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DOE OSTI2025

Track Reconstruction using Graph Neural Networks in the EMPHATIC Experiment

Track reconstruction is essential for extracting physics observables from detector data in high-energy and nuclear physics experiments. In this work, we investigate the use of graph neural networks (GNNs) to reconstruct particle momentum in the EMPHATIC experiment using simulated data. The model takes raw hit information from the silicon strip detectors (SSDs) as input and is trained to predict momentum components and the scattering angle of the particle. We describe the GNN architecture, training procedure, and performance metrics, and present results showing improved resolution in momentum reconstruction. These results demonstrate the potential of GNN-based approaches in track reconstruction tasks within complex detector environments like EMPHATIC.

Bhattarai, Aayush [Notre Dame U.]↗

DOE OSTI2020

Temperature statistics in a radiatively heated particle-laden turbulent square duct flow

Radiation absorption by preferentially concentrated particles in a turbulent square duct flow is studied experimentally. The particle-laden flow is exposed to near-infrared radiation, and the gas phase temperature statistics are measured along the wall bisector of the duct. It is found that the instantaneous temperature fluctuations are comparable to the overall mean temperature rise. The temperature statistics at the duct centerline and near the wall are qualitatively different. The former reflects preferential concentration in isotropic flows while the latter displays evidence of particle clustering into streamwise elongated streaks. Comparison of the experimental data to a simplified heat transfer model suggests that the Lagrangian evolution of particle clusters and voids, and turbulent mixing in the vicinity of particle clusters, are important. This work was motivated by particle solar receiver technology, but the findings are also relevant to systems where there is localized heat release or mass transfer from disperse particles or droplets. It shows that obtaining Lagrangian histories of particle trajectories is an important next step towards understanding thermal transport phenomena in particle-laden turbulent flows.

42 ENGINEERING↗

DOE OSTI2021

Prediction of Leakage Current and Depletion Voltage in Silicon Detectors Under Extraterrestrial Radiation Conditions

Silicon detection is a mature technology for registering the passage of charged particles. At the same time it continues to evolve toward increasing radiation tolerance as well as precision and adaptability. For these reasons it is likely to remain a critical element of detection of systems associated with extraterrestrial exploration. Silicon sensor leakage current and depletion voltage depend on the integrated fluence received by the sensor and on its thermal history during and after the irradiation process. For minimal assumptions on shielding and hence on the particle energy spectrum, and using published data on Martian ground temperature, we predict the leakage current density and the depletion voltage, as a function of time, of silicon sensors in transit to and deployed continuously on the Mars surface for a duration of up to 28 Earth-years, for several sensor geometries and a worst-case temperature scenario.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Numerical investigation of laser-driven shock interaction with a deformable particle

A laser-driven shock propagating through an isolated particle embedded in a plastic (CH) target was studied using the radiation-hydrodynamic code FLASH. Preliminary simulations using IONMIX equations of state (EOS) showed significant differences in the shock Hugoniot of aluminum compared to experimental data in the low-pressure regime [O(10) GPa], resulting in higher streamwise compression and deformation of an aluminum particle. Hence, a simple modification to the ideal gas EOS was developed and employed to describe the target materials and examine the particle dynamics. The evolution of the pressure field demonstrated a complex wave interaction, resulting in a highly unsteady particle drag which featured two drag minima due to shock focusing at the rear end of the particle and rarefaction stretching due to laser shut-off. Although ~30% lateral expansion and ~25% streamwise compression were observed, the aluminum particle maintained considerable integrity without significant distortion. Additional simulations examined the particle response for a range of particle densities, sizes, and acoustic impedances. The data revealed that lighter particles such as aluminum gained significant momentum, reaching up to ~96% of the shocked CH's speed, compared to ~29% for the heavier tungsten particles. Despite the differences seen in the early stage of shock interaction, particles with varying acoustic impedances ultimately reached the same peak velocity. This identified particle-to-host density ratio is an important factor in determining the inviscid terminal velocity of the particle. In addition, the modified EOS model presented in this study could be used to approximate solid materials in hydrocodes that lack material strength models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Discrimination between individual dust and bioparticles using aerosol time-of-flight mass spectrometry

Ice nucleating particles (INPs) impact cloud properties and precipitation processes through their ability to trigger cloud glaciation. Dust and bioparticles are two important sources of INPs that have markedly different atmospheric loadings and ice nucleating efficiencies. In situ identification of the sources of INPs in clouds has been accomplished using single particle mass spectrometry (SPMS). However, external mixtures of dust and bioparticles present a unique challenge as they have overlapping mass spectral ion signatures, complicating their unambiguous identification. This study presents a detailed discussion of dust and bioparticle SPMS signatures, uniting data from a broad array of studies. As emphasized, the ion signals from both dust and bioparticles are highly sensitive to ionization conditions. To understand the observed variations, we characterize the mass spectral dependence of distinct dust and bioparticle samples using total positive ion intensity (TPII) as an indicator of the laser pulse energy each particle encountered. Through this analysis, a broad range of characteristic biogenic low intensity ion peaks that may be useful to distinguish bioparticles from dust became apparent and are highlighted. Insights informed by this analysis were utilized to identify bioparticles in ambient SPMS data. Ambient particles exhibiting both dust and characteristic biogenic spectral fingerprints were excluded from the bioparticle classification. Although bioparticles only made up 0.2% of all sampled particles, their abundance was moderately correlated with INP concentrations measured at 15 °C.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics

Large language models have revolutionized artificial intelligence by enabling large, generalizable models trained through self-supervision. This paradigm has inspired the development of scientific foundation models (FMs). However, applying this capability to experimental particle physics is challenging due to the sparse, spatially distributed nature of detector data, which differs dramatically from natural language. This work addresses if an FM for particle physics can scale and generalize across diverse tasks. We introduce a new dataset with more than 11 million particle collision events and a suite of downstream tasks and labeled data for evaluation. We propose a novel self-supervised training method for detector data and demonstrate its neural scalability with models that feature up to 188 million parameters. With frozen weights and task-specific adapters, this FM consistently outperforms baseline models across all downstream tasks. The performance also exhibits robust data-efficient adaptation. Further analysis reveals that the representations extracted by the FM are task-agnostic but can be specialized via a single linear mapping for different downstream tasks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Non-Gaussianities in collider energy flux

The microscopic dynamics of particle collisions is imprinted into the statistical properties of asymptotic energy flux, much like the dynamics of inflation is imprinted into the cosmic microwave background. This energy flux is characterized by correlation functions < E(n → 1 )…E(n → k ) > of energy flow operators E(n → ). There has been significant recent progress in studying energy flux, including the calculation of multi-point correlation functions and their direct measurement inside high-energy jets at the Large Hadron Collider (LHC). In this paper, we build on these advances by defining a notion of “celestial non-gaussianity” as a ratio of the three-point function to a product of two-point functions. We show that this celestial non-gaussianity is under perturbative control within jets at the LHC, allowing us to cleanly access the non-gaussian interactions of quarks and gluons. We find good agreement between perturbative calculations of the non-gaussianity and a charged-particle-based analysis using CMS Open Data, and we observe a strong non-gaussianity peaked in the “flattened triangle” regime. The ability to robustly study three-point correlations is a significant step in advancing our understanding of jet substructure at the LHC. We anticipate that the celestial non-gaussianity, and its generalizations, will play an important role in the development of higher-order parton showers simulations and in the hunt for ever more subtle signals of potential new physics within jets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Sediment Mineralogy Data Review for the Hanford Central Plateau

The compendium of Hanford site sediment mineralogy data provided in this report was initiated in 2012 to support flux mitigation technologies and real-time monitoring that could limit contaminant fluxes to the groundwater in the Central Plateau. Recent efforts in advancing spectral induced polarization as a method for monitoring contaminant transformations for active biogeochemical remedies have underscored the need for a published compendium of sediment characterization data. Although the data are primarily focused on the mineralogical composition of Hanford sediments, other soil measurements such as particle size distribution such as particle size distribution and carbon content are provided. The data have been reproduced from the original reports and are provided here for information only (FIO).

58 GEOSCIENCES↗

DOE OSTI2023

TRACER-CAT-UCDAVIS Aerosol Optical Properties and Sizing - smps

Aerosol size distribution and optical properties collected in La Port TX, during TRACER-CAT-UCDavis, July 2022. Speciated particle composition measurements are reported separately. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by UC Davis (TRACER-CAT-UCDavis, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-UCDavis is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments include: UC Davis two-wavelength cavity ringdown-photoacoustic spectrometer (CRD-PAS); Brechtel scanning electrical mobility sizer (SEMS); modified humidified cavity attenuated phase shift spectrometer with single scatter albedo (H-CAPS-SSA)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

TRACER-CAT-UCDAVIS Aerosol Optical Properties and Sizing - crdps

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Continual Learning for Production-Level Machine Learning in Particle Accelerators

Particle accelerators operate in complex environments where data distribution can change dynamically, leading to data drifts that significantly challenge Machine Learning (ML) models. These non-stationary conditions often cause ML models to deteriorate in performance, making it difficult to maintain reliable predictions in operation. The primary sources of data drifts are changes in accelerator settings and changes in equipment performance which cannot be measured directly. To bridge this gap between ML development and long-term deployment in operational settings, we identify key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. We will provide a practical guide on selecting the appropriate method given resource constraints and desired stability plasticity trade offs. As a concrete example, we will present a real-world use case for anomaly detection to predict errant beams at the Spallation Neutron Source accelerator, where continual learning has been employed to demonstrate stable performance on drifting data streams. We will present practical challenges, lessons learned, and the results from the deployed ML model.

Rajput, Kishansingh [Thomas Jefferson National Acc↗

DOE OSTI2020

Pass-efficient methods for compression of high-dimensional turbulent flow data

The future of high-performance computing, specifically on future Exascale computers, will presumably see memory capacity and bandwidth fail to keep pace with data generated, for instance, from massively parallel partial differential equation (PDE) systems. Current strategies proposed to address this bottleneck entail the omission of large fractions of data, as well as the incorporation of in situ compression algorithms to avoid overuse of memory. To ensure that post-processing operations are successful, this must be done in a way that a sufficiently accurate representation of the solution is stored. Moreover, in situations where the input/output system becomes a bottleneck in analysis, visualization, etc., or the execution of the PDE solver is expensive, the number of passes made over the data must be minimized. In the interest of addressing this problem, this work focuses on the utility of pass-efficient, parallelizable, low-rank, matrix decomposition methods in compressing high-dimensional simulation data from turbulent flows. Additionally, a particular emphasis is placed on using coarse representation of the data – compatible with the PDE discretization grid – to accelerate the construction of the low-rank factorization. This includes the presentation of a novel single-pass matrix decomposition algorithm for computing the so-called interpolative decomposition. The methods are described extensively and numerical experiments on two turbulent channel flow data are performed. In the first (unladen) channel flow case, compression factors exceeding 400 are achieved while maintaining accuracy with respect to first- and second-order flow statistics. In the particle-laden case, compression factors of 100 are achieved and the compressed data is used to recover particle velocities. These results show that these compression methods can enable efficient computation of various quantities of interest in both the carrier and disperse phases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Nuclear Data Sheets for A=233

The present evaluation of the known A=233 nuclides ( 233 Fr, 233 Ra, 233 Ac, 233 Th, 233 Pa, 233 U, 233 Np, 233 Pu, 233 Am, 233 Cm) and 233 Bk represents an update and revision, after 15 years of the previous evaluation of A=233 published by B. Singh and J.K. Tuli (2005Si15). Laboratory identification of 233 Bk nuclide is still considered as tentative. Various decay and reaction data are evaluated and compared. Adopted data, levels, gammas, level half-lives, spins, parities, and configuration assignments are provided. For 233 Fr, 233 Ra, 233 Ac, 233 Pu, 233 Am and 233 Cm, information is available for the respective ground states only. The decay schemes from β - decays of 233 Fr and 233 Ra, and from ε decay of 233 Am and 233 Cm are unknown, while those from β - decay of 233 Ac, and from ε decay of 233 Np are considered incomplete. The α decay of 237 Np to 233 Pa has been studied in detail by many investigators, yet the decay scheme is considered incomplete as several very low-energy transitions are involved, without the knowledge of their intensities, resulting in mismatch of directly measured α feedings and those deduced from γ-transition intensity balances. The decay schemes from α decays of 237 Am, 237 Cm and 237 Cf to respective A=233 daughters are considered incomplete. Particle-transfer and high-spin data are available for 233 Th, 233 Pa and 233 U nuclei, which remain the most extensively studied in this mass chain. However, data for half-lives for the excited states are available only for one level in 233 Pa, and for five levels in 233 U, thus limiting the knowledge of reduced transition probabilities in this mass chain.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber

The liquid argon time projection chamber (LArTPC) detector technology has an excellent capability to measure properties of low-energy neutrinos produced by the sun and supernovae and to look for exotic physics at very low energies. In order to achieve those physics goals, it is crucial to identify and reconstruct signals in the waveforms recorded on each TPC wire. In this work, we report on a novel algorithm based on a one-dimensional convolutional neural network (CNN) to look for the region-of-interest (ROI) in raw waveforms. We test this algorithm using data from the ArgoNeuT experiment in conjunction with an improved noise mitigation procedure and a more realistic data-driven noise model for simulated events. This deep-learning ROI finder shows promising performance in extracting small signals and gives an efficiency approximately twice that of the traditional algorithm in the low energy region of ~0.03–0.1 MeV. This method offers great potential to explore low-energy physics using LArTPCs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Knowing our Sun: data fusion for optimizing space weather forecasts

Outline: Space weather: model for predicting solar wind & polarity; Particle filtering: optimization with Monte Carlo; Simulation & observation: twin tests and real data; Back to Earth: independent work in inertial confinement fusion (ICF)

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Particle size influences decay rates of environmental DNA in aquatic systems

Abstract Environmental DNA (eDNA) analysis is a powerful tool for remote detection of target organisms. However, obtaining quantitative and longitudinal information from eDNA data is challenging, requiring a deep understanding of eDNA ecology. Notably, if the various size components of eDNA decay at different rates, and we can separate them within a sample, their changing proportions could be used to obtain longitudinal dynamics information on targets. To test this possibility, we conducted an aquatic mesocosm experiment in which we separated fish‐derived eDNA components using sequential filtration to evaluate the decay rate and changing proportion of various eDNA particle sizes over time. We then fit four alternative mathematical decay models to the data, building towards a predictive framework to interpret eDNA data from various particle sizes. We found that medium‐sized particles (1–10 μm) decayed more slowly than other size classes (i.e., <1 and > 10 μm), and thus made up an increasing proportion of eDNA particles over time. We also observed distinct eDNA particle size distribution (PSD) between our Common carp and Rainbow trout samples, suggesting that target‐specific assays are required to determine starting eDNA PSDs. Additionally, we found evidence that different sizes of eDNA particles do not decay independently, with particle size conversion replenishing smaller particles over time. Nonetheless, a parsimonious mathematical model where particle sizes decay independently best explained the data. Given these results, we suggest a framework to discern target distance and abundance with eDNA data by applying sequential filtration, which theoretically has both metabarcoding and single‐target applications.

Brandão‐Dias, Pedro F. P.↗

DOE OSTI2024

Tethered balloon measurements reveal enhanced aerosol occurrence aloft interacting with Arctic low-level clouds

Low-level clouds in the Arctic affect the surface energy budget and vertical transport of heat and moisture. The limited availability of cloud-droplet-forming aerosol particles strongly impacts cloud properties and lifetime. Vertical particle distributions are required to study aerosol–cloud interaction over sea ice comprehensively. This article presents vertically resolved measurements of aerosol particle number concentrations and sizes using tethered balloons. The data were collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition in the summer of 2020. Thirty-four profiles of aerosol particle number concentration were observed in 2 particle size ranges: 12–150 nm (N 12−150 ) and above 150 nm (N >150 ). Concurrent balloon-borne meteorological measurements provided context for the continuous profiles through the cloudy atmospheric boundary layer. Radiosoundings, cloud remote sensing data, and 5-day back trajectories supplemented the analysis. The majority of aerosol profiles showed more particles above the lowest temperature inversion, on average, double the number concentration compared to below. Increased N 12−150 up to 3,000 cm −3 were observed in the free troposphere above low-level clouds related to secondary particle formation. Long-range transport of pollution increased N >150 to 310 cm −3 in a warm, moist air mass. Droplet activation inside clouds caused reductions of N >150 by up to 100%, while the decrease in N 12−150 was less than 50%. When low-level clouds were thermodynamically coupled with the surface, profiles showed 5 times higher values of N 12−150 in the free troposphere than below the cloud-capping temperature inversion. Enhanced N 12−150 and N >150 interacting with clouds were advected above the lowest inversion from beyond the sea ice edge when clouds were decoupled from the surface. Vertically discontinuous aerosol profiles below decoupled clouds suggest that particles emitted at the surface are not transported to clouds in these conditions. It is concluded that the cloud-surface coupling state and free tropospheric particle abundance are crucial when assessing the aerosol budget for Arctic low-level clouds over sea ice.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Green Bank and Effelsberg Radio Telescope Searches for Axion Dark Matter Conversion in Neutron Star Magnetospheres

Axion dark matter (DM) may convert to radio-frequency electromagnetic radiation in the strong magnetic fields around neutron stars. The radio signature of such a process would be an ultranarrow spectral peak at a frequency determined by the mass of the axion particle. In this work, we analyze data we collected from the Robert C. Byrd Green Bank Telescope in the L band and the Effelsberg 100-m Telescope in the $\textit{L}$ band and $\textit{S}$ band from a number of sources expected to produce bright signals of axion-photon conversion, including the Galactic center of the Milky Way and the nearby isolated neutron stars RX J0720.4-3125 and RX J0806.4-4123. We find no evidence for axion DM and are able to set constraints on the existence of axion DM in the highly motivated mass range between ~ 5 and 11 μ eV with the strongest constraints to date on axions in the ~10–11 μ eV range.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗