arXiv subjects

A. Lozano Sanchez

Publications and source records attributed to A. Lozano Sanchez.

6 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↗

arXiv2025

Final Search for Short-Baseline Neutrino Oscillations with the PROSPECT-I Detector at HFIR

The PROSPECT experiment is designed to perform precise searches for antineutrino disappearance at short distances (7 - 9~m) from compact nuclear reactor cores. This Letter reports results from a new neutrino oscillation analysis performed using the complete data sample from the PROSPECT-I detector operated at the High Flux Isotope Reactor in 2018. The analysis uses a multi-period selection of inverse beta decay neutrino interactions with reduced backgrounds and enhanced statistical power to set limits on electron-flavor disappearance caused by mixing with sterile neutrinos with 0.2 - 20 eV$^2$ mass splittings. Inverse beta decay positron energy spectra from six different reactor-detector distance ranges are found to be statistically consistent with one another, as would be expected in the absence of sterile neutrino oscillations. The data excludes at 95% confidence level the existence of sterile neutrinos in regions above 3~eV$^2$ previously unexplored by terrestrial experiments, including all space below 10~eV$^2$ suggested by the recently strengthened Gallium Anomaly. The best-fit point of the Neutrino-4 reactor experiment's claimed observation of short-baseline oscillation is ruled out at more than five standard deviations.

hep-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

Reactor Antineutrino Directionality Measurement with the PROSPECT-I Detector

The PROSPECT-I detector has several features that enable measurement of the direction of a compact neutrino source. In this paper, a detailed report on the directional measurements made on electron antineutrinos emitted from the High Flux Isotope Reactor is presented. With an estimated true neutrino (reactor to detector) direction of $ϕ= 40.8\unicode{xB0} \pm 0.7\unicode{xB0}$ and $θ= 98.6\unicode{xB0} \pm 0.4\unicode{xB0}$, the PROSPECT-I detector is able to reconstruct an average neutrino direction of $ϕ= 39.4\unicode{xB0} \pm 2.9\unicode{xB0}$ and $θ= 97.6\unicode{xB0} \pm 1.6\unicode{xB0}$. This measurement is made with approximately 48000 Inverse Beta Decay signal events and is the most precise directional reconstruction of reactor antineutrinos to date.

nucl-ex↗