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At least 127 records · Page 7

Neutrino Beam Monitoring

Accelerator facilities produce neutrino beams from meson decays in a decay volume. Experiments measure event rates that depend on flux, cross sections, and detector response, so the flux is predicted using hadron production and beamline modeling and constrained by beam instrumentation, since near detectors alone cannot separate flux from cross section. Proton, hadron, and muon monitors can track the parent particle distributions and beam conditions, providing the inputs needed for flux predictions in long-baseline experiments such as NOvA, T2K, and DUNE. This talk reviews how beam monitors are used in practice to understand neutrino flux. Proton beam monitors tell where the beam hits the target and how stable it is. Farther downstream, hadron and muon monitors sample particles produced in meson decays. Because those muons come from the same parents as the neutrinos, their profiles reveal focusing, alignment shifts, and other changes in the beam, and they are routinely used to detect problems and guide flux predictions. The muon information can be used more quantitatively; for example, to infer the parent meson phase space, and fast radiation-hard timing detectors can add sensitivity to the momentum dependence of the focusing. These developments show both how tightly beam measurements can constrain the flux and where the current limits still lie. These approaches complement monitored-beam concepts, in which the decay region is instrumented to detect charged leptons from meson decays and to measure the neutrino flux directly.

Ganguly, Sudeshna [Fermilab] (ORCID:00000003163482↗

A Joint Search for Muon Neutrino Disappearance with the Short-Baseline Neutrino Program Using the SPINE Deep Learning-Based Reconstruction Package

We present the status of a joint search for muon neutrino disappearance in the Booster Neutrino Beam at Fermilab using the Short-Baseline Neutrino (SBN) Program's two-detector configuration, SBND and ICARUS. Charged-current interactions consistent with muon neutrinos and containing only a muon and at least one proton in the final state are reconstructed and selected using the SPINE deep learning-based particle reconstruction package. To exploit proton multiplicity information and enhance sensitivity to modeling effects, the selected sample is partitioned into exclusive channels with exactly one reconstructed proton and with more than one reconstructed proton. Comprehensive systematic uncertainties from the neutrino flux, interaction, and detector response are incorporated into the analysis, and the coverage of these systematic uncertainty models is validated using data from both detectors, including checks of near-far consistency in key kinematic and topology-sensitive observables. This analysis is intended for inclusion in SBN's first oscillation result.

Mueller, Justin [Fermilab]↗

First-Scatter Source Generation in GADRAS

A new scatter calculation algorithm has been implemented in the Gamma Detector Response and Analysis Software (GADRAS) package that accounts for spectral effects of scattering materials not in the line of sight of the detector and the source. Previously, GADRAS would only apply scattering effects due to materials that fall between the source and detector. This new routine will allow better modeling of various scenarios including gamma imagers, collimated detectors, or traditional gamma detectors where scattering materials are present.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Long-lived heavy neutral leptons with a displaced shower signature at $\mathrm{CMS}$

We study the LHC discovery potential in the search for heavy neutral leptons (HNL) with a new signature: a displaced shower in the CMS muon detector, giving rise to a large cluster of hits forming a displaced shower. A new Delphes module is used to model the CMS detector response for such displaced decays. We reinterpret a dedicated CMS search for neutral long-lived particles decaying in the CMS muon endcap detectors for the minimal HNL scenario. We demonstrate that this new strategy is particularly sensitive to active-sterile mixings with τ leptons, due to hadronic τ decays. HNL masses between ~ 1–6 GeV can be accessed for mixings as low as |V τN | 2 ~10 -7 , probing unique regions of parameter space in the τ sector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Surface Trap Dynamics and Gamma‐Ray Detection Enhancement in Passivated FAPbBr 3 Perovskite Crystals

Perovskite‐based direct radiation detectors offer a compelling platform for room‐temperature gamma spectroscopy due to their high sensitivity, tunable optoelectronic properties, and compatibility with solution processing. While prior studies on formamidinium lead tribromide (FAPbBr 3 ) have shown that surface passivation mitigates deep trap state impact on the charge collection efficiency (CCE), the influence of surface‐based shallow traps on time‐resolved detector response remains largely unexplored. In this article, a digital signal processing (DSP) method is applied to analyze waveform rising edge dynamics in FAPbBr 3 single‐crystal detectors, identifying prompt and delayed charge collection components. Using the second derivative of the collected charge signal, the onset of shallow trap reemission is isolated, occurring 1.1 µs after carrier generation. Surface passivation suppressed deep trap states, shifting carrier occupancy toward shallower traps and extending the temporal window of delayed charge collection. This delayed contribution correlates with measurable changes in the temporal and amplitude distributions of detector events. By leveraging time‐domain filtering informed by these dynamics, a selective enhancement in signal quality and gamma peak‐to‐Compton ratio (PCR) is achieved, while preserving over 90% of photoelectric events. An improvement in the PCR to 1.80, from the original 1.09, is enabled by focusing on high collection efficiency events corresponding to reemitted carriers in the 1.1 to 3 µs range after the generation event. These results demonstrate a pathway for leveraging shallow trap dynamics to enhance spectral fidelity in time‐resolved perovskite detectors.

36 MATERIALS SCIENCE↗

Supernova electron-neutrino interactions with xenon in the nEXO detector

Electron-neutrino charged-current interactions with xenon nuclei were modeled in the nEXO neutrinoless double-𝛽 decay detector (∼5 metric ton, 90% 136 Xe, 10% 134 Xe) to evaluate its sensitivity to supernova neutrinos. Predictions for event rates and detectable signatures were modeled using the Model of Argon Reaction Low Energy Yields (MARLEY) event generator. We find good agreement between MARLEY’s predictions and existing theoretical calculations of the inclusive cross sections at supernova neutrino energies. The interactions modeled by MARLEY were simulated within the nEXO simulation framework and were run through an example reconstruction algorithm to determine the detector’s efficiency for reconstructing these events. The simulated data, incorporating the detector response, were used to study the ability of nEXO to reconstruct the incident electron-neutrino spectrum and these results were extended to a larger xenon detector of the same isotope enrichment. We estimate that nEXO will be able to observe electron-neutrino interactions with xenon from supernovae as far as 5–8 kpc from Earth, while the ability to reconstruct incident electron-neutrino spectrum parameters from observed interactions in nEXO is limited to closer supernovae.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Design and characterization of AmLi neutron sources for the LZ experiment

In this paper we describe the development, testing, and characterization of three low-emission rate AmLi neutron sources. The sources are used to calibrate the nuclear recoil response of the LUX-ZEPLIN (LZ) dark matter experiment. Furthermore, the sources' neutron emission rate was measured using 3He proportional tubes. The sources' gamma emissions were characterized using a high-purity germanium (HPGe) detector. Source-validated Geant4Monte Carlo simulations allowed to calibrate the Ge and neutron detectors' responses.

47 OTHER INSTRUMENTATION↗

Measurement of the pion charge exchange differential cross section on Argon with the ProtoDUNE-SP detector

The Deep Underground Neutrino Experiment (DUNE) utilizes Liquid Argon Time Projection Chamber (LArTPC) technology in its pursuit of uncovering the origins of matter. As a crucial prototype for the DUNE far detector (FD) module, ProtoDUNE-SP at CERN Neutrino Platform Facility employs a hadron test beam with a momentum range of 0.3 to 7 GeV/c to study the detector response to particles that could be produced in the neutrino interactions at the DUNE FD. Furthermore, ProtoDUNE-SP is dedicated to testing event reconstruction and performing detector calibration under controlled conditions. Neutral pion reconstruction performance is an important benchmark of the calorimetric and tracking capability of LArTPC, and understanding neutral pion in LArTPC is crucial to characterize backgrounds to oscillation measurements and rare searches. To improve neutral pion reconstruction in LArTPC, a kinematic fitting algorithm is developed, achieving a 12% resolution in neutral pion energy in ProtoDUNE-SP. This enhancement enables the first measurements of the pion-argon charge exchange differential cross-section. By comparing these results with the current model, valuable constraints can be made to improve generator simulations in the future DUNE experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

An energy-dependent electro-thermal response model of CUORE cryogenic calorimeter

The Cryogenic Underground Observatory for Rare Events (CUORE) is the most sensitive experiment searching for neutrinoless double-beta decay (0νββ) in 130 Te. CUORE uses a cryogenic array of 988 TeO 2 calorimeters operated at ~10 mK with a total mass of 741 kg. To further increase the sensitivity, the detector response must be well understood. Here, we present a non-linear thermal model for the CUORE experiment on a detector-by-detector basis. We have examined both equilibrium and dynamic electro-thermal models of detectors by numerically fitting non-linear differential equations to the detector data of a subset of CUORE channels which are well characterized and representative of all channels. Further, we demonstrate that the hot-electron effect and electric-field dependence of resistance in NTD-Ge thermistors alone are inadequate to describe our detectors' energy-dependent pulse shapes. We introduce an empirical second-order correction factor in the exponential temperature dependence of the thermistor, which produces excellent agreement with energy-dependent pulse shape data up to 6 MeV. We also present a noise analysis using the fitted thermal parameters and show that the intrinsic thermal noise is negligible compared to the observed noise for our detectors.

47 OTHER INSTRUMENTATION↗

Detector Characterization for Quantitative Spectral Radiography of Uranium Powder Samples

Pacific Northwest National Laboratory (PNNL) is developing a prototype spectral x-ray radiography (SpecX) non-destructive assay system for noninvasive material mass quantification of uranium-bearing powders. The SpecX system uses a custom material estimation algorithm, which requires a high-fidelity pixel-by-pixel detector response model for accurate quantification. We performed a detailed characterization and built a model of a High-Energy X-ray Imaging Technology (HEXITEC) CdTe pixelated spectral detector. Here we present the detector characterization which led to that model, including a determination of the pixel-wise energy calibration, the detection efficiency, the detector uniformity, the detector stability, and a comparison of methods to remove charge-shared events.

spectral X-ray radiography, pixelated detector, ca↗

Automated detector simulation and reconstruction parametrization using machine learning

Rapidly applying the effects of detector response to physics objects (e.g. electrons, muons, showers of particles) is essential in high energy physics. Presently available tools for the transformation from truth-level physics objects to reconstructed detector-level physics objects involve manually defining resolution functions. These resolution functions are typically derived in bins of variables that are correlated with the resolution (e.g. pseudorapidity and transverse momentum). This process is time consuming, requires manual updates when detector conditions change, and can miss important correlations. Machine learning offers a way to automate the process of building these truth-to-reconstructed object transformations and can capture complex correlation for any given set of input variables. Such machine learning algorithms, with sufficient optimization, could have a wide range of applications: improving phenomenological studies by using a better detector representation, allowing for more efficient production of Geant4 simulation by only simulating events within an interesting part of phase space, and studies on future experimental sensitivity to new physics.

47 OTHER INSTRUMENTATION↗

Review of Neutron Uncertainty Types

The assessment of neutron data requires understanding the uncertainty. The uncertainty of the final quantities of interest, neutron multiplication and mass, is a combination of many other uncertainties because it is a combination of many other variables. The equations for the values of neutron multiplication and mass are quite complex. The relations between the many contributing variables and their uncertainties are complicated leading to complications in combining them. Additionally, some are statistical in nature, while others are systematic. In an effort to better understand and quantify the total uncertainty in neutron multiplication and SNM mass, they have been subdivided into four categories: setup, detector response, nuclear data, and object characterization. This paper discusses the four categories and what types of uncertainties are in each of them. This effort attempts to generalize this process for any neutron detector, but many of our examples show the Next Generation Multiplicity Detector (MC-15).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Generalizing to new geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation

Generation of simulated detector response to collision products is crucial to data analysis in particle physics, but computationally very expensive. One subdetector, the calorimeter, dominates the computational time due to the high granularity of its cells and complexity of the interactions. Generative models can provide more rapid sample production, but currently require significant effort to optimize performance for specific detector geometries, often requiring many models to describe the varying cell sizes and arrangements, without the ability to generalize to other geometries. Here, we develop a geometry-aware autoregressive model, which learns how the calorimeter response varies with geometry, and is capable of generating simulated responses to unseen geometries without additional training. The geometry-aware model outperforms a baseline unaware model by over 50% in several metrics such as the Wasserstein distance between the generated and the true distributions of key quantities which summarize the simulated response. A single geometry-aware model could replace the hundreds of generative models currently designed for calorimeter simulation by physicists analyzing data collected at the Large Hadron Collider. This proof-of-concept study motivates the design of a foundational model that will be a crucial tool for the study of future detectors, dramatically reducing the large upfront investment usually needed to develop generative calorimeter models.

47 OTHER INSTRUMENTATION↗

Validation of neutron-induced reactions on natural carbon using an active target at neutron energies up to 22 MeV at LANSCE

A single crystal chemical vapor deposited (sCVD) diamond detector is used as an active target to measure neutron-induced reactions on natural carbon using the neutrons produced by spallation, with a broad energy spectrum at LANSCE. Additionally, the neutron-induced reactions are detected in the diamond as low as E n = 400 keV and up to approximately 100 MeV. Relative cross sections for C 12 ( n , α 0 ) , C 12 ( n , p 0 ) , C 12 ( n , d 0 + p 1 ) , and C 13 ( n , α 0 ) are reported up to E n = 22 MeV and comparisons on detected pulse-height spectra and detector response of scattering reactions are made with GEANT4 simulations using the ENDF/B-VIII.0 evaluated nuclear data library up to 20 MeV. The results are compared with past experimental data, including other works that incorporate diamond detectors as an active carbon target. In addition, R-matrix calculations for the C 13 + n system are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Numerical Investigation of Fluid Flow and Space Charge in Liquid Argon Time Projection Chamber (LArTPC) Detectors

Overview This project focused on developing a high-fidelity numerical framework to simulate the multiphysics environment within Liquid Argon Time Projection Chamber (LArTPC) detectors. The primary objective was to characterize the complex interplay between ion transport, background fluid dynamics, and electric field distortions—a critical factor for the calibration and sensitivity of next-generation High Energy Physics experiments, such as DUNE. Technical Achievements The research successfully yielded a hybrid numerical space-charge solver utilizing a Cell-Centered Finite Volume Method (FVM) for ion transport coupled with a Finite Element Method (FEM) for electric potential. Key accomplishments include: • Verification & Validation: The 3-D solver was rigorously verified against 1-D analytical solutions, demonstrating high numerical accuracy in predicting space-charge-induced field deviations. • Field Distortion Analysis: 3D simulations revealed that space charge effects introduce significant non-uniformities in the electric field. Critically, the research identified that background LAr flow velocities, when comparable to ion drift velocities, markedly exacerbate these distortions. • Technology Transfer: The resulting source code and comprehensive user manuals were successfully transferred to collaborators at Fermilab, providing a portable computational tool for the broader scientific community. Challenges and Future Directions While the space-charge solver achieved all performance metrics, the integrated fluid dynamics modeling encountered convergence challenges stemming from the extreme 200-fold disparity in length scales between the detector's 37 mm inlet pipes and the 8-meter global domain. To address this, the project has identified a clear technical pivot toward Hierarchical Geometric Adaptive Mesh Refinement (HG-AMR). By implementing an h-type refinement strategy with hanging nodes, future iterations of this solver will be capable of resolving localized high-gradient inlet flows without the prohibitive computational costs of regular grids. This advancement, combined with data-driven uncertainty quantification based on MicroBooNE-style calibration, will enable the precise modeling of detector responses in large-scale cryogenic environments where direct measurement remains difficult. Impact The computational tools developed under this award provide a foundation for enhancing the energy resolution and spatial reconstruction of noble liquid detectors. By bridging the gap between theoretical fluid dynamics and experimental field calibration, this work supports the DOE’s mission to advance the frontiers of neutrino physics and dark matter detection.

42 ENGINEERING↗

AIACHNE's contribution for Nuclear Energy Agency Working Party on International Nuclear Data Evaluation Co-operation Subgroup 50

The AIACHNE (AI/ML Informed cAlifornium CHi Nuclear data Experiment) project aims at designing an experiment for the 252 Cf Prompt Fission Neutron Spectrum (PFNS) that explores systematic biases in an experimental database retrieved from the EXFOR databases. To that end, machine learning (ML) methods were applied to pint-point measurement features likely related to bia. From that information, we selected a feature that should be explored by the AIACHNE experiment. Measurement features are metadata encapsulating all pertinent information about the physical measurement and analysis techniques. Examples are, for instance, what neutron and fission detectors were used for the physical metadata, and what background reduction techniques were employed for analysis techniques. Such metadata were retrieved both from EXFOR entries as well as the literature of data sets described in detail in Ref. [2]. The prerequisite for applying machine learning techniques is casting the metadata into a format that can be parsed by the algorithm. This step might seem trivial but requires to find a unique language where metadata that carry the same physics meaning across several experiments must have the same identifier. One example is, for instance, the neutron detector. As seen in Figure 1, the machine learning code identified the use of 6 Li detectors as being related to bias in some datasets of the AIACHNE 252 Cf PFNS experimental database. In fact, here are several experiments that used neutron detectors containing 6Li in the database, for instance for the example below. EXFOR format has a unique keywords describing detectors such as “SCIN” or “GLASD”. One may think that these keywords are already sufficient descriptors for ML to uniquely find an issue. However, “SCIN” (used for [3, 4]) and “GLASD” (used for [5]) fail to inform the algorithm what is the active material in the detector. And, the key common issue leading to bias in 252 Cf related to neutron detectors is not whether it is a glass detector or a scintillator. No, the issue is that 6 Li was within both detector types and that even small mistakes in the detector response functions around approximately 200 keV are amplified by the 6 Li(n,α) resonance there leading to bias in data as highlighted in Fig. 1 and Ref. [1]. Hence, the features describing the neutron detector must call out the active material in the detector, rather than the existing EXFOR detector keyword, that the ML algorithm can find physically meaningful features related to bias. The AIACHNE team used a precursor of the WPEC (Working Party on International Nuclear Data Evaluation Co-operation) SG(Subgroup)-50 format to store the metadata for the ML analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Inference of the neutron down-scatter ratio using neutron time-of-flight data for DT-layered implosions on the OMEGA laser

First analysis of the neutron time of flight (nTOF) data is presented to infer the areal density using the neutron down-scatter ratio (DSR) for OMEGA deuterium–tritium (DT)-layered (cryogenic) implosions. The required very-high-dynamic range (nTOF) signal is constructed using an nTOF detector [Forrest et al., Rev. Sci. Ins. 83, 10D919 (2012)] with multiple time-gated photomultiplier tubes. The nTOF data are analyzed using a forward-fit technique to account for the detector responses and infer the DSR and areal density for DT-layered implosions. The areal densities inferred using this analysis are found to be in agreement with the areal densities measured using the magnetic recoil spectrometer detector that is located in a similar line-of-sight. This work establishes the feasibility of the DSR measurement using nTOF data on OMEGA and provides an additional areal-density measurement that will benefit the assessment of the implosion performance and 3D reconstruction of the imploded core.

47 OTHER INSTRUMENTATION↗

Evaluation of the response to electrons and pions in the scintillating fiber and lead calorimeter for the future electron-ion collider

The performance of the Baby Barrel Electromagnetic Calorimeter (Baby BCAL) — a small-scale lead-scintillating-fiber (Pb/ScFi) prototype of the GlueX Barrel Electromagnetic Calorimeter (BCAL) — was tested in a dedicated beam campaign at the Fermilab Test Beam Facility (FTBF). This study provides a benchmark for the Pb/ScFi component of the future Barrel Imaging Calorimeter (BIC) in the ePIC detector at the Electron-Ion Collider (EIC). The detector response to electrons and pions was studied at beam energies between 4 and 10 GeV, extending previous GlueX tests to a higher energy regime. The calibrated detector exhibits good linearity within uncertainties, and its electron energy resolution meets EIC requirements. The data further constrain the constant term in the energy resolution to below 1.9%, improving upon previous constraints at lower energies. Simulations reproduce key features of the electron and pion data within the limitations of the collected dataset and the FTBF test environment. Electron-pion separation in the test beam setup was analyzed using multiple methods, incorporating varying degrees of beam-related effects. The inclusion of longitudinal shower profile information enhanced the separation performance, underscoring its relevance for the full-scale BIC in ePIC. These results provide essential benchmarks for the Pb/ScFi section of the future BIC, validating detector simulations and guiding optimization strategies for electron-pion discrimination.

47 OTHER INSTRUMENTATION↗