arXiv subjects

N. Craft

Publications and source records attributed to N. Craft.

3 recordsLinked to original sources

arXiv2026

Gamma Backgrounds for Experiments at the High Flux Isotope Reactor

This article describes the deployment of a germanium detector at Oak Ridge National Lab's High Flux Isotope Reactor (HFIR) for the purpose of understanding the energy and spatial distribution of the gamma field in the experiment hall where the Precision Reactor Oscillation and Spectrum Experiment (PROSPECT) took data and future neutrino experiments could be located. The sources from both the reactor and the neutron beamlines are described in detail, along with their temporal variations due to reactor power and their spatial variations due to the geometry of the beamlines and building materials in the vicinity. Additionally, a shielding study was performed to assess the amount that backgrounds in tens of keV range can be mitigated. This work helps inform backgrounds for future experiments at reactors such as IBD-based neutrino measurements and CEvNS measurements.

hep-ex↗

arXiv2026

Probing Long-Lived Particle Production in Muon Decays at the SNS with a Highly Capable Hydrocarbon Detector

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) is a prolific muon producer, making it an ideal location for studying dark sector particles produced in muon decays at rest. In this paper, we explore sub-GeV dark particle detection possibilities in a tons-scale, highly capable hydrocarbon scintillator ($HC^2$) detector at the SNS. We consider a search for $e^+e^-$ final states produced by decays of long-lived, $O(10-100)$ MeV axion-like particles and heavy neutral leptons. The $HC^2$ technology space, exemplified by the PROSPECT and Mobile Antineutrino Demonstrator detectors, offers strong rejection capabilities for the cosmic ray backgrounds that would normally dominate this search. By benchmarking on-surface cosmic ray signatures with data from PROSPECT at ORNL, we generate robust predictions for a multi-year SNS deployment of a range of $HC^2$ detector implementations. Results indicate the potential for order-of-magnitude improvements in sensitivity to axion-like particles and heavy neutral leptons in the 10-100 MeV mass regime compared to current global limits. We also comment on the neutrino detection possibilities of a $HC^2$ deployment at the SNS.

hep-ex↗

arXiv2026

New Deep Learning Data Analysis Method for PROSPECT using GAPE: Genetic Algorithm Powered Evolution

We propose a genetic algorithm powered evolution (GAPE) method to create deep learning solutions for energy and position estimation for reactor antineutrino interactions in the Precision Reactor Oscillation and Spectrum Experiment (PROSPECT) at the highly enriched High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. We also apply GAPE to create classification models to distinguish signatures of inverse beta decay (IBD) interactions of reactor antineutrinos from common background types. The GAPE method can also be adopted for optimization of other types of problems that utilize machine learning (ML) models for particle physics applications. When applied in the PROSPECT context, we find that the models selected by GAPE can, in some cases, outperform the traditional models previously used for PROSPECT data analysis. In particular, when benchmarked against conventional PROSPECT neutrino identification pathways using the same underlying information, the classifier offers the promise of improving the signal-to-background ratio by nearly 2.8 times. Performance biases uncovered during initial IBD classifier validation were primarily caused by differences in time-dependent response between background and signal training datasets. Biases were effectively mitigated through a data-period-specific training regimen, offering a pathway towards realizing an unbiased IBD signal classifier for future reactor neutrino datasets.

physics.data-an↗