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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Light and Dark in Liquid Argon Time Projection Chamber Neutrino Detectors

Liquid Argon Time Projection Chamber (LArTPC) neutrino detectors have emerged as a detector technology poised to perform measurements of neutrino interactions with unprecedented precision and as a result answer some of the largest open questions in neutrino physics in the coming decade. This thesis describes methods developed to tackle the computational challenges faced as LArTPC detectors increase in scale and complexity moving towards the multi-kiloton DUNE detectors. These include a new, approximated, model that enables rapid simulation of scintillation light in very large scale detectors, as well as the first demonstration of running the LArSoft software framework on a high performance computer. The high precision of LArTPC detectors designed for next-generation neutrino measurements enables them to also search for Beyond the Standard Model physics produced in high-energy proton--fixed-target collisions in neutrino beams. This thesis presents searches for two dark-sector models performed with the ArgoNeuT experiment: Heavy Neutral Leptons and Heavy QCD Axions. Between them, these models can provide solutions for various unresolved puzzles including neutrino mass generation, the baryon asymmetry of the universe, dark matter and the strong CP problem. In both cases, the dark-sector particles could be produced in the NuMI neutrino beam and can then decay to a pair of oppositely charged muons observable in ArgoNeuT and the downstream MINOS near detector. Both measurements required the development of novel experimental selection techniques and enabled new constraints to be set on the existence of these particles in previously unexplored parameter-space. These searches are both the first of their kind in LArTPC neutrino detectors and pave the way for searches at future neutrino facilities.Date of Award31 Dec 2022Original languageEnglishAwarding InstitutionThe University of ManchesterSupervisorStefan Soldner-Rembold (Supervisor) & Justin Evans (Supervisor)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Modeling light signals using data from the first pulsed neutron source program at the DUNE vertical drift ColdBox test facility at the CERN Neutrino Platform

In this paper, we present a first quantitative test of detected light signals produced in a pulsed neutron source run in a small vertical drift LArTPC at the CERN Neutrino Platform ColdBox test facility. The ColdBox cryostat, detectors, neutron sources, and particle interactions are modeled and simulated using Fluka. We demonstrate the ability to identify the contribution from neutron interactions using X-ARAPUCA photodetectors, and show first comparisons of data to simulation, which indicate reasonable agreement. A time constant is also fitted from the neutron-beam-off light signal spectrum and found consistent between data and simulation. Several important systematic effects are discussed and serve as guides for future runs at larger LArTPCs.

Detector modelling and simulations I (interaction ↗
DOE OSTI2024

Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction

Detector simulation and reconstruction are a significant computational bottleneck in particle physics. Here, we develop particle-flow neural-assisted simulations (parnassus) to address this challenge. Our deep learning model takes as input a point cloud (particles impinging on a detector) and produces a point cloud (reconstructed particles). By combining detector simulations and reconstruction into one step, we aim to minimize resource utilization and enable fast surrogate models suitable for application both inside and outside large collaborations. We demonstrate this approach using a publicly available dataset of jets passed through the full simulation and reconstruction pipeline of the Compact Muon Solenoid (CMS) experiment. We show that parnassus accurately mimics the CMS particle flow algorithm on the (statistically) same events it was trained on and can generalize to jet momentum and type outside of the training distribution.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Machine Learning Neutrino-Nucleus Cross Sections

Neutrino-nucleus scattering cross sections are critical theoretical inputs for long-baseline neutrino oscillation experiments. However, robust modeling of these cross sections remains challenging. For a simple but physically motivated toy model of the DUNE experiment, we demonstrate that an accurate neural-network model of the cross section -- leveraging Standard Model symmetries -- can be learned from near-detector data. We then perform a neutrino oscillation analysis with simulated far-detector events, finding that the modeled cross section achieves results consistent with what could be obtained if the true cross section were known exactly. This proof-of-principle study highlights the potential of future neutrino near-detector datasets and data-driven cross-section models.

Wagman, Michael L. [Fermilab] (ORCID:0000000176701↗
DOE OSTI2025

Measurement of three-dimensional inclusive muon-neutrino charged-current cross sections on argon with the MicroBooNE detector

We report the measurement of the triple-differential cross section d 3 σ/dE vis d cos(θ μ )dP μ for inclusive muon-neutrino charged-current scattering on argon. This measurement utilizes data from 6.4 x 10 20 protons on target of exposure collected using the MicroBooNE liquid argon time projection chamber located along the Fermilab Booster Neutrino Beam with a mean neutrino energy of approximately 0.8 GeV. The mapping from reconstructed kinematics to truth quantities is validated within uncertainties by comparing the distribution of reconstructed hadronic energy in data to that of the model prediction in different muon scattering angle bins after applying a conditional constraint from the muon momentum distribution in data. The success of this validation provides confidence that the energy transfer in the MicroBooNE detector is well-modeled within simulation uncertainties, enabling a reliable unfolding to a triple-differential cross section defined at the nominal neutrino flux over muon momentum, muon scattering angle, and visible neutrino energy. This validation not only supports accurate cross-section extraction, but also establishes a critical foundation for tuning interaction models used in future neutrino oscillation measurements. The unfolded measurement covers an extensive phase space, providing a wealth of information useful for future liquid argon time projection chamber experiments measuring neutrino oscillations. Comparisons against a number of commonly used model predictions are included and their performance in different parts of the available phase-space is discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Real-Time event reconstruction for Nuclear Physics Experiments using Artificial Intelligence

Charged track reconstruction is a critical task in nuclear physics experiments, enabling the identification and analysis of particles produced in high-energy collisions. Machine learning (ML) has emerged as a powerful tool for this purpose, addressing the challenges posed by complex detector geometries, high event multiplicities, and noisy data. Traditional methods rely on pattern recognition algorithms like the Kalman filter, but ML techniques, such as neural networks, graph neural networks (GNNs), and recurrent neural networks (RNNs), offer improved accuracy and scalability. By learning from simulated and real detector data, ML models can identify and classify tracks, predict trajectories, and handle ambiguities caused by overlapping or missing hits. Moreover, ML-based approaches can process data in near-real-time, enhancing the efficiency of experiments at large-scale facilities like the Large Hadron Collider (LHC) and Jefferson Lab (JLAB). As detector technologies and computational resources evolve, ML-driven charged track reconstruction continues to push the boundaries of precision and discovery in nuclear physics. In these proceedings, we highlight advancements in charged track identification leveraging Artificial Intelligence within the CLAS12 detector, achieving a notable enhancement in experimental statistics compared to traditional methods. Additionally, we showcase real-time event reconstruction capabilities, including the inference of charged particle properties, such as momentum, direction, and species identification, at speeds matching data acquisition rates. These innovations enable the extraction of physics observables directly from the experiment in real-time.

Gavalian, Gagik (ORCID:0000000267385457)↗
DOE OSTI2023

Large-volume CdZnTe bar detectors characterized by laser-induced transient currents

In this work, we studied the free-carrier transport properties and space-charge formation/polarization in a CdZnTe bar-shaped radiation detector using the Laser-Induced Transient Current technique. We found out that a steady-state space charge ranging from 8 x 10 8 to 2.1 x 10 9 cm -3 is formed throughout the detector at about 0.5 s following biasing. The measured current waveforms were modeled by Monte Carlo simulations after taking into account the electric and weighting fields inside the detector and shielding box. Modeling of the unpolarized waveforms revealed an exceptionally high electron mobility-lifetime product μ e τ e ≥0.095cm 2 V -1 . The observed formation of positive space charge in the biased detector and linear scaling of the current waveform shapes on applied bias revealed that the detector polarization is attributed to carrier injection at the anode. In conclusion, measurements in pulsed and DC bias in modified electrode geometry proved the surface-charge formation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Secondary-Source Core Reload Modeling with VERA

The CASL reactor simulation package VERA has been adapted to provide high-fidelity simulation capabilities for modeling source range detector response during subcritical reactor configurations. New features include the activation and shuffling of secondary-source assemblies, use of burned fuel neutron emission data from the ORIGEN depletion solver to the MPACT deterministic neutron transport solver, allowance of user-defined sources in MPACT based on material composition, ability to solve the subcritical source-driven system with neutron multiplication using the MPACT diffusion solver, and transfer of the calculated fission source from MPACT to the continuous-energy Monte Carlo solver Shift for final detector response evaluation using the CADIS methodology for variance reduction. These new capabilities were benchmarked against Watts Bar Unit 1 plant operating data for the first few fuel loading steps and were found to demonstrate excellent agreement with the measured data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Acceptance tests of Hamamatsu R7081 photomultiplier tubes

Photomultiplier tubes (PMTs) are traditionally an integral part of large underground experiments as they measure the light emission from particle interactions within the enclosed detection media. The BUTTON experiment will utilise around 100 PMTs to measure the response of different media suitable for rare event searches. A subset of low-radioactivity 10-inch Hamamatsu R7081 PMTs were tested, characterised, and compared to manufacture certification. This manuscript describes the laboratory tests and analysis of gain, peak-to-valley ratio and dark rate of the PMTs to give an understanding of the charge response, signal-to-noise ratio and dark noise background as an acceptance test of the suitability of these PMTs for water-based detectors. Following the evaluation of these tests, the PMT performance agreed with the manufacturer specifications. These results are imperative for modeling the PMT response in detector simulations and providing confidence in the performance of the devices once installed in the detector underground.

neutrino detectors↗
DOE OSTI2023

Ratpac-Two v1.0.0

Geant-4 based detector simulations in c++ which combines simulation and analysis into a single framework to precisely model detector components and analyze experimental data. Ratpac allows the user to easily build new detector geometries and configurations through both the source library and through configurable text files at runtime.

Askins, Morgan↗
DOE OSTI2022

FlameNEST: explicit profile likelihoods with the Noble Element Simulation Technique

We present FlameNEST, a framework providing explicit likelihood evaluations in noble element particle detectors using data-driven models from the Noble Element Simulation Technique. FlameNEST provides a way to perform statistical analyses on real data with no dependence on large, computationally expensive Monte Carlo simulations by evaluating the likelihood on an event-by-event basis using analytic probability elements convolved together in a single TensorFlow multiplication. Furthermore, this robust framework creates opportunities for simple inter-collaboration analyses which will be fundamental for the future of experimental dark matter physics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Improving Detector Systematic Uncertainties Through Data-Driven Machine Learning

Detector simulation in liquid argon time projection chambers (LArTPCs) is a constant challenge. In particular the modeling of electrons response on wires is highly nontrivial. However, new machine learning techniques exist which can be leveraged to ameliorate these concerns. We present a novel methodology to attempt to learn from cosmic muon data in the ICARUS detector how reconstructed wire signals are influenced by features of the hits such as location, particle direction, angle relative to the wire plane, etc. A model can then be generated which can apply the learned mapping to Monte Carlo events to create a more data-like simulation sample. By creating such a sample we expect to reduce the systematic uncertainties at ICARUS due to our detector modeling.

Hausner, Harry [Fermilab] (ORCID:0000000188932280)↗
DOE OSTI2026

Improving Detector Systematic Uncertainties Through Data-Driven Machine Learning

Detector simulation in liquid argon time projection chambers (LArTPCs) is a constant challenge. In particular the modeling of electrons response on wires is highly nontrivial. However, new machine learning techniques exist which can be leveraged to ameliorate these concerns. We present a novel methodology to attempt to learn from cosmic muon data in the ICARUS detector how reconstructed wire signals are influenced by features of the hits such as location, particle direction, angle relative to the wire plane, etc. A model can then be generated which can apply the learned mapping to Monte Carlo events to create a more data-like simulation sample. By creating such a sample we expect to reduce the systematic uncertainties at ICARUS due to our detector modeling.

Hausner, Harry [Fermilab] (ORCID:0000000188932280)↗
DOE OSTI2025

Characterization of the Cherenkov Photon Background for Low-noise Silicon Detectors in Space

Future space observatories that seek to perform imaging and spectroscopy of faint astronomical sources will require ultra-low-noise detectors that are sensitive over a broad wavelength range. Silicon charge-coupled devices (CCDs), such as EMCCDs, skipper CCDs, multi-amplifier sensing CCDs, and single-electron sensitive read out CCDs have demonstrated the ability to detect and measure single photons from X-ray energies to near the silicon band gap (∼1.1 μm), making them candidate technologies for this application. Here, in this context, we study a relatively unexplored source of low-energy background coming from Cherenkov radiation produced by energetic cosmic rays traversing a silicon detector. We present a model for Cherenkov photon production and absorption that is calibrated to laboratory data, and we use this model to characterize the residual background rate for ultra-low-noise silicon detectors in space. We study how the Cherenkov background rate depends on detector thickness, variations in solar activity, and the contribution of heavy cosmic ray species (Z > 2). We find that for thick silicon detectors, such as those required to achieve high quantum efficiency at long wavelengths, the rate of cosmic-ray-induced Cherenkov photon production is comparable to other detector and astrophysical backgrounds. We apply our Cherenkov background model to simulated spectroscopic observations of extra-solar planets, and we find that thick detectors continue to outperform their thinner counterparts at longer wavelengths despite a larger Cherenkov background rate. Furthermore, we find that minimal masking of cosmic-ray tracks continues to maximize the signal-to-noise ratio of very faint sources despite the existence of extended halos of Cherenkov photons.

Astronomical detectors↗
DOE OSTI2021

Simulated Performance of the Micro-Pocket Fission Detector in the Advanced Test Reactor Critical Facility

The Micro-Pocket Fission Detector (MPFD) is a small-form–factor real-time fission chamber. MPFD performance has been simulated in the Advanced Test Reactor Critical Facility (ATRC), located at Idaho National Laboratory (INL). Here, the neutron and gamma-ray flux profiles and magnitudes were simulated using Monte Carlo N-Particle (MCNP) in the near-core B-8 irradiation position. These simulations were performed at 69 discrete axial locations inside the B-8 position for three separate orientations of the nearby hafnium outer shim control cylinders and at a power level of 700 Wth. The resulting neutron and gamma-ray flux values were used to determine the MPFD response for various fissile masses and detector gas pressures. The optimal gas-operating pressure was determined to be between 30 and 60 psig. The required fissile-layer mass was determined to be between 1–2 µg of 235 U. Additionally, the gamma-ray to fission-fragment interaction rate was determined to be 4.42 × 10 -3 with average energy deposition for gamma rays and fission fragments in 30 psig argon gas to be 1 keV and 9.5 MeV, respectively.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Irradiated Low-Enriched Uranium Fuel Measurements with a Gamma-Ray Scanning System

A gamma-ray scanning system was designed to perform post-irradiation measurements of nuclear fuel at Idaho National Laboratory (INL). The system is composed of a coaxial high-purity germanium (HPGe) detector, a collimator, and mechanical positioning stages that translate the fuel sample across the front of the collimator. A Monte Carlo N-Particle code simulation of the system and the fuel were created with vendor-supplied design specifications, dimensional measurements, and x-ray radiographs of the HPGe detector as well as all available fuel specifications. Benchmark measurements were performed by scanning an irradiated fuel rodlet containing eight pellets of 0.74% enriched UO2 in zirconium alloy. This fuel rodlet was part of the Static Environment Rodlet Transient Test Apparatus (SERTTA) testing campaign in the Transient Reactor Test Facility (TREAT). These results were compared to simulated spectra to help characterize the detector model and establish fidelity. A goal of the system is to determine the number of fissions per gram of UO2 in the fuel. This paper documents the modeling of the system and the calculation of the number of fissions per gram along the axial length of the SERTTA-C rodlet.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Predicting Missing Regions in Charged Particle Tracks Using a Sparse 3D Convolutional Neural Network

The 2x2 Demonstrator is a prototype of ND-LAr, the liquid argon time-projection chamber of the Deep Underground Neutrino Experiment’s Near Detector complex. Both the 2x2 Demonstrator and ND-LAr are modular detectors that will have pixelated charge readouts and inactive regions wherein there is no sensitivity to charge deposition and light signals that arise from charged particle interactions with liquid argon. In the 2x2, these inactive regions are located in between the active detector modules, which introduces the challenge of inferring what charge signals ought to look like in these regions. This study explores the use of a Sparse 3D Convolutional Neural Network (ConvNet) to infer missing regions in charged particle tracks. Hits corresponding to energy depositions are voxelized into a three-dimensional grid for each track. Voxels that fall into predefined inactive regions are removed to simulate the lack of detector output. The model is trained to infer the topology of the missing track voxels, with the ultimate goal of inferring the missing charge or energy values in these voxels as well. Results indicate that this approach shows promise in prediction of missing track regions with some accuracy.

Utaegbulam, Hilary↗
DOE OSTI2023

Measurements of Irradiated Low Enriched Fuel with a Gamma-Ray Scanning System (ANIMMA 2023 Conference Presentation)

A gamma-ray scanning system was designed to perform post-irradiation measurements of nuclear fuel at Idaho National Laboratory (INL). The system is composed of a coaxial high-purity germanium (HPGe) detector, a collimator, and mechanical positioning stages that translate the fuel sample across the front of the collimator. A Monte Carlo N-Particle code simulation of the system and the fuel were created with vendor-supplied design specifications, dimensional measurements, and x-ray radiographs of the HPGe detector as well as all available fuel specifications. Benchmark measurements were performed by scanning an irradiated fuel rodlet containing eight pellets of 0.74% enriched UO2 in zirconium alloy. This fuel rodlet was part of the Environment Rodlet Transient Test Apparatus (SERTTA) testing campaign in the Transient Reactor Test Facility (TREAT). These results were compared to simulated spectra to help characterize the detector model and establish fidelity. One goal of the system is to determine the number of fissions per gram of UO2 in the fuel. This article documents the modeling, benchmarking measurements, and the calculated number of fissions per gram in the SERTTA-A rodlet which was previously irradiated at TREAT.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗