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NASA NTRS1993

Analysis of SOFCAL calibration data

Ionization calorimeters determine the total energy of a particle by absorbing, within the mass of the calorimeter, the entire energy of the particle or a significant (and determinable) fraction of it. The JACEE group has used passive ionization calorimetery, which employs photographic emulsions as the detector medium, to measure the charge composition and energy spectrum of cosmic rays up to, and exceeding, 10(exp 14)eV. The SOFIC approach depends similarly on the use of three-dimensional nuclear-electromagnetic shower theory to relate the ionization deposits obtained during a shower to the energy of the particle causing the shower. Bundles of thousands of scintillating optical fibers are read out using image-intensified CCD's. Such an event may be triggered by a fast shower detector placed under the instrument or if only heavy particles are of interest, from a fast primary Cerention detector placed above the calorimeter. In the first study, fast Hammatsu photomultiplier tubes were purchased and tested for possible application for a triggering purpose. In the second study, some refinements have been made to the theoretical treatment of hadronic interactions in the central collision region. These will be helpful in improving the simulations necessary for observations of high energy cosmic ray nuclei with a SOFIC.

Gregory, John C.↗

DOE OSTI2024

Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments

Artificial intelligence (AI) generative models, such as generative adversarial networks (GANs), variational autoencoders, and normalizing flows, have been widely used and studied as efficient alternatives for traditional scientific simulations. However, they have several drawbacks, including training instability and inability to cover the entire data distribution, especially for regions where data are rare. This is particularly challenging for whole-event, full-detector simulations in high-energy heavy-ion experiments, such as sPHENIX at the Relativistic Heavy Ion Collider and Large Hadron Collider experiments, where thousands of particles are produced per event and interact with the detector. This work investigates the effectiveness of denoising diffusion probabilistic models (DDPMs) as an AI-based generative surrogate model for the sPHENIX experiment that includes the heavy-ion event generation and response of the entire calorimeter stack. DDPM performance in sPHENIX simulation data is compared with a popular rival, GANs. Results show that both DDPMs and GANs can reproduce the data distribution where the examples are abundant (low-to-medium calorimeter energies). Nonetheless, DDPMs significantly outperform GANs, especially in high-energy regions where data are rare. Additionally, DDPMs exhibit superior stability compared to GANs. The results are consistent between both central and peripheral centrality heavy-ion collision events. Moreover, DDPMs offer a substantial speedup of approximately a factor of 100 compared to the traditional Geant4 simulation method.

42 ENGINEERING↗

NASA NTRS2020

A Fast Evaluation of PDA Experimental Parameters Using Mie Scattering to Enhance the Measurement Accuracy of Droplet Size Distributions

The Phase Doppler technique (PDA) and its implementation generally relies on a geometric optics formulation for light scattering to estimate drop sizes from the measured phase differences between Doppler signals from two or more detectors. Although this limits experimental PDA scattering geometries to those where only one component of the geometric scatter is dominant, the calculations are relatively simple. This TM describes a fast method to evaluate PDA experimental parameters analytically using Mie scattering that provides a performance estimate of all possible configurations. The best configurations are fully tested using a Monte Carlo simulation to obtain a more accurate capability estimate for each one.

PDA↗

NASA NTRS1992

Real-time diagnostics for a reusable rocket engine

A hierarchical, decentralized diagnostic system is proposed for the Real-Time Diagnostic System component of the Intelligent Control System (ICS) for reusable rocket engines. The proposed diagnostic system has three layers of information processing: condition monitoring, fault mode detection, and expert system diagnostics. The condition monitoring layer is the first level of signal processing. Here, important features of the sensor data are extracted. These processed data are then used by the higher level fault mode detection layer to do preliminary diagnosis on potential faults at the component level. Because of the closely coupled nature of the rocket engine propulsion system components, it is expected that a given engine condition may trigger more than one fault mode detector. Expert knowledge is needed to resolve the conflicting reports from the various failure mode detectors. This is the function of the diagnostic expert layer. Here, the heuristic nature of this decision process makes it desirable to use an expert system approach. Implementation of the real-time diagnostic system described above requires a wide spectrum of information processing capability. Generally, in the condition monitoring layer, fast data processing is often needed for feature extraction and signal conditioning. This is usually followed by some detection logic to determine the selected faults on the component level. Three different techniques are used to attack different fault detection problems in the NASA LeRC ICS testbed simulation. The first technique employed is the neural network application for real-time sensor validation which includes failure detection, isolation, and accommodation. The second approach demonstrated is the model-based fault diagnosis system using on-line parameter identification. Besides these model based diagnostic schemes, there are still many failure modes which need to be diagnosed by the heuristic expert knowledge. The heuristic expert knowledge is implemented using a real-time expert system tool called G2 by Gensym Corp. Finally, the distributed diagnostic system requires another level of intelligence to oversee the fault mode reports generated by component fault detectors. The decision making at this level can best be done using a rule-based expert system. This level of expert knowledge is also implemented using G2.

Guo, T. H.↗

NASA NTRS1991

Use of simulated neural networks of aerial image classification

The utility of one layer neural network in aerial image classification is examined. The network was trained with the delta rule. This method was shown to be useful as a classifier in aerial images with good resolution. It is fast, it is easy to implement, because it is distribution-free, nothing about statistical distribution of the data is needed, and it is very efficient as a boundary detector.

Medina, Frances I.↗

DOE OSTI2025

Status of the Mu2e experiment

The Mu2e experiment at Fermilab searches for the coherent, neutrino-less conversion of a μ − to e − in the Coulomb field of Al nuclei, that represents one of the cleanest Charged Lepton Flavor Violating (CLFV) processes for exploring Beyond the Standard Model (BSM) physics. Mu2e aims to improve previous sensitivity by four orders of magnitude, with a distinctive signature provided by identifying mono-energetic electrons with energy slightly below the muon rest mass. To reach this goal, the experiment will use the highest intensity pulsed muon beam in the world, with up to 6 × 1 0 9 stopped muons/sec. This is achieved using the Fermilab proton beam and the design and realization of a unique 25 m long superconducting solenoidal system. The high beam intensity relies upon minimizing beam losses in the slow extraction region, indicating an opportunity of using bent crystals for shadowing. A high-resolution straw tracker and a fast CsI crystal calorimeter identify the conversion electron. Both detectors are inserted behind the Stopping Target in the last solenoid section. A Cosmic Ray Veto covers a large part of the solenoids to suppress background produced by cosmic rays. In this paper, we report the details of the experimental layout, the construction status of the magnetic system and detectors, and a short description of the simulation and realization of the bent crystals. Performing crystal channeling in front of the first slow extraction septa will allow beam shadowing and largely reduce beam losses. •Charge Lepton Flavor Violation processes: μ − → e − conversion.•Large Superconducting Solenoid system.•High-Intensity beams•High precision detectors.•Crystal channeling.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Intelligent Experiments Through Real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and Future EIC Detectors (Final Report)

The overall vision of this project was to integrate real-time artificial intelligence (AI) directly into the data acquisition and detector-control systems of nuclear physics experiments, including both fast online event selection and an autonomous detector-control feedback loop. The work carried out under the award focused on the fast online event-selection half of that vision: the efficient recording of low-momentum heavy-flavor (HF) hadron decays in proton-proton collisions at the sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC)—an observable that requires fast tracking and topological trigger selection not previously demonstrated at RHIC, and that is essential for QCD studies at future facilities such as the Electron-Ion Collider (EIC). The autonomous detector-control (GPU-based feedback) component named in the project title remained a design concept and was not implemented under this award. The Massachusetts Institute of Technology (MIT) group led the offline simulation and data processing needed to train the machine-learning (ML) models, the translation of trained models to Field-Programmable Gate Array (FPGA) firmware using the hls4ml framework, and the physics validation of heavy-flavor reconstruction. Over the award period, the team developed and hardware-tested the principal components of an AI-based heavy-flavor trigger on simulated and recorded sPHENIX tracker data: a software Bipartite Graph Attention Network (BiGAT) trigger model reaching > 95% signal efficiency at 99% background rejection; an FPGA-native hit clusterizer matching the offline clustering; smaller networks synthesized to FPGA within the required sub-10 µs latency; and an assembled decoder–clusterizer–inference firmware chain exercised on the FELIX readout board. A complete, fully integrated hardware demonstrator was not finished within the award period. This report documents the project goals, the MIT group’s contributions, the technical accomplishments, and the outlook toward applications at the future EIC ePIC detector.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Solid, structured composite neutron detectors with high dynamic range capability

Neutron detectors are essential across disciplines such as fundamental science, nuclear security, safeguards, and civilian applications. While 3 He-filled gas proportional counters have long been revered for their efficacy in detecting thermal neutrons and praised for their efficiency, neutron/gamma discrimination, and stability, the scarcity of 3He has spurred a search for alternatives. Here, we explore a solid structured scintillating particle composite (SPC) consisting of 6 Li-containing scintillating glass particles within an acrylic matrix as a neutron detector for high dynamic range applications. We show for the first time that an SPC neutron detector can boast an intrinsic detection efficiency of 0.261% for pure 252 Cf fission neutrons and an overall neutron detection efficiency of (0.546 ± 0.003)% at the Neutron Free-in-Air facility while being able to function in an intense gamma-ray environment. We also show that the SPC neutron detector supports fast neutron capture times and enables a dual-readout scheme that extends the detector dynamic range to high incident neutron fluxes. A scalable fabrication process allows for tailoring the SPC detector properties to the requirements of specific applications. Good agreement is found between the experimental results taken with a National Institute of Standards and Technology traceable 252 Cf source and the coupled MCNP6 and optical-ray-tracing simulations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Super-Resolution Ptychography with Small Segmented Detectors

To overcome the spatial resolution limit set by aperture-limited diffraction in traditional scanning transmission electron microscopy, microscopists have developed ptychography enabled by iterative phase retrieval algorithms and high-dynamic-range pixel array detectors. Current detector designs are limited by the data rate off chip, so a high-pixel-count detector has a proportionally lower frame rate than the few-segment detectors used for differential phase contrast (DPC) imaging. This slower acquisition speed leads to heightened vulnerability to scan noise, drift, and potential sample damage. This creates opportunities for repurposing fast segmented detectors for ptychography by trading a reduction in reciprocal space pixels for an increase in real space pixels. Here, we explore a strategy of oversampling in real space and instead apply detector pixel upsampling during the reconstruction process. Further, we demonstrate the viability of achieving super-resolution ptychography on thin objects using only 2 × 2 detector pixels, surpassing the resolution of integrated DPC (iDPC) imaging. With optimization using simulated datasets and experiments on MoTe 2 /WSe 2 bilayer moiré superlattices, we achieved super-resolution ptychography reconstructions under rapid acquisition conditions (37.5 pA, 1 μs dwell time), yielding over 50% improvements in contrast and information limit compared to annular dark field and iDPC imaging on the same detectors.

2D materials↗

DOE OSTI2025

A Fast Framework for Generating Radioactive Mixture Spectra and Its Application to Remote High-Performance Mixture Identification

Remote detection of radioactive materials in mixtures using handheld or portal detectors remains a challenge because of factors such as low concentration, environmental interference, sensor noise, and other complications. This work introduces a fast framework for generating realistic mixture spectra. Moreover, we present mixture isotope identification using data generated by the fast framework. Researchers have examined a range of conventional and recent algorithms within the fields of machine learning and deep learning. An application to uranium enrichment-level prediction has been included. Extensive simulation experiments validated the efficacy of the proposed framework.

GADRAS↗

NASA NTRS2015

The Parameterization of Top-Hat Particle Sensors with Microchannel-Plate-Based Detection Systems and its Application to the Fast Plasma Investigation on NASA's Magnetospheric MultiScale Mission

The most common instrument for low energy plasmas consists of a top-hat electrostatic analyzer geometry coupled with a microchannel-plate (MCP)-based detection system. While the electrostatic optics for such sensors are readily simulated and parameterized during the laboratory calibration process, the detection system is often less well characterized. Furthermore, due to finite resources, for large sensor suites such as the Fast Plasma Investigation (FPI) on NASA's Magnetospheric Multiscale (MMS) mission, calibration data are increasingly sparse. Measurements must be interpolated and extrapolated to understand instrument behavior for untestable operating modes and yet sensor inter-calibration is critical to mission success. To characterize instruments from a minimal set of parameters we have developed the first comprehensive mathematical description of both sensor electrostatic optics and particle detection systems. We include effects of MCP efficiency, gain, scattering, capacitive crosstalk, and charge cloud spreading at the detector output. Our parameterization enables the interpolation and extrapolation of instrument response to all relevant particle energies, detector high voltage settings, and polar angles from a small set of calibration data. We apply this model to the 32 sensor heads in the Dual Electron Sensor (DES) and 32 sensor heads in the Dual Ion Sensor (DIS) instruments on the 4 MMS observatories and use least squares fitting of calibration data to extract all key instrument parameters. Parameters that will evolve in flight, namely MCP gain, will be determined daily through application of this model to specifically tailored in-flight calibration activities, providing a robust characterization of sensor suite performance throughout mission lifetime. Beyond FPI, our model provides a valuable framework for the simulation and evaluation of future detection system designs and can be used to maximize instrument understanding with minimal calibration resources.

Instrument-model↗

NASA NTRS1981

Submillisecond measurements of system optical modulation functions in mosaic focal plane arrays

Measurements of system optical modulation functions (MTF, SWR) may be distorted by time-dependent environmental effects (thermal, vibration, flexure) and by electronics drift. Fast data collection may therefore be advantageous by minimizing drift time. The problem of fast data collection is accentuated when modulation data must be taken on a large number of detectors in a focal plane array. A method has been developed for the generation and storage of knife edge data from focal plane arrays, where data collection time per detector is in the submillisecond range. Once knife edge collects are completed, MTF response is found using conventional convolution techniques. SWR is obtained directly from knife edge response using a computerized simulation algorithm which bypasses use of MTF harmonics. Requirements for detector electronics speed, damping, and dynamic range are considered.

Thurlow, P. E.↗

NASA NTRS2012

Progress on the Development of a Simulation Environment for Optical Communications

Deep space laser communications require extremely accurate beam pointing to take advantage of the narrow beams achievable at optical wavelengths. This pointing accuracy must be achieved in the presence of spacecraft basebody motion which may exceed laser pointing requirements by orders of magnitude.In this paper a model of an optical band transceiver pointing control system is developed that can be used to predict performance under various operating scenarios. The transceiver model consists of an electro-mechanical model of the telescope platform and isolator. A novel photon counting detector array is used in the simulation as the focal plane detector. In the simulation we are able to inject various cases of spacecraft basebody motion based on both flight data and future mission spacecraft jitter requirements. Various models of uplink beacon flux levels and atmospheric scintillation are also available for analysis.Using these models, detector processing and control functions are implemented in the simulation. A complete acquisition sequence is demonstrated with blind acquisition and tracking of the modulated uplink beam and positioning of the downlink beam on the focal plane array. These simulations predict that pointing requirements will be met with representative disturbance models and uplink beam scintillation.

pointing control↗

DOE OSTI2026

Fermilab PIP II machine protection system digitized data noise elimination scheme and its FPGA implementation

In Fermilab's PIP-II machine protection system, beam loss signals from various detectors are digitized at 125 MS/s. Noise from both high-frequency sources and low-frequency 60 Hz AC power equipment can contaminate the data. To suppress noise across these ranges especially 60 Hz and its harmonics, which overlap with beam loss signal frequencies advanced digital processing beyond standard filtering is required. Several real-time functional blocks were simulated and tested on an FPGA: (1) a dual time-constant discharging integrator filter, (2) a de-ripple baseline extraction and storage block, and (3) a fast-recovery discharging integrator. The nonlinear IIR integrator filter removes high-frequency noise and feeds into the baseline extractor. Upon detecting abrupt beam loss, it switches to a longer time constant to prevent baseline distortion. The de-ripple block calculates a valid baseline by averaging over multiple 60 Hz periods, storing results in a 4096-word FPGA RAM. This baseline is subtracted from raw data before integration by the fast-recovery block, which resets quickly after use. All blocks achieved expected performance.

Wu, J. [Fermilab] (ORCID:0000000344329521)↗

DOE OSTI2026

Neural posterior unfolding

Differential cross section measurements are the currency of scientific exchange in particle and nuclear physics. A key challenge for these analyses is the correction for detector distortions, known as deconvolution or unfolding. Binned unfolding of cross section measurements traditionally rely on the regularized inversion of the response matrix that represents the detector response, mapping pre-detector (`particle level') observables to post-detector (`detector level') observables. In this paper we introduce Neural Posterior Unfolding, a modern, Bayesian approach that leverages normalizing flows for unfolding. By using normalizing flows for neural posterior estimation, NPU offers several key advantages including implicit regularization through the neural network architecture, fast amortized inference that eliminates the need for repeated retraining, and direct access to the full uncertainty in the unfolded result. In addition to introducing NPU, we implement a classical Bayesian unfolding method called Fully Bayesian Unfolding (FBU) in modern Python so it can also be studied. These tools are validated on simple Gaussian examples and then tested on simulated jet substructure examples from the Large Hadron Collider (LHC). We find that the Bayesian methods are effective and worth additional development to be analysis ready for cross section measurements at the LHC and beyond.

Analysis and statistical methods↗

DOE OSTI2026

Bayesian Exploration and Surrogate Emulation of Nonlinear Beam-Response Geometry in the LBNF Beamline

Next-generation long-baseline neutrino experiments aim to achieve multi-MW proton beam power while reducing accelerator-induced systematic uncertainties. At Fermilab, the LBNF beamline is designed for 1.2 MW operation with PIP-II and is upgradeable to 2.4 MW. DUNE will probe the three-flavor neutrino paradigm and search for CP violation, requiring precise neutrino-flux normalization and improved control of accelerator-related uncertainties. Within the LBNF beamline, the System for On-Axis Neutrino Detection (SAND) will constrain flux uncertainties using precision near-detector measurements, while the Muon Monitor System (MuMS) will provide beamline diagnostics sensitive to the proton beam, target, and horn configuration. However, the pion phase space relevant for DUNE depends simultaneously on many correlated parameters, including beam centroid, beam width, horn current and alignment, target position, optics shifts, and radiation-induced changes. Consequently, MuMS observables exhibit nonlinear and coupled responses that are difficult to characterize using traditional one-parameter scans. To address this challenge, we are developing a Bayesian Exploration framework coupled to physics-informed surrogate emulators trained on Geant4 beamline simulations. Gaussian-process emulators provide both fast predictions and uncertainty estimates, enabling adaptive selection of new simulation points in beam-parameter space. As an initial demonstration, we construct surrogate emulators for MuMS response observables using a verified simulation campaign spanning proton-beam steering conditions. The emulators reproduce the simulated dependence of MuMS centroid and gradient observables while providing predictive uncertainties, and serve as the foundation for future multidimensional exploration including beam width, horn current, and additional beamline parameters. This work establishes a framework for uncertainty-aware beam monitoring, adaptive simulation campaigns, and rapid beam-response inference for future DUNE operations.

Ganguly, Sudeshna [Fermilab] (ORCID:00000003163482↗

DOE OSTI2026

Absolute efficiency response of the γ-ray spectrometer GRETINA for high-energy γ rays up to 6 MeV

GRETINA (Gamma-Ray Energy Tracking In-beam Nuclear Array), a state-of-the-art γ-ray tracking spectrometer, has been instrumental in advancing nuclear structure studies, particularly in experiments using fast beams of rare isotopes where the γ rays emitted in flight are subject to large Doppler shifts. This work presents an experimental determination of the γ-ray detection efficiency of GRETINA at energies up to 6 MeV. The high-energy γ rays originated from states that were populated in nucleon-removal reactions from a projectile beam at an intermediate beam energy of about 85 MeV/nucleon. Utilizing γ-γ coincidences, efficiency values were extracted in-beam and translated into a source efficiency at rest employing GEANT4 Monte Carlo simulations. A systematic discrepancy in the efficiency of the forward-positioned detectors was identified which is attributed to specific dead-time effects caused by high-energy light-particle events prevalent at forward angles, generating signals in the Ge crystals that saturate the preamplifier’s first stage. Furthermore, this study provides refined efficiency benchmarks for in-beam experiments, improving the accuracy of quantitative γ-ray spectroscopy analyses with GRETINA and GRETA (Gamma Ray Energy Tracking Array) in the future.

GRETINA↗

DOE OSTI2026

Nuclear recoil detection with color centers in bulk lithium fluoride

We present initial results on the detection of nuclear recoils in lithium fluoride (LiF) through the fluorescence of color centers created by particle interactions in the crystal lattice. Using light-sheet fluorescence microscopy, we image nuclear recoil tracks from both fast and thermal neutron interactions deep within a cubic-centimeter-scale sample. Automated three-dimensional feature extraction based on machine-learning tools enables the identification and classification of individual events. We observe that the fluorescence response of LiF to gamma irradiation is strongly suppressed, by a factor of 30–50 compared to neutron exposure, demonstrating intrinsic insensitivity to electromagnetic backgrounds. The observed and simulated event characteristics are consistent, including their number, size, and topology. These results establish the feasibility of LiF as a scalable detection medium for rare nuclear-recoil events and constitute a first step toward 10–1000 g scale detectors with single-event sensitivity for applications in reactor-neutrino detection, neutron spectroscopy, and dark matter searches.

Aroujo, G R [University of Zurich]↗