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DOE OSTI2025

Monte Carlo toolkit for designing and validating step-range-filter spectrometer designs

Here, we present a Monte Carlo toolkit for validating step range filter (SRF) spectrometer designs. Geant4 is used to transport charged particles through the SRF filters to generate synthetic SRF data that include realistic CR-39 effects. Synthetic SRF spectra generated by this method inherently account for instrument response and allow for the quantification of SRF performance before shots. The usefulness of this toolkit is demonstrated through its application to a number of problems. A new broadband SRF for the ∼10 MeV wide 3He3He proton spectrum is validated, and an analysis method for analyzing 3He3He-p SRF data that accounts for instrument response is put forth. In addition, an SRF design for the compact recoil-proton spectrometer (CRS) on the Z-machine is validated. Finally, a new calibration technique for the DD-p SRF is proposed and validated.

Johnson, T. M. (ORCID:0000000193032949)↗

DOE OSTI2022

Nuclear DFT analysis of electromagnetic moments in odd near doubly magic nuclei

We use the nuclear density functional theory to determine nuclear electric quadrupole and magnetic dipole moments in all one-particle and one-hole neighbours of eight doubly magic nuclei. We align angular momenta along the intrinsic axial-symmetry axis with broken time-reversal symmetry, which allows us to explore fully the self-consistent charge, spin, and current polarisation. Spectroscopic moments are determined for symmetry-restored wave functions and compared with available experimental data. We find that the obtained polarisations do not call for using quadrupole- or dipole-moment operators with effective charges or effective g-factors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Flow-dependent tagging of 214 Pb decays in the LZ dark matter detector

The LUX-ZEPLIN (LZ) experiment is searching for dark matter interactions in a liquid xenon time projection chamber (LXe-TPC). This article demonstrates how control of the flow state in the LXe-TPC enables the identification of pairs of sequential alpha-decays, which are used to map fluid flow and ion drift in the liquid target. The resulting transport model is used to tag 214 Pb~ beta-decays, a leading background to dark matter signals in LZ. Temporally evolving volume selections, at a cost of 9.0% of exposure, target the decay of each 214 Pb~ atom up to 81 minutes after production, resulting in (63±6 (s⁢t⁢a⁢t) ±7 (s⁢y⁢s) )% identification of 214 Pb~decays to ground state. We also demonstrate how flow-based tagging techniques enable a novel calibration side band that is concurrent with science data. Lastly we report updated estimates of radon-chain charge branching fractions in liquid xenon, finding branching to 218 Po + at 0.49±0.01, 214 Pb + at 0.48±0.12, and 214 Bi + at 0.74±0.05, with a mean charged ion lifetime in the LZ TPC of 49±4 min.

Lorenzon, Wolfgang [University of Michigan, Ann Ar↗

DOE OSTI2022

Spin dynamics and exchange interaction in orthoferrite TbFeO 3 with non-Kramers rare-earth ion

The low-temperature spin dynamics of the orthorhombic TbFeO 3 perovskite has been studied. It has been found that the inelastic neutron scattering (INS) spectrum of the investigated compound contains two modes corresponding to different sublattices. It is shown that the iron subsystem orders antiferromagnetically at T N =632 K and exhibits the high-energy magnon dispersion. The magnetic dynamics of this subsystem has been described within the linear spin wave theory and the in-plane and out-of-plane exchange anisotropy has been demonstrated. The approach proposed previously to describe the magnon dispersion in the TmFeO 3 compound has been used. Three levels of the nondispersive crystal electric field corresponding to Tb 3+ ions have been found in the energy region below 40 meV at about 17, 26, and 35 meV. The behavior of the magnetic correlation length of the terbium subsystem has been determined by studying the diffuse scattering at different temperatures. The evolution of this subsystem has been numerically described within the point charge model. In conclusion, it is shown that the numerical data agree satisfactorily with the experiment and with the general concept of the single-ion approximation applied to the rare-earth subsystem of orthorhombic perovskites.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Measurement of the D * longitudinal polarization in B 0 → D * − τ + ν τ decays

The longitudinal polarization fraction of the D * meson is measured in B 0 → D * − τ + ν τ decays, where the τ lepton decays to three charged pions and a neutrino, using proton-proton collision data collected by the LHCb experiment at center-of-mass energies of 7, 8 and 13 TeV and corresponding to an integrated luminosity of 5 fb − 1 . The D * polarization fraction F L D * is measured in two q 2 regions, below and above 7 GeV 2 / c 4 , where q 2 is defined as the squared invariant mass of the τ ν τ system. The F L D * values are measured to be 0.52 ± 0.07 ± 0.04 and 0.34 ± 0.08 ± 0.02 for the lower and higher q 2 regions, respectively. The first uncertainties are statistical and the second systematic. The average value over the whole q 2 range is F L D * = 0.41 ± 0.06 ± 0.03 . These results are compatible with the Standard Model predictions. © 2024 CERN, for the LHCb Collaboration 2024 CERN

Aaij, R. (ORCID:0000000305331952)↗

DOE OSTI2025

Quadrilateral particle arrangement within shocks in a two-dimensional dusty plasma

The microscopic structure within a two-dimensional shock was studied using data from a dusty plasma experiment. A single layer of charged microparticles, levitated in a glow-discharge plasma, was perturbed by an electrically floating wire that was moved at a steady supersonic speed to excite a compressional shock. A rearrangement of particles was observed, from a hexagonal lattice in the preshock into a quadrilateral microstructure on the front side of the shock. This quadrilateral structure would not be stable in a monolayer of identical repulsive particles, under equilibrium conditions. Glaser-Clark polygon analysis of the microstructure helped in identifying quadrilaterals. Voronoi analysis was used to characterize the defect fraction behind the shock, as an indication of shock-induced melting. Published by the American Physical Society 2025

Kananovich, Anton (ORCID:0000000225374952)↗

DOE OSTI2020

Search for a Narrow Resonance Lighter than 200 GeV Decaying to a Pair of Muons in Proton-Proton Collisions at $\sqrt{s} =$ TeV

A search is presented for a narrow resonance decaying to a pair of oppositely charged muons using s=13 TeV proton-proton collision data recorded at the LHC. In the 45–75 and 110–200 GeV resonance mass ranges, the search is based on conventional triggering and event reconstruction techniques. In the 11.5–45 GeV mass range, the search uses data collected with dimuon triggers with low transverse momentum thresholds, recorded at high rate by storing a reduced amount of trigger-level information. The data correspond to integrated luminosities of 137 and 96.6 fb-1 for conventional and high-rate triggering, respectively. No significant resonant peaks are observed in the probed mass ranges. The search sets the most stringent constraints to date on a dark photon in the ∼30–75 and 110–200 GeV mass ranges.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Collectivity in Ultraperipheral Pb+Pb Collisions at the Large Hadron Collider

In this work, we present the first full (3+1)D dynamical simulations of ultraperipheral Pb+Pb collisions at the Large Hadron Collider. Extrapolating from p + Pb collisions, we explore whether a quasireal photon γ* interacting with the lead nucleus in an ultraperipheral collision can create a many-body system exhibiting fluid behavior. Assuming strong final-state interactions, we provide model results for charged hadron multiplicity, identified particle mean transverse momenta, and charged hadron anisotropic flow coefficients, and compare them with experimental data from the ALICE and ATLAS Collaborations. The elliptic flow hierarchy between p + Pb and γ*+Pb collisions is dominated by the difference in longitudinal flow decorrelations and reproduces the experimental data well. We have demonstrated that our theoretical framework provides a quantitative tool to study particle production and collectivity for all system sizes, ranging from central heavy-ion collisions to small asymmetric collision systems at the Relativistic Heavy-Ion Collider and the Large Hadron Collider and even at the future Electron-Ion Collider.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

National Electric Vehicle Infrastructure Formula Program (NEVI) Brief for State Public Utility Commissions

The National Electric Vehicle Infrastructure (NEVI) Formula Program (NEVI program) is a funding opportunity for all 50 U.S. states, the District of Columbia, and Puerto Rico, established by the Bipartisan Infrastructure Law (BIL). The NEVI program instructs states to “strategically deploy electric vehicle (EV) charging infrastructure and to establish an interconnected network to facilitate data collection, access, and reliability.” The NEVI program allocates more than $\$$5 billion to states from Fiscal Year (FY) 2022 to 2026 on a formula basis. NEVI funds are available to private entities, including utilities, to build EV Supply Equipment (EVSE)1 and associated EV grid infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Using AI to Reproduce Neutrino Cross Section Analysis - Prototyping the Neutrino Discovery Platform

The Neutrino Discovery Platform (NDP) aims to accelerate DUNE-era science by making the neutrino program's existing datasets analyzable through fast, reproducible, and auditable workflows. We report a working version of two of its layers, data curation and agentic orchestration, built and tested end to end on MINERvA open data. The guiding lesson throughout is that a cross section is a measurement, and not just a plotted shape, only if it carries a defensible systematic-uncertainty budget, a trustworthy unfolding, and a reproducible record. Using a single medium-energy playlist pair from the MINERvA open-data release (about $2.05\times10^{17}$ protons on target of data), we first reproduced the shapes of two published charged-current inclusive $\nu_\mu$ measurements through a complete extraction ladder: selection, background subtraction, D'Agostini unfolding, efficiency correction, and flux normalization. These shape-level reproductions ran and tracked the published results, but they lacked the systematic-uncertainty machinery that defines a MINERvA cross section. To supply it, we vendored and built the MINERvA Analysis Toolkit and developed a many-universe systematic-uncertainty tool that produces a portable covariance artifact, a parallel event-loop runner, and a per-run auditability harness. Validated against a published covariance release, the toolchain reproduces the released statistical, flux, and muon-energy-scale terms and shows that they account for roughly 63\% of the total variance, with the remainder unreleased. Using this same infrastructure, we then performed a measurement of our own design, the hadronic recoil-energy distribution of low-energy ($E_\nu<2.5$~GeV) charged-current inclusive events, and found data/simulation shape agreement of $\chi^2/\mathrm{ndf}=1.26$. Together these results show that the platform supports original physics and not only reproductions.

Breaux, Auto [Tulane U. (main)]↗

DOE OSTI2023

Modeling and simulation to investigate the electrification potential of medium- and heavy-duty vehicle fleets

This project involves developing and integrating new modeling tools to simulate the dynamics of electric medium- and heavy-duty fleet vehicle adoption. A technical and economic modeling tool, combining a data-driven hardware cost model with a cost-optimal charging strategy microsimulation, enables tailored analysis of the costs and benefits of electrifying individual fleets. Next, a novel text synthesis process, applied to a curated corpus of literature, quantifies trade-offs between technical, economic, and other factors in the fleet vehicle procurement decision. The outcomes of these tasks combine with knowledge from recent literature on fleet decision processes to specify the vehicle procurement model used by fleets in an agent-based model of the medium- and heavy-duty electric vehicle market. This model embodies an especially disaggregated approach to adoption modeling, internalizing factors and dynamics that conventional adoption models externalize. In particular, explicitly modeling the formation and diffusion of opinions among agents enables experiments that conventional models cannot support. Demonstrations show, for example, that increasing the extent of interactions between populations with different proclivities to electric vehicles has an asymmetrical outcome. High-proclivity electric vehicle adoption is generally unaffected as interactions increase, but low-proclivity adoption is accelerated. By representing individual fleets' requirements and costs at a high level of detail, incorporating an adoption decision model informed by a wide body of empirical research, and broadening the array of variables and dynamics available for experimentation, this integrated model offers a new way to understand the urgent challenge of eliminating emissions from the most emissions-intensive transportation sectors.

Trinko, David A.↗

DOE OSTI2025

Exploring the Effects of Varying Pre-Chamber Geometry in a Heavy-Duty Natural Gas Optical Engine under Dilution Conditions

Pre-chamber combustion is an advanced ignition strategy that has been shown to enhance spark ignition (SI) combustion stability in natural gas (NG) engines by providing distributed ignition sites from turbulent jets and enhancing main-chamber turbulence. Pre-chamber combustion has been proven especially advantageous compared to SI in ultra-lean and dilute operating conditions. This work involves experimental investigation of the effects of varying passive pre-chamber nozzle configuration on pre-chamber and main chamber combustion under simulated exhaust gas recirculation (EGR) dilution (0 and 20%) conditions in a heavy-duty, single-cylinder, optically accessible NG engine at stoichiometric fuel-air ratio. Pre-chamber nozzle configurations include four pre-chambers with constant nozzle area to pre-chamber volume ratio (A/V) with different nozzle sizes and orientations and one configuration with larger nozzles. The optical engine is operated in a skip-fire sequence consisting of 18 motored cycles followed by two consecutive fired cycles to elucidate the effect of combustion residuals (internal EGR) on combustion evolution. Pressure-based diagnostics are used to monitor pre-chamber and subsequent main chamber combustion, and optical diagnostics include high-speed OH* chemiluminescence to visualize the development of pre-chamber jets and the resulting ignition of the main chamber charge. Heat release analysis of the in-cylinder pressure data indicates that a faster pre-chamber pressure rise does not always translate into faster main-chamber combustion. The pre-chamber with the smallest nozzle diameter produced the highest pre-chamber pressure rise and fastest combustion under non-diluted conditions. However, dilution delays the main chamber ignition for smaller nozzles despite a comparable rise in pre-chamber pressure compared to configurations with larger nozzles. This effect is more pronounced for cycles with in-cylinder combustion residuals in addition to external dilution. Additionally, it was observed that pre-chambers with swirling nozzles have a faster pressure rise in the pre-chamber and main chamber under dilute conditions. Optical diagnostics suggest that the main reason for the delay between the pre-chamber pressure rise and main-chamber combustion lies in jet quenching and delayed re-ignition, which can even lead to misfire if jets emitted from small nozzles combined with dilution fail to re-ignite.

Dhotre, Akash [University Of Minnesota-Twin Cities↗

DOE OSTI2021

CDMSLITE LIGHTLY IONIZING PARTICLE SEARCH

An encouraging avenue of investigation beyond the Standard Model (SM) is the search for particles with fractional electric charge. While the SM does not anticipate the existence of free particles with a fractional electric charge, fractionally charged particles (FCPs) have not been experimentally excluded. Free fractionally charged particles are a feature of viable extensions to the Standard Model with extra U(1) gauge symmetries. This thesis describes a FCP search using CDMSlite Run 2 Period 1 data which results in the first direct detection limits on cosmogenic FCPs with an electric charge as small as e{108 and the most stringent limit on vertical intensity for FCPs with an electric charge § e{160. This analysis is also the first to consider cosmogenic FCPs with a wide range of masses (5 MeV/c2 – 100 TeV/c2) and velocities (

Poudel, S. S.↗

DOE OSTI2026

Toward the Neutrino Discovery Platform: An Auditable, Uncertainty-Bearing Toolchain for MINERvA Open-Data Cross-Section Analysis

Breaux, Auto [Tulane U. (main)]↗

DOE OSTI2020

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

Abstract For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical ‘Rosetta Stone’ to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org and KBase.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Effects of cluster correlations on fragment emission in 12 C+ 12 C at 50 MeV/nucleon

The effects of cluster correlations have been studied in the 12 C + 12 C reaction at 50 MeV/nucleon, using three AMD models, the AMD (AMD/D) without any additional cluster correlations, AMD/D-COALS with nucleon correlations based on a coalescence prescription for light cluster formations with A ≤ 4 and AMD-Cluster with an extended cluster correlation in two-nucleon collision processes and a special treatment for intermediate fragment formation with A ≤ 9. The angular distributions and energy spectra of fragments have been simulated and compared with the available experimental data. It is found that the cluster correlations take a crucial role to describe the productions of light charged particles (LCPs) and intermediate mass fragments (IMFs), and the AMD-cluster studied here provides a consistent overall reproduction of the experimental data. It is also shown that the significant effects of the secondary decay processes are involved for the fragment production besides the dynamical productions in the AMD stage. Detailed LCP and IMF production mechanisms involved in the intermediate energy heavy ion collisions are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Reconstruction and Selection of Neutrino Interactions in MicroBooNE using Deep Convolutional Neural Networks

In this document, we describe a new reconstruction workflow developed for the MicroBooNE experiment. It features the use of Deep Convolutional Neural Networks trained to recognize key structures within the data sufficient for the 3D reconstruction of neutrino interactions within the detector. As a test of the reconstruction utility, the products of the reconstruction workflow are used to select inclusive charged-current (CC) $\nu_e$ and $\nu_\mu$ interactions in both simulated and real MicroBooNE data. In simulation, our $\nu_e$ and $\nu_\mu$ selections achieve an efficiency of 57% and 68\%, respectively, with a purity of 91% and 96%, respectively. We find that these selections are competitive with the inclusive selections used for the most recent MicroBooNE LEE searches. In particular, the CC-$\nu_e$ inclusive selection efficiency improves by over 20% while also improving sample purity. As a first step in quantifying potential bias, the data and Monte Carlo expectati ons are compared for both selections using the MicroBooNE open data. Within statistical and systematic uncertainties, both the electron and muon CC-inclusive event samples agree. A comparison of the real data events chosen by our work and another reconstruction framework shows that the two analyses each identify a sizeable fraction of events the other does not. This suggests that future analyses integrating the strengths of each could lead to combined gains. This work demonstrates, for the first time on real LArTPC data, state-of-the-art neutrino interaction reconstruction centered around deep learning algorithms.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Counter Data Paucity through Adversarial Invariance Encoding: A Case Study on Modeling Battery Thermal Runaway

Lithium-ion batteries, widely used for their durability and high energy storage, face the risk of internal short circuits leading to catastrophic thermal runaway events. These events, triggered by external stimuli like mechanical loads, pose safety concerns in applications such as electric vehicles. Detecting and understanding thermal runaway events is crucial, but physics-driven models struggle to explain the non-linear evolution of battery temperature during these events, considering factors like material composition and state-of-charge. Due to the rarity of these events and the cost of data collection, we propose a deep learning (DL) model to predict battery temperature responses during thermal runaway. The challenge lies in the scarcity of data, making traditional DL models prone to overfitting and learning low-quality representations of the complex process.Our approach introduces a novel few-shot architecture that incorporates an adversarially governed invariant encoding process. This architecture aims to distill "invariant" relationships by addressing distributional shifts in data across various battery properties, facilitating the detection of thermal runaway events. Specifically, our results demonstrate that deep learning models conditioned on these "invariant" representations outperform state-of-the-art baselines, achieving a remarkable 96.8% performance improvement in terms of the popular metric MAPE. This framework presents a promising direction for enhancing battery safety modeling, particularly in the context of rare and complex events like thermal runaway. Our code and code and dataset used for the paper are public1.

Tabassum, Anika [ORNL] (ORCID:0000000254600955)↗