arXiv subjects

T. E. Haugen

Publications and source records attributed to T. E. Haugen.

7 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

Precision measurement of radiative neutron \b{eta}-decay: methodology and systematic effects

In the Standard Model the free neutron decays to a proton, an electron, and an antineutrino along with a continuous spectrum of photons. In 2016 the RDK II collaboration reported on a measurement of the photon energy spectrum and branching ratio over the range of 0.4 keV to the 782 keV endpoint using two different detector arrays. In the experiment, the radiative decay photons were observed in coincidence with the decay electrons and protons. In this paper, we present details of the analysis, including the determination of the systematic corrections and uncertainties and comparison of measured particle and photon energy spectra to Monte Carlo simulations. We conclude with approaches to improving the precision of these measurements.

nucl-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↗

arXiv2025

Backscattering Study of Electrons from 0.1 to 3.4 MeV

Benchmarking simulation codes for electron transport and scattering in matter is a crucial step for estimating uncertainties in many applications. However, experimental data for electron energies of a few MeV is scarce to make such comparisons. We report here the measurement and the quantitative analysis of backscattering probabilities of electrons in the energy range 0.1 to 3.4~MeV impinging on YAP:Ce scintillator. The setup consists of a $2\times 2π$ calorimeter which enables, in particular, the inclusion of large incidence angles. The results are used to benchmark various scattering models incorporated in Geant4, showing relative deviations smaller than 5% between experiment and simulations. They demonstrate the current rather high reliability of the simulations when employing appropriate electromagnetic Physics Lists.

nucl-ex↗

arXiv2025

Machine Learning for Single-Ended Event Reconstruction in PROSPECT Experiment

The Precision Reactor Oscillation and Spectrum Experiment, PROSPECT, was a segmented antineutrino detector that successfully operated at the High Flux Isotope Reactor in Oak Ridge, TN, during its 2018 run. Despite challenges with photomultiplier tube base failures affecting some segments, innovative machine learning approaches were employed to perform position and energy reconstruction, and particle classification. This work highlights the effectiveness of convolutional neural networks and graph convolutional networks in enhancing data analysis. By leveraging these techniques, a 3.3\% increase in effective statistics was achieved compared to traditional methods, showcasing their potential to improve analysis performance. Furthermore, these machine learning methodologies offer promising applications for other segmented particle detectors, underscoring their versatility and impact.

physics.data-an↗

arXiv2024

Comments on the publication "Discrete symmetries tested at 10e-4 precision using linear polarization of photons from positronium annihilations" by P. Moskal et al

The authors of Ref.[1] (referred to here as "Moskal et al.") claim to have performed the most precise test of P, T and CP invariance in the decay of ortho-Positronium. In this note: 1) we demonstrate, assuming standard properties for Compton scattering, that the average value of the correlation measured by Moskal et al. must necessarily be zero, independently of any physics occurring in o-Ps decay; 2) we point out that there is no formal justification to equate the normal vector to the Compton scattering plane with the incident photon polarization, as done by Moskal et al.; 3) we observe the absence of characterization of the device as a Compton polarimeter, which is paramount in photon polarimetry; 4) we review previous measurements of the polarization of photons from o-Ps decay, properly implementing the Compton polarimetry technique, and make the connection with tests of discrete symmetries; and 5) we stress that the correlation proposed by Moskal et al. cannot be generated solely by the physics of o-Ps decay, including possible violations of discrete symmetries.

hep-ph↗