arXiv subjects

H. Lay

Publications and source records attributed to H. Lay.

6 recordsLinked to original sources

arXiv2026

Production and installation of wavelength-shifting reflective light enhancers for the Short-Baseline Near Detector

We report on the design, production, and installation of a wavelength-shifting reflective system on the cathode of the Short-Baseline Near Detector (SBND), a liquid argon time projection chamber located along the Fermilab Booster Neutrino Beam. To increase and homogenize scintillation-light collection, 64 double-sided plates were fabricated from FR4, laminated with specular reflector film and coated with 300 $\mu$g/cm$^2$ of tetraphenyl butadiene (TPB) wavelength shifter using controlled physical vapor deposition. The coating uniformity was validated through dedicated measurements of deposited mass and profilometry studies. Because exposure to ambient blue/UV light could degrade the TPB, protective filtering and controlled storage conditions were implemented during handling and installation. The coated plates were assembled between conductive meshes for high-voltage compatibility and installed in situ during detector integration. This system constitutes the largest TPB-coated area deployed in a neutrino detector. It operates in conjunction with SBND's photon detection system, which consists of photomultiplier tubes and X-ARAPUCAs. Early light-collection measurements show high uniformity and light response across the detector, supporting improved triggering, calorimetry, and position reconstruction in SBND.

physics.ins-det↗

arXiv2025

Sampling Off-Axis Neutrino Fluxes with the Short-Baseline Near Detector

The Short-Baseline Near Detector (SBND), the near detector in the Short-Baseline Neutrino Program at Fermi National Accelerator Laboratory, is located just 110 m from the Booster Neutrino Beam target. Thanks to this close proximity, relative to its 4 m $\times$ 4 m front face, neutrinos enter SBND over a range of angles from $0^{\circ}$ to approximately $1.6^{\circ}$, enabling the detector to sample variations in the neutrino flux as a function of angle-a technique known as PRISM, referred to here as SBND-PRISM. In this paper, we show how muon- and electron-neutrino fluxes vary as a function of the neutrino beam axis angle and how this can be exploited to expand the physics potential of SBND. We make use of a model that predicts an angle-dependent electron-neutrino excess signal to illustrate this effect, such as $\nu_\mu \to \nu_e$ oscillations. We present how SBND-PRISM provides a method to add robustness against uncertainties in cross-section modeling and, more generally, uncertainties that do not depend on the spatial position of neutrino interaction inside the detector. The fluxes, along with their associated covariance matrices, are made publicly available with this publication.

hep-ex↗

arXiv2025

Optimization of a cosmic muon tomography scanner for cargo border control inspection

The past several decades have seen significant advancement in applications using cosmic-ray muons for tomography scanning of unknown objects. One of the most promising developments is the application of this technique in border security for the inspection of cargo inside trucks and sea containers in order to search for hazardous and illicit hidden materials. This work focuses on the optimization studies for a muon tomography system similar to that being developed within the framework of the `SilentBorder' project funded by the EU Horizon 2020 scheme. Current studies are directed toward optimizing the detector module design, following two complementary approaches. The first leverages TomOpt, a Python-based end-to-end software that employs differentiable programming to optimize scattering tomography detector configurations. While TomOpt inherently supports gradient-based optimization, a Bayesian Optimization module is introduced to better handle scenarios with noisy objective functions, particularly in image reconstruction-driven optimization tasks. The second optimization strategy relies on detailed GEANT4-based simulations, which, while more computationally intensive, offer higher physical fidelity. These simulations are also employed to study the impact of incorporating secondary particle information alongside cosmic muons for improved material discrimination. This paper presents the current status and results obtained from these optimization studies.

physics.ins-det↗

arXiv2025

The Short-Baseline Near Detector at Fermilab

SBND is a 112 ton liquid argon time projection chamber (LArTPC) neutrino detector located 110 meters from the Booster Neutrino Beam (BNB) target at Fermilab. Its main goals include searches for eV-scale sterile neutrinos as part of the Short-Baseline Neutrino (SBN) program, other searches for physics beyond the Standard Model, and precision studies of neutrino-argon interactions. In addition, SBND is providing a platform for LArTPC neutrino detector technology development and is an excellent training ground for the international group of scientists and engineers working towards the upcoming flagship Deep Underground Neutrino Experiment (DUNE). SBND began operation in July 2024, and started collecting stable neutrino beam data in December 2024 with an unprecedented rate of ~7,000 neutrino events per day. During its currently approved operation plans (2024-2027), SBND is expected to accumulate nearly 10 million neutrino interactions. The near detector dataset will be instrumental in testing the sterile neutrino hypothesis with unprecedented sensitivity in SBN and in probing signals of beyond the Standard Model physics. It will also be used to significantly advance our understanding of the physics of neutrino-argon interactions ahead of DUNE. After the planned accelerator restart at Fermilab (2029+), opportunities are being explored to operate SBND in antineutrino mode in order to address the scarcity of antineutrino-argon scattering data, or in a dedicated beam-dump mode to significantly enhance sensitivity to searches for new physics. SBND is an international effort, with approximately 40% of institutions from Europe, contributing to detector construction, commissioning, software development, and data analysis. Continued European involvement and leadership are essential during SBND's operations and analysis phase for both the success of SBND, SBN and its role leading up to DUNE.

hep-ex↗

arXiv2024

Scintillation Light in SBND: Simulation, Reconstruction, and Expected Performance of the Photon Detection System

SBND is the near detector of the Short-Baseline Neutrino program at Fermilab. Its location near to the Booster Neutrino Beam source and relatively large mass will allow the study of neutrino interactions on argon with unprecedented statistics. This paper describes the expected performance of the SBND photon detection system, using a simulated sample of beam neutrinos and cosmogenic particles. Its design is a dual readout concept combining a system of 120 photomultiplier tubes, used for triggering, with a system of 192 X-ARAPUCA devices, located behind the anode wire planes. Furthermore, covering the cathode plane with highly-reflective panels coated with a wavelength-shifting compound recovers part of the light emitted towards the cathode, where no optical detectors exist. We show how this new design provides a high light yield and a more uniform detection efficiency, an excellent timing resolution and an independent 3D-position reconstruction using only the scintillation light. Finally, the whole reconstruction chain is applied to recover the temporal structure of the beam spill, which is resolved with a resolution on the order of nanoseconds.

physics.ins-det↗

arXiv2020

Cosmic 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 semantic segmentation on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, at single image-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.

physics.data-an↗