.0Full textApplied physics & instrumentationarXiv
Metastable Neutron Stars as Transient-Rate Detectors of Heavy Dark Matter
Ziwen Yin, Hong-Yi Zhang
arXiv24 Sep 2026PreprintarXiv licenceFull textCosmology High-energy astrophysicsarXiv

Cite

BibTeX RIS

BibTeX

@misc{selin2026gamma,
  title = {{Gamma Neutron Radioactive Source Identification in Water Cherenkov Detectors}},
  author = {A. Núñez Selin and C. Sarmiento Cano and H. Asorey and I. Sidelnik},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2608.06518},
  eprint = {2608.06518},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.06518},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - Gamma Neutron Radioactive Source Identification in Water Cherenkov Detectors
AU  - A. Núñez Selin
AU  - C. Sarmiento Cano
AU  - H. Asorey
AU  - I. Sidelnik
PY  - 2026
DA  - 2026/10/22
PB  - arXiv
DO  - 10.48550/arxiv.2608.06518
UR  - https://arxiv.org/abs/2608.06518
AB  - Water Cherenkov Detectors (WCDs) are a robust technology widely used in astrophysics, high energy physics, and recently nuclear security applications. They detect high energy interactions through the Cherenkov light emitted by charged particles traveling faster than the speed of light in water. In this work, we demonstrate the feasibility of gamma-neutron discrimination in WCDs using a combined methodology that integrates statistical analysis with machine learning techniques. The experimental setup employs different shielding configurations to isolate gamma and neutron contributions from a \textsuperscript{241}AmBe source, while \textsuperscript{60}Co and \textsuperscript{137}Cs sources are used to establish a signal to energy calibration. A statistical analysis based on a 3σ significance criterion is used to define energy thresholds, enabling a linear relationship between the measured charge spectrum and the deposited energy. Building on this calibration, pulse shape information is further exploited through machine learning methods to improve event classification. An ensemble model based on a soft-voting strategy combining a Bagging classifier, CatBoost, and a Multilayer Perceptron was trained on detector signals acquired under different shielding conditions, achieving an accuracy of 0.816 and an area under the Receiver Operating Characteristic (ROC) curve. The combined approach demonstrates that statistical thresholding provides a physically grounded discrimination baseline across the full energy range, while machine learning enhances classification performance at higher energies by leveraging pulse level information. This integrated strategy improves radiation identification capabilities in water Cherenkov detectors, with potential applications in nuclear security and radiation detection.
ER  -

Abstract

Related

Cite