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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The Impact of Nuclear Physics Uncertainties on Galactic Chemical Evolution Predictions

Modeling the evolution of the elements in the Milky Way is a multidisciplinary and challenging task. In addition to simulating the ~13 billion years evolution of our Galaxy, chemical evolution simulations must keep track of the elements synthesized and ejected from every astrophysical site of interest (e.g., supernova, compact binary merger). The elemental abundances of such ejecta, which are a fundamental input for chemical evolution codes, are usually taken from theoretical nucleosynthesis calculations performed by the nuclear astrophysics community. Therefore, almost all chemical evolution predictions rely on the nuclear physics behind those calculations. In this proceedings article, we highlight the impact of nuclear physics uncertainties on galactic chemical evolution predictions. We demonstrate that nuclear physics and galactic evolution uncertainties both have a significant impact on interpreting the origin of neutron-capture elements in our Solar System. Those results serve as a motivation to create and maintain collaborations between the fields of nuclear astrophysics and galaxy evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Evaluating Awkward Arrays, uproot, and coffea as a query platform for High Energy Physics Data

Query languages for High Energy Physics (HEP) are an ever present topic within the field. A query language that can efficiently represent the nested data structures that encode the statistical and physical meaning of HEP data will help analysts by ensuring their code is more clear and pertinent. As the result of a multi-year effort to develop an in-memory columnar representation of high energy physics data, the NumPy, Awkward Array, and uproot Python packages present a mature and efficient interface to HEP data. Atop that base, the coffea package adds functionality to launch queries at scale, manage and apply experiment-specific transformations to data, and present a rich object-oriented columnar data representation to the analyst. Recently, a set of Analysis Description Language (ADL) benchmarks has been established to compare HEP queries in multiple languages and frameworks. In this paper we present these benchmark queries implemented within the coffea framework and discuss their readability and performance characteristics. We find that the columnar queries perform as well or better than the implementations given in previous studies.

Gray, L.↗
DOE OSTI2022

Nanosecond machine learning regression with deep boosted decision trees in FPGA for high energy physics

We present a novel application of the machine learning / artificial intelligence method called boosted decision trees to estimate physical quantities on field programmable gate arrays (FPGA). The software package fwXmachina features a new architecture called parallel decision paths that allows for deep decision trees with arbitrary number of input variables. It also features a new optimization scheme to use different numbers of bits for each input variable, which produces optimal physics results and ultraefficient FPGA resource utilization. Problems in high energy physics of proton collisions at the Large Hadron Collider (LHC) are considered. Estimation of missing transverse momentum (E T miss ) at the first level trigger system at the High Luminosity LHC (HL-LHC) experiments, with a simplified detector modeled by Delphes, is used to benchmark and characterize the firmware performance. The firmware implementation with a maximum depth of up to 10 using eight input variables of 16-bit precision gives a latency value of $\mathcal{O}$(10) ns, independent of the clock speed, and $\mathcal{O}$(0.1)% of the available FPGA resources without using digital signal processors.

Instruments & Instrumentation↗
DOE OSTI2021

Explicit structure-preserving geometric particle-in-cell algorithm in curvilinear orthogonal coordinate systems and its applications to whole-device 6D kinetic simulations of tokamak physics

Explicit structure-preserving geometric particle-in-cell (PIC) algorithm in curvilinear orthogonal coordinate systems is developed. The work reported represents a further development of the structure-preserving geometric PIC algorithm achieving the goal of practical applications in magnetic fusion research. The algorithm is constructed by discretizing the field theory for the system of charged particles and electromagnetic field using Whitney forms, discrete exterior calculus, and explicit non-canonical symplectic integration. In addition to the truncated infinitely dimensional symplectic structure, the algorithm preserves exactly many important physical symmetries and conservation laws, such as local energy conservation, gauge symmetry and the corresponding local charge conservation. As a result, the algorithm possesses the long-term accuracy and fidelity required for first-principles-based simulations of the multiscale tokamak physics. The algorithm has been implemented in the SymPIC code, which is designed for high-efficiency massively-parallel PIC simulations in modern clusters. The code has been applied to carry out whole-device 6D kinetic simulation studies of tokamak physics. A self-consistent kinetic steady state for fusion plasma in the tokamak geometry is numerically found with a predominately diagonal and anisotropic pressure tensor. The state also admits a steady-state sub-sonic ion flow in the range of 10 km s -1 , agreeing with experimental observations and analytical calculations Kinetic ballooning instability in the self-consistent kinetic steady state is simulated. It is shown that high-n ballooning modes have larger growth rates than low-n global modes, and in the nonlinear phase the modes saturate approximately in 5 ion transit times at the 2% level by the E × B flow generated by the instability. These results are consistent with early and recent electromagnetic gyrokinetic simulations.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Comparison of Different Variants of the U.S. Army Occupational Physical Assessment Test

The U.S. Army Occupational Physical Assessment Test (OPAT) is a pre-enlistment physical employment screening assessment developed to place recruits and soldiers into Military Occupational Specialties (MOSs) based on their physical capabilities in order to optimize performance and limit injury. The OPAT consists of the seated power throw (SPT), strength deadlift (SDL), standing long jump, and interval aerobic run. During the scientific validation of the OPAT, two variants of the SPT and two variants of the SDL were used. Although the OPAT was validated using both variants for each test, U.S. Army scientists and policymakers have received queries regarding how these variants compare to each other. Therefore, the purpose of this study was to compare different variants of the SPT and SDL. Thirty-two participants (14 male and 18 female) between the ages of 18 and 42 years visited the laboratory on one occasion and performed two variants of the SPT (seated on the ground [the current OPAT standard] versus seated in a chair with a 35 cm seat height) and two variants of the SDL (using a hex-bar [the current OPAT standard] versus using paired dumbbells). Testing order for the different variants was randomized. The protocol was approved by the U.S. Army Medical Research and Development Command Institutional Review Board. Performing the SPT from a chair significantly (P < .05) increased performance when compared to performing the SPT from the ground (5.4 ± 1.3 m versus 5.0 ± 1.4 m, respectively). Values for the two SPT variants were correlated (tau = 0.90). Performing the SDL using the hex-bar significantly increased the maximal weight lifted when compared to performing the SDL using paired dumbbells (86.9 ± 18.4 kg versus 83.1 ± 18.0 kg, respectively). Values for the two SDL variants were correlated (tau = 0.83). Performing different variants of the SPT and SDL influenced the resulting score. Although these findings do not alter the administration or scoring of the OPAT, they do provide a valuable reference in the event of future inquiries regarding the development of the OPAT.

General & Internal Medicine↗
DOE OSTI2021

The Cosmic Ultraviolet Baryon Survey (CUBS) – III. Physical properties and elemental abundances of Lyman-limit systems at z < 1

ABSTRACT We present a systematic investigation of physical conditions and elemental abundances in four optically thick Lyman-limit systems (LLSs) at z = 0.36–0.6 discovered within the Cosmic Ultraviolet Baryon Survey (CUBS). Because intervening LLSs at z < 1 suppress far-UV (ultraviolet) light from background QSOs, an unbiased search of these absorbers requires a near-UV-selected QSO sample, as achieved by CUBS. CUBS LLSs exhibit multicomponent kinematic structure and a complex mix of multiphase gas, with associated metal transitions from multiple ionization states such as C ii, C iii, N iii, Mg ii, Si ii, Si iii, O ii, O iii, O vi, and Fe ii absorption that span several hundred km s−1 in line-of-sight velocity. Specifically, higher column density components (log N(H i)/cm−2≳ 16) in all four absorbers comprise dynamically cool gas with $\langle T \rangle =(2\pm 1) \times 10^4\,$K and modest non-thermal broadening of $\langle b_\mathrm{nt} \rangle =5\pm 3\,$km s−1. The high quality of the QSO absorption spectra allows us to infer the physical conditions of the gas, using a detailed ionization modelling that takes into account the resolved component structures of H i and metal transitions. The range of inferred gas densities indicates that these absorbers consist of spatially compact clouds with a median line-of-sight thickness of $160^{+140}_{-50}$ pc. While obtaining robust metallicity constraints for the low density, highly ionized phase remains challenging due to the uncertain $N\mathrm{(H\, {\small I})}$, we demonstrate that the cool-phase gas in LLSs has a median metallicity of $\mathrm{[\alpha /H]_{1/2}}=-0.7^{+0.1}_{-0.2}$, with a 16–84 percentile range of [α/H] = (−1.3, −0.1). Furthermore, the wide range of inferred elemental abundance ratios ([C/α], [N/α], and [Fe/α]) indicate a diversity of chemical enrichment histories. Combining the absorption data with deep galaxy survey data characterizing the galaxy environment of these absorbers, we discuss the physical connection between star-forming regions in galaxies and diffuse gas associated with optically thick absorption systems in the z < 1 circumgalactic medium.

Zahedy, Fakhri S. (ORCID:0000000178692551)↗
DOE OSTI2021

SDSS-IV MaNGA: the physical origin of off-galaxy H α blobs in the local Universe

ABSTRACT H α blobs are off-galaxy emission-line regions with weak or no optical counterparts. They are mostly visible in H α line, appearing as concentrated blobs. Such unusual objects have been rarely observed and studied, and their physical origin is still unclear. We have identified 13 H α blobs in the public data of MaNGA survey, by visually inspecting both the optical images and the spatially resolved maps of H α line for ∼4600 galaxy systems. Among the 13 H α blobs, 2 were reported in previously MaNGA-based studies and 11 are newly discovered. This sample, though still small in size, is by far the largest sample with both deep imaging and integral field spectroscopy. Therefore, for the first time we are able to perform statistical studies to investigate the physical origin of H α blobs. We examine the physical properties of these H α blobs and their associated galaxies, including their morphology, environments, gas-phase metallicities, kinematics of ionized gas, and ionizing sources. We find that the H α blobs in our sample can be broadly divided into two groups. One is associated with interacting/merging galaxy systems, of which the ionization is dominated by shocks or diffuse ionized gas. It is likely that these H α blobs used to be part of their nearby galaxies, but were stripped away at some point due to tidal interactions. The other group is found in gas-rich systems, appearing as low-metallicity star-forming regions that are visually detached from the main galaxy. These H α blobs could be associated with faint discs, spiral arms, or dwarf galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A comparative study of the physical properties for a representative sample of Narrow and Broad-line Seyfert galaxies

ABSTRACT We present a comparative study of the physical properties of a homogeneous sample of 144 Narrow line Seyfert 1 (NLSy1) and 117 Broad-line Seyfert 1 (BLSy1) galaxies. These two samples are in a similar luminosity and redshift range and have optical spectra available in the 16th data release of Sloan Digital Sky Survey (SDSS-DR16) and X-ray spectra in either XMM-NEWTON or ROSAT. Direct correlation analysis and a principal component analysis (PCA) have been performed using ten observational and physical parameters obtained by fitting the optical spectra and the soft X-ray photon indices as another parameter. We confirm that the established correlations for the general quasar population hold for both types of galaxies in this sample despite significant differences in the physical properties. We characterize the sample also using the line shape parameters, namely the asymmetry and kurtosis indices. We find that the fraction of NLSy1 galaxies showing outflow signatures, characterized by blue asymmetries, is higher by a factor of about 3 compared to the corresponding fraction in BLSy1 galaxies. The presence of high iron content in the broad-line region of NLSy1 galaxies in conjunction with higher Eddington ratios can be the possible reason behind this phenomenon. We also explore the possibility of using asymmetry in the emission lines as a tracer of outflows in the inner regions of Active Galactic Nuclei. The PCA results point to the NLSy1 and BLSy1 galaxies occupying different parameter spaces, which challenges the notion that NLSy1 galaxies are a subclass of BLSy1 galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

On data set tensions and signatures of new cosmological physics

ABSTRACT Can new cosmic physics be uncovered through tensions amongst data sets? Tensions in parameter determinations amongst different types of cosmological observation, especially the ‘Hubble tension’ between probes of the expansion rate, have been invoked as possible indicators of new physics, requiring extension of the ΛCDM paradigm to resolve. Within a fully Bayesian framework, we show that the standard tension metric gives only part of the updating of model probabilities, supplying a data co-dependence term that must be combined with the Bayes factors of individual data sets. This shows that, on its own, a reduction of data set tension under an extension to ΛCDM is insufficient to demonstrate that the extended model is favoured. Any analysis that claims evidence for new physics solely on the basis of alleviating data set tensions should be considered incomplete and suspect. We describe the implications of our results for the interpretation of the Hubble tension.

Cortês, Marina (ORCID:0000000304853767)↗
DOE OSTI2021

Gravitational wave physics and astronomy in the nascent era

The detections of gravitational waves (GW) by the LIGO/Virgo collaborations provide various possibilities for both physics and astronomy. We are quite sure that GW observations will develop a lot, both in precision and in number, thanks to the continuous work on the improvement of detectors, including the expected new detector, KAGRA, and the planned detector, LIGO-India. On this occasion, we review the fundamental outcomes and prospects of gravitational wave physics and astronomy. We survey the development, focusing on representative sources of gravitational waves: binary black holes, binary neutron stars, and supernovae. We also summarize the role of gravitational wave observations as a probe of new physics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Non-Hermitian quantum mechanics approach for extracting and emulating continuum physics based on bound-state-like calculations: Detailed description

Here, this work applies a reduced basis method to study the continuum physics of a finite quantum system—either few or many-body. Specifically, I develop reduced-order models, or emulators, for the underlying inhomogeneous Schrödinger equation and train the emulators against the equation's bound-state-like solutions at complex energies. The emulators rapidly and accurately interpolate and extrapolate the matrix elements of the Hamiltonian resolvent operator (Green's function) across a parameter space that includes both complex energy and other real-valued physical inputs in the Schrödinger equation. The spectra, discretized and compressed as the result of emulation, and the associated resolvent matrix elements (or amplitudes), have the defining characteristics of non-Hermitian quantum mechanics calculations, featuring complex eigenenergies with negative imaginary parts and branch cuts moved below the real axis in the complex energy plane. Therefore, one now has a method that extracts continuum physics from bound-state-like calculations and emulates those extractions in the input parameter space. Building on a prior Letter [Zhang, Phys. Rev. Lett. 135, 242501 (2025)], this article provides the full theoretical details, a comprehensive analysis of the method's performance, and a brief discussion of how it can be coupled with existing continuum approaches to perform emulations in their input parameter spaces.

ab initio calculations↗
DOE OSTI2026

Agentic artificial intelligence for multistage physics experiments at a large-scale user facility particle accelerator

We present a language-model-driven agentic artificial intelligence (AI) system to autonomously execute multistage physics experiments on a production synchrotron light source. Implemented at the Advanced Light Source particle accelerator, the system translates natural language user prompts into structured execution plans that combine archive data retrieval, control-system channel resolution, automated script generation, controlled machine interaction, and analysis. In a representative machine physics task, we show that preparation time was reduced by 2 orders of magnitude relative to manual scripting even for a system expert, while operator-standard safety constraints were strictly upheld. Core architectural features, plan-first orchestration, bounded tool access, and dynamic capability selection, enable transparent, auditable execution with fully reproducible artifacts. These results establish a blueprint for the safe integration of agentic AI into accelerator experiments and demanding machine physics studies, as well as routine operations, with direct portability across accelerators worldwide and, more broadly, to other large-scale scientific infrastructures.

Accelerator/storage ring control systems↗
DOE OSTI2024

Electron-beam-based Compton scattering x-ray source for probing high-energy-density physics

The physics basis for an electron-beam-based Compton scattering x-ray source is investigated for single-shot experiments at the major high-energy-density facilities, such as the Omega Laser Facility, National Ignition Facility, and Z pulsed power facility. A source of monoenergetic ( δ ε / ε < 5 % ) 10- to 50-keV x rays can be produced by scattering of a short-pulse optical laser by a 23- to 53-MeV electron beam and collimating the scattered photons. The number and spectrum of scattered photons are calculated as a function of electron packet charge, electron and laser pulse duration, laser intensity, and collision geometry. A source delivering greater than 10 10 photons in a 1-mm-radius spot and 100-ps time resolution is plausible with the available electron gun and laser technology. Applications of this source for x-ray diffraction, x-ray imaging, x-ray absorption fine structure, and x-ray absorption spectroscopy in high-energy-density physics experiments are described, demonstrating significant advancements compared to the present state of the art. Published by the American Physical Society 2024

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Resimulation-based self-supervised learning for pretraining physics foundation models

Self-supervised learning (SSL) is at the core of training modern large machine learning models, providing a scheme for learning powerful representations that can be used in a variety of downstream tasks. However, SSL strategies must be adapted to the type of training data and downstream tasks required. We propose resimulation-based self-supervised representation learning (RS3L), a novel simulation-based SSL strategy that employs a method of resimulation to drive data augmentation for contrastive learning in the physical sciences, particularly, in fields that rely on stochastic simulators. By intervening in the middle of the simulation process and rerunning simulation components downstream of the intervention, we generate multiple realizations of an event, thus producing a set of augmentations covering all physics-driven variations available in the simulator. Using experiments from high-energy physics, we explore how this strategy may enable the development of a foundation model; we show how RS3L pretraining enables powerful performance in downstream tasks such as discrimination of a variety of objects and uncertainty mitigation. In addition to our results, we make the RS3L dataset publicly available for further studies on how to improve SSL strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Exploring a New Regime of Molecular Orbital Physics in 4⁢d Cluster Magnets with Resonant Inelastic X-Ray Scattering

Molecular orbital systems with clusters of heavy transition metal (TM) ions are one of the most important classes of model materials for studying the interplay between local physics and effects of itinerancy. Despite a large number of candidates identified in the family of 4⁢d TM materials, an understanding of their physics from competing microscopic energy scales is still missing. Here, we bridge this gap by reporting the first resonant inelastic x-ray scattering (RIXS) measurement on a well-known series of Ru cluster magnets with a 6⁢H-perovskite structure Ba 3 ⁢M⁢Ru 2 ⁢O 9 (M 3+ = In 3+ , Y 3+ , La 3+ ) comprised of Ru dimers. In addition to providing a microscopic explanation for their anomalous magnetic properties, our RIXS measurements combined with theoretical modeling uncover a new regime of molecular orbital physics where the combined effect of large hopping and small spin-orbit coupling results in highly fragile electronic states in the Ru-dimer compounds directly manifested as an abrupt change in the RIXS spectrum accompanying a tiny change in the local structure tuned by the M-site ion. This unique combination of energy scales found only in the 4⁢d but not the 5⁢d cluster magnets highlights the value of these materials as a new platform for studying quantum phase transition involving molecular orbitals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Securing Smart Manufacturing: Detection of Cyber-Physical Attacks in CNC-Based Systems

As Industry 4.0 advances, the integration of computer numerical control (CNC) machines and advanced manufacturing technologies is transforming production into smart manufacturing systems that blend physical and digital processes as cyber-physical systems. However, this increased cyber-physical connectivity exposes manufacturing systems to cyber threats that can cause severe operational and financial disruptions. This paper presents a comparative study on cyber attacks and anomaly detection techniques in manufacturing, focusing on network traffic from CNC machines. The data extracted from network packets includes machine commands and control signals exchanged between the machine's interface and control system, crucial for maintaining operational integrity. We explore two types of cyber attacks, design modification and command injection, which pose substantial risks to CNC machine productivity and system integrity. Our investigation involves experiments on a real CNC system, highlighting the urgent need for effective detection mechanisms. To address these threats, we evaluate three anomaly detection methods: dynamic time warping (DTW), rolling average, and a deep learning, long short-term memory (LSTM) time-series-based autoencoder. Each is assessed for its effectiveness in identifying anomalous behaviors caused by the attacks. Our findings demonstrate the unique strengths and limitations of each detection technique, providing a deeper understanding of their applicability in realworld manufacturing environments. The comparative analysis indicates that while certain methods are highly effective against specific attack types, others offer broader applicability across different attacks. This study contributes to the accurate detection of anomalies in CNC machining processes, thereby enhancing the reliability and security of smart manufacturing systems against diverse cyber threats.

Williams, Bethanie [Tennessee Technological Univer↗
DOE OSTI2020

Cyber-Physical Smart Light Control System Integration with Smart Grid using Zigbee

This paper presents a hardware-in-the-loop cyber-physical system architecture design to monitor and control smart lights connected to the active distribution grid. The architecture uses Zigbee-based (IEEE 802.15.4) wireless sensor networks and publish-subscribe architecture to exchange monitoring and control signals between smart-light actuators (SLAs) and a smart-light central controller (SLCC). Each SLA integrated into a smart light consists of a Zigbee-based endpoint module to send and receive signals to and from the SLCC. The SLCC consists of a Zigbee-based coordinator module, which further exchanges the monitoring and control signals with the active distribution management system over the TCP/IP communication network. The monitoring signals from the SLAs include light status, brightness level, voltage, current, and power data, whereas, the control signals to the SLAs include light intensity, turn ON, turn OFF, standby, and default settings. We have used our existing hardware-in-the-loop (HIL) cyber-physical system (CPS) security SCADA testbed to process signals received from the SLCC and respond suitable control signals based on the smart light schedule requirements, system operation, and active distribution grid dynamic characteristics. We have integrated the proposed cyber-physical smart light control system (CPSLCS) testbed to our existing HIL CPS SCADA testbed. We use the integrated testbed to demonstrate the efficacy of the proposed algorithm by real-time performance and latency between the SLCC and SLAs. The experiments demonstrated significant results by 100% realtime performance and low latency while exchanging data between the SLCC and SLAs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Dynamic Parameter Estimation with Physics-based Neural Ordinary Differential Equations

Accurate estimation of dynamic parameters of gen-erators is crucial to building a reliable model for dynamical studies and reliable operation of the power system. This paper develops a physics-based neural ordinary differential equations (ODE) approach to learn the parameters of generator dynamic model using phasor measurement units (PMU) data. We design a physics-based neural network to represent the swing equations of the power system dynamics. A loss function is defined as the difference between dynamic simulation results from the physics-based neural networks and pseudo PMU measurements. The parameters of generator dynamic model are iteratively updated using the neural ODEs and the adjoint method. By exploiting the mini-batch scheme in neural ODE training, the parameter estimation performance is significantly improved. Numerical study results on a 3-machine 9-bus system show that the proposed algorithm outperforms state-of-the-art baseline method in both computation time and dynamic parameter estimation accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗