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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Photocathode characterisation for robust PICOSEC Micromegas precise-timing detectors

The PICOSEC Micromegas detector is a precise-timing gaseous detector based on a Cherenkov radiator coupled with a semi-transparent photocathode and a Micromegas amplifying structure, targeting a time resolution of tens of picoseconds for minimum ionising particles. Initial single-pad prototypes have demonstrated a time resolution below σ = 25 ps, prompting ongoing developments to adapt the concept for High Energy Physics applications, where sub-nanosecond precision is essential for event separation, improved track reconstruction and particle identification. The achieved performance is being transferred to robust multi-channel detector modules suitable for large-area detection systems requiring excellent timing precision. To enhance the robustness and stability of the PICOSEC Micromegas detector, research on robust carbon-based photocathodes, including Diamond-Like Carbon (DLC) and Boron Carbide (B 4 C), is pursued. Results from prototypes equipped with DLC and B 4 C photocathodes exhibited a time resolution of σ ≈ 32 ps and σ ≈ 34.5 ps, respectively. Efforts dedicated to improve detector robustness and stability enhance the feasibility of the PICOSEC Micromegas concept for large experiments, ensuring sustained performance while maintaining excellent timing precision.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Evaluation of a deuterated detector for gamma-ray beam intensity measurements

Laser-Compton scattering sources of quasi-monochromatic γ-ray beams have been in operation for over 30 years, with new facilities being under construction or proposed. To sustain a high-quality experimental physics program, continuous monitoring of these beams is essential by measuring the spectral, temporal, and spatial characteristics. In this work, we introduce a new method for determining the γ-ray beam intensity by using photodisintegration of deuterons in an active target detector approach. Measurements were performed using deuterated liquid scintillator detectors at several incident gamma energies between 4 and 13 MeV at the High Intensity γ-ray Source, Durham, USA. The results demonstrate that an instrument based on a deuterated active detector can be used to monitor the intensity of the γ-ray beam with high efficiency and operational simplicity.

Compton scattering↗
DOE OSTI2025

Flavor Classification in ICARUS Using Convolutional Visual Networks

In this work, we adapt the Convolutional Visual Network (CVN) approach [1] to the ICARUS detector by incorporating TPC stitching methods inspired by NuGraph[8]. The stitching technique used here is unique to ICARUS, designed specifically to handle its distinct detector segmentations. We then retrain the network using ICARUS-specific data. This study underscores the flexibility of deep learning models in high-energy physics and the importance of accounting for detector-specific features when transferring machine learning techniques between experiments. This poster presents the methods, classification performance, and insights gained from applying CVN to ICARUS data.

Wieler, Felipe [Tech. Fed. Parana U.]↗
DOE OSTI2026

Neutrino Flavor Classification in ICARUS Experiment Using Convolutional Visual Networks

In this work, I adapt the Convolutional Visual Network (CVN) approach to the ICARUS detector by incorporating TPC stitching methods inspired by NuGraph. The stitching technique used here is unique to ICARUS, designed specifically to handle its distinct detector segmentation s. I then retrain the network using ICARUS-specific data. This study underscores the flexibility of deep learning models in high-energy physics and the importance of accounting for detector-specific features when transferring machine learning techniques between experiments. This dissertation presents the methods, classification performance, and insights gained from applying CVN to ICARUS data.

Wieler, Felipe Andre [Parana Tech. Fed. U., Toledo↗
DOE OSTI2025

Summer 2025 SULI: Nucleus ID, TinyTPC, and Scientific Communication

This paper summarizes my work during the Summer 2025 SULI internship, which focused on two main projects and broader scientific development. The first project involved improving the particle identification (PID) of protons, deuterons, and tritons using PIDA distributions and template fitting, with the goal of modeling nuclear final-state interactions (FSI) and testing the robustness of the method against systematic uncertainties. These techniques pave the way for future application to LArTPC data from the ICARUS detector. The second project centered on the optimization and data-taking of the TinyTPC detector, a compact LArTPC used for high-resolution low-energy measurements. I adjusted gain and threshold parameters, performed hardware validation tests, and developed analysis strategies to extract meaningful physics from collected data. Throughout the summer, I also enhanced my scientific communication and mentorship skills through presentations, collaborative analysis, and peer guidance.

McCright, Hannah [Maryland U.]↗
DOE OSTI2026

DC Cryogenic Modeling of Open-Source SkyWater 130 nm MOSFETs at 77 K Using BSIM4

Cryogenic applications in high-energy physics (HEP) demand reliable, low-power CMOS electronics capable of operating at liquid nitrogen temperatures (77 K). The open-source SkyWater 130nm (SKY130) CMOS process has previously been shown to operate at temperatures as low as 4 K making it a promising candidate for HEP applications. In this work, we characterize and model SKY130 low-threshold voltage transistors at 77 K, which is a temperature commonly used in modeling applications for liquid argon detectors. DC characteristic measurements were performed at both room temperature and liquid nitrogen temperature. We created a cryogenic modeling approach to produce a SPICE-compatible, isothermal BSIM4-based model for select transistor sizes at 77 K. The resulting model agrees with data at 77 K with an average error on the order of 20% (relative RMS) and shows no dependence on drain voltage. Due to the open-source nature of SKY130, we have made our models publicly available on Github. We hope this work will continue the trend for democratizing circuit design at cryogenic temperatures in high-energy physics by enabling open access to accurate cryogenic CMOS device models at 77 K.

Beall, F. [Texas U., Arlington]↗
DOE OSTI2025

Design and simulation of a SiPM-on-tile ZDC for the future EIC, and its performance with graph neural networks

We present a design for a high-granularity zero-degree calorimeter (ZDC) for the upcoming Electron-Ion Collider (EIC). The design uses SiPM-on-tile technology and features a novel staggered-layer arrangement that improves spatial resolution. To fully leverage the design’s high granularity and non-trivial geometry, we employ graph neural networks (GNNs) for energy and angle regression as well as signal classification. The GNN-boosted performance metrics meet, and in some cases, significantly surpass the requirements set in the report on science requirements and detector requirements for the EIC (Yellow Report), laying the groundwork for enhanced measurements that will facilitate a wide physics program. Our studies show that GNNs can significantly enhance the performance of high-granularity CALICE-style calorimeters by automating and optimizing the software compensation algorithms required for these systems. This improvement holds true even in the case of complicated geometries that pose challenges for image-based AI/ML methods.

Calorimeter↗
DOE OSTI2025

Quantum information meets high-energy physics: input to the update of the European strategy for particle physics

Some of the most astonishing and prominent properties of Quantum Mechanics, such as entanglement and Bell nonlocality, have only been studied extensively in dedicated low-energy laboratory setups. The feasibility of these studies in the high-energy regime explored by particle colliders was only recently shown and has gathered the attention of the scientific community. For the range of particles and fundamental interactions involved, particle colliders provide a novel environment where quantum information theory can be probed, with energies exceeding by about 12 orders of magnitude those employed in dedicated laboratory setups. Furthermore, collider detectors have inherent advantages in performing certain quantum information measurements and allow for the reconstruction of the state of the system under consideration via quantum state tomography. Here, we elaborate on the potential, challenges, and goals of this innovative and rapidly evolving line of research and discuss its expected impact on both quantum information theory and high-energy physics.

Physics↗
DOE OSTI2026

Forward modeling approach to nuclear reaction cross sections: Applications in neutron inelastic scattering

The development of nuclear reaction models for the production of evaluated nuclear data has traditionally been performed by comparing measured cross sections with predictions from reaction model codes whose physical input parameters are adjusted to obtain the best agreement between measured and modeled results. To more directly probe reaction model inputs, this work introduces a forward modeling approach to experimental reaction cross-section determination, where the most important physical input parameters to reaction model calculations are obtained via 𝜒 2 minimization between measured and calculated observables. This was demonstrated using data collected by the Gamma Energy Neutron Energy Spectrometer for Inelastic Scattering (GENESIS) at the 88-inch cyclotron at Lawrence Berkeley National Laboratory, a detection array consisting of organic liquid scintillators and high-purity germanium (HPGe) detectors. Using a broad-spectrum neutron beam and a 99.98%-enriched 56 Fe target, GENESIS was used to perform a simultaneous measurement of 56 Fe 𝛾-ray production cross sections and secondary neutron energy and angle distributions. The results of the forward modeling approach to the determination of energy-differential 𝛾-ray production cross sections for the yrast 4 + → 2 + and 6 + → 4 + transitions, as well as eight other off-yrast transitions, were compared against those obtained using conventional techniques, and the results are in good agreement. In addition to discrete 𝛾-ray yield total scattered neutron energy-angular distributions as a function of incident neutron energy were also obtained using forward modeling and found to agree with evaluated data, with the exception of elastic scattering at small angles. The fitted reaction model parameters obtained through forward modeling were also used to calculate the cross section for the unobserved (𝑛, 2⁢𝑛) reaction; excellent agreement with the current evaluation was obtained, providing a validation of the predictive capabilities of the forward model approach. This work bridges the gap between nuclear data experiment and evaluation by providing a new means for extracting inelastic neutron-scattering cross sections and neutron-induced 𝛾-ray production data while directly probing reaction model physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Graph neural network for neutrino physics event reconstruction

Liquid argon time projection chamber (LArTPC) detector technology offers a wealth of high-resolution information on particle interactions, and leveraging that information to its full potential requires sophisticated automated reconstruction techniques. Here, this article describes NUGRAPH 2, a graph neural network for low-level reconstruction of simulated neutrino interactions in a LArTPC detector. Simulated neutrino interactions in the MicroBooNE detector geometry are described as heterogeneous graphs, with energy depositions on each detector plane forming nodes on planar subgraphs. The network utilizes a multihead attention message-passing mechanism to perform background filtering and semantic labeling on these graph nodes, identifying those associated with the primary physics interaction with 98.0% efficiency and labeling them according to particle type with 94.9% efficiency. The network operates directly on detector observables across multiple two-dimensional representations but utilizes a three-dimensional-context-aware mechanism to encourage consistency between these representations. Model inference takes 0.12 s / event on a CPU and 0.005 s / event batched on a GPU. This architecture is designed to be a general-purpose solution for particle reconstruction in neutrino physics, with the potential for deployment across a broad range of detector technologies, and offers a core convolution engine that can be leveraged for a variety of tasks beyond the two described in this paper.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Artificial Intelligence for Event Reconstruction and Higgs Physics at CMS and Future Colliders

This dissertation charts a trajectory in which advances in artificial intelligence (AI) play a central role in pushing the high-energy physics frontier, complementing progress driven by higher collision energies and larger colliders. The discovery potential of the LHC and future colliders relies on accurate reconstruction of increasingly complex particle collision events. In the CMS experiment, this task is performed by the particle-flow (PF) algorithm. This dissertation presents the first implementation of a machine-learning-based particle-flow (MLPF) reconstruction in the CMS detector based on transformer architectures. In simulated top quark--antiquark pair (ttbar) events under LHC Run~3 (2023--2024) conditions, MLPF improves jet energy resolution by 10--20\% compared to standard PF for jets with transverse momentum between 30--100\GeV. Runtime performance is evaluated using simulated multijet events, with a median inference time of 20\unit{ms} per event on an NVIDIA L4 GPU, compa red to approximately 110\unit{ms} for standard PF. The MLPF algorithm is also validated on Run~3 collision data, representing the first data-validated ML-based reconstruction pipeline at any LHC experiment. We then extend MLPF toward future electron--positron colliders and introduce the first full-simulation cross-detector transfer learning workflow for PF reconstruction. The model is pre-trained on simulated events from the Compact Linear Collider detector (CLICdet) and fine-tuned on the CLIC-like detector (CLD) proposed for the Future Circular Collider (FCC). This approach achieves up to a 40\% improvement in jet energy resolution over rule-based reconstruction while reducing the required training dataset size by an order of magnitude, demonstrating the potential of AI to accelerate detector development and optimization. This dissertation also demonstrates how modern AI techniques enhance the sensitivity of LHC physics analyses. A CMS search for highly Lorentz-boosted Higgs bosons decaying to \textrm{W} boson pairs is presented, focusing on the single-lepton final state. A dedicated fine-tuning strategy for \ParT yields an approximately 70\% increase in expected sensitivity relative to the baseline model. The analysis uses proton--proton collision data at a center-of-mass energy of \ensuremath{\sqrt{s}=13\TeV} collected by CMS between 2016 and 2018, corresponding to an integrated luminosity of 138\ensuremath{\ \mathrm{fb}^{-1}}. The expected significance of the search is $1.86\sigma$, with an observed signal strength of $-0.19^{+0.48}_{-0.46}$. Finally, explainable AI techniques are applied to the MLPF and \ParticleNet algorithms using layerwise relevance propagation, showing that both models base their predictions on physically meaningful features consistent with our physics intuition. Together, these results demonstrate how advanced AI methods can enhance reconstruction, analysis sensitivity, and interpretability, shaping the next era of experimental parti cle physics.

Mokhtar, Farouk [UC, San Diego]↗
DOE OSTI2026

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↗
NASA NTRS2003

Beam Tests of the Balloon-Borne ATIC Experiment

The Advanced Thin Ionization Calorimeter (ATIC) balloon-borne experiment is designed to perform cosmic-ray elemental spectra measurement from 50 GeV to 100 TeV for nuclei from hydrogen to iron. These measurements are expected to provide crucial hints about some of the most fundamental questions in astroparticle physics today. ATTIC'S design centers on an 18 radiation length (X(sub Omnicron)) deep bismuth germanate (BGO) calorimeter, preceded by a 0.75 lambda(sub int) graphite target. In September 1999 the ATIC detector was exposed to high-energy beams at CERN's SPS accelerator, within the framework of the development program for the Advanced Cosmic-ray Composition Experiment for the Space Station (ACCESS). In December 2000 - January 2001, ATIC flew on the first of a series of long duration balloon (LDB) flights from McMurdo Station, Antarctica. We present here results from the 1999 beam-tests, including energy resolutions for electrons and protons at several beam energies from 100 GeV to 375 GeV, as well as signal linearity and collection efficiency estimates. We show how these results compare with expectations based on simulations, and their expected impacts on mission performance.

Ganel, O.↗
DOE OSTI2026

Reducing systematic bias in machine learning applications to J/ψ signal extraction in high-energy nuclear physics

Machine learning techniques are increasingly used in high-energy nuclear physics because they can exploit multivariate correlations more efficiently than conventional cut-based analyses. A central challenge is the construction of training samples that faithfully reproduce the detector response observed in data. Signal samples are usually derived from detector simulations; therefore, mismatches between simulation and data can degrade classifier performance and introduce systematic biases. This work presents two practical correction procedures, namely cumulative distribution function (CDF) mapping and a shift-and-scale transformation, to align simulated signal features with those measured in data. Their performance is demonstrated with $J$/$\psi$ yield measurements in $\sqrt{s_{nn}}$ = 200 GeV Ru+Ru and Zr+Zr collisions recorded by STAR. A set of self-consistency tests shows that these procedures substantially suppress the systematic bias associated with data-simulation discrepancies in machine-learning-based signal extraction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2015

Space Detectors for Gamma Rays (100 MeV-100 GeV): from Egret to Fermi LAT

The design of spaceborne high-energy (E is greater than 100 MeV) gamma-ray detectors depends on two principal factors: (1) the basic physics of detecting and measuring the properties of the gamma rays; and (2) the constraints of operating such a detector in space for an extended period. Improvements in technology have enabled major advances in detector performance, as illustrated by two successful instruments, EGRET on the Compton Gamma Ray Observatory and LAT on the Fermi Gamma-ray Space Telescope.

Gamma rays Detectors Space↗
NASA NTRS1984

Spartan - Space at low-cost

Mission profiles and objectives of the Spartan Shuttle payload are described. Spartan was designed to provide extended observations normally carried out for only a few minutes using sounding rockets. The payload will be placed in co-orbit with the Orbiter by the RMS arm and controlled by a ground-based researcher or by an on-board program. Spartan features minimal battery power, batteries, and control systems and no redundancy. Thermal control is added because the flights last 100-1000 times as long as those on sounding rockets. Data are recorded by an on-board tape recorder and operations are controlled by an on-board microprocessor. Spartan 1 will carry a star-pointer and X-ray detectors for high energy astrophysics experiments. Spartan 2 will gather solar physics data in the form of temperatures of hydrogen gas and ions and electrons. Particle densities will also be monitored and a 43 cm solar telescope will produce UV and white light solar coronagraphs. UV spectroscopy of stars, clouds and galaxies will be performed by the Spartan 3 payload.

Source record↗
DOE OSTI2026

Identification of Final-State Neutrons in ANNIE

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton gadolinium-loaded (Gd-loaded) water Cherenkov detector located on the Booster Neutrino beamline at Fermilab. ANNIE's primary physics objectives include measuring neutron multiplicity for neutrino-nucleus interactions and performing cross-section measurements of charge current quasi-elastic and neutral current quasi-elastic processes. These measurements aim to improve neutrino energy reconstruction and reduce uncertainties in current and future neutrino oscillation experiments. Additionally, ANNIE serves as a testbed for advanced technologies such as Large Area Picosecond Photodetectors (LAPPDs) and Water-Based Liquid Scintillator, which enhance vertex resolution and enable detection below the Cherenkov threshold. By leveraging the high neutron capture cross section of Gd-loaded water, ANNIE is well positioned to observe final-state neutrons in beam-correlated neutrino interactions. To constrain the uncertainties in the neutron capture efficiency and time within the detector, we have conducted multiple calibration campaigns using an AmBe source. The AmBe source is deployed at different locations of the tank to map the neutron capture efficiency and determine the expected neutron capture time. Additionally, the AmBe calibration data are used to define the precise neutron clustering that will be useful for identifying neutron-like clusters from neutrino interaction. In this poster, we will present the current status of the AmBe neutron calibration program and the development of neutron cluster definition for the ANNIE Experiment.

Ajana, Dhavalkumar [Florida State U.]↗
DOE OSTI2024

Characterizing novel Indium Phosphide pad detectors with focused X-ray beams and laboratory tests

Future tracking systems in High Energy Physics experiments will require large instrumented areas with low radiation length. Crystalline silicon sensors have been used in tracking systems for decades, but are difficult to manufacture and costly to produce for large areas. We are exploring alternative sensor materials that are amenable to fast fabrication techniques used for thin film devices. Indium Phosphide pad sensors were fabricated at Argonne National Lab using commercially available InP:Fe 2-inch mono-crystal substrates. Current-voltage and capacitance-voltage characterizations were performed to study the basic operating characteristics of a group of sensors. Micro-focused X-ray beams at Canadian Light Source and Diamond Light Source were used to study the response to ionizing radiation, and characterize the uniformity of the response for several devices. Electrical test results showed a high degree of performance uniformity between the 48 tested devices. X-ray test beam results showed good performance uniformity within tested devices after accounting for spatially-local defects and edge fields. As a result, this motivates further studies into thin film devices for future tracking detectors.

47 OTHER INSTRUMENTATION↗