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DOE OSTI2024

Enabling dynamic 3D coherent diffraction imaging via adaptive latent space tuning of generative autoencoders

Abstract Coherent diffraction imaging (CDI) is an advanced non-destructive 3D X-ray imaging technique for measuring a sample’s electron density. The main challenge of CDI is loss of phase information in diffraction intensity measurements, resulting in lengthy iterative reconstruction processes that can return non-unique solutions, which pose challenges for experiments attempting to track dynamic sample evolution through multiple states. As the increased brightness of fourth-generation light sources enables faster sample measurements and drives operando experiments with Bragg CDI, there is a growing need for faster reconstruction techniques that can keep pace. We have developed an adaptive generative autoencoder approach for uniquely tracking a sample’s electron density as it dynamically evolves. Our approach adaptively tunes the low-dimensional latent embedding of a generative autoencoder, enabling a computationally efficient manner to account for time-varying shifting distributions in real-time. Analytic proof of convergence is provided as well as numerical demonstration of sample tracking with noisy measurements.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Integrated optical frequency division for microwave and mmWave generation

Abstract The generation of ultra-low-noise microwave and mmWave in miniaturized, chip-based platforms can transform communication, radar and sensing systems 1–3 . Optical frequency division that leverages optical references and optical frequency combs has emerged as a powerful technique to generate microwaves with superior spectral purity than any other approaches 4–7 . Here we demonstrate a miniaturized optical frequency division system that can potentially transfer the approach to a complementary metal-oxide-semiconductor-compatible integrated photonic platform. Phase stability is provided by a large mode volume, planar-waveguide-based optical reference coil cavity 8,9 and is divided down from optical to mmWave frequency by using soliton microcombs generated in a waveguide-coupled microresonator 10–12 . Besides achieving record-low phase noise for integrated photonic mmWave oscillators, these devices can be heterogeneously integrated with semiconductor lasers, amplifiers and photodiodes, holding the potential of large-volume, low-cost manufacturing for fundamental and mass-market applications 13 .

Science & Technology - Other Topics↗

DOE OSTI2025

Open-source generation of sigma profiles: impact of quantum chemistry and solvation treatment on machine learning performance

The combination of machine learning (ML) models with chemistry-related tasks requires the description of molecular structures in a machine-readable way. The nature of these so-called molecular descriptors has a direct and major impact on the performance of ML models and remains an open problem in the field. Structural descriptors like SMILES strings or molecular graphs lack size-independence and can be memory intensive. Machine-learned descriptors can be of low dimensionality and constant size but lack physical significance and human interpretability. Sigma profiles, which are unnormalized histograms of the surface charge distributions of solvated molecules, combine physical significance with low dimensionality and size-independence, making them a suitable candidate for a universal molecular descriptor. However, their widespread adoption in ML applications requires open access to sigma profile generation, which is currently not available. This work details the development of OpenSPGen – an open-source tool for generating sigma profiles. Also presented are studies on the effect of different settings on the efficacy of the generated sigma profiles at predicting thermophysical material properties when used as inputs to a Gaussian process as a simple surrogate ML model. We find that a higher level of theory does not translate to more accurate results. We also provide further recommendations for sigma profile calculation and use in ML models.

Salih, Fathya Y. M. [University of Notre Dame, IN ↗

DOE OSTI2024

FORSE+: Simulating non-Gaussian CMB foregrounds at 3 arcmin in a stochastic way based on a generative adversarial network

We present FORSE+, a Python package that produces non-Gaussian diffuse Galactic thermal dust emission maps at arcminute angular scales and that has the capacity to generate random realizations of small scales. This represents an extension of the FORSE (Foreground Scale Extender) package, which was recently proposed to simulate non-Gaussian small scales of thermal dust emission using generative adversarial networks (GANs). With the input of the large-scale polarization maps from observations, FORSE+ has been trained to produce realistic polarized small scales at 3′ following the statistical properties, mainly the non-Gaussianity, of observed intensity small scales, which are evaluated through Minkowski functionals. Furthermore, by adding different realizations of random components to the large-scale foregrounds, we show that FORSE+ is able to generate small scales in a stochastic way. In both cases, the output small scales have a similar level of non-Gaussianity compared with real observations and correct amplitude scaling as a power law. These realistic new maps will be useful, in the future, to understand the impact of non-Gaussian foregrounds on the measurements of the cosmic microwave background (CMB) signal, particularly on the lensing reconstruction, de-lensing, and the detection of cosmological gravitational waves in CMB polarizationB-modes.

Astronomy & Astrophysics↗

DOE OSTI2025

A generative artificial intelligence framework for long-time plasma turbulence simulations

Generative deep learning techniques are employed in a novel framework for the construction of surrogate models capturing the spatiotemporal dynamics of 2D plasma turbulence. The proposed Generative Artificial Intelligence Turbulence (GAIT) framework enables the acceleration of turbulence simulations for long-time transport studies. GAIT leverages a convolutional variational auto-encoder and a recurrent neural network to generate new turbulence data from existing simulations, extending the time horizon of transport studies with minimal computational cost. The application of the GAIT framework to plasma turbulence using the Hasegawa–Wakatani (HW) model is presented, evaluating its performance via various analyses. Very good agreement is found between the GAIT and the HW models in the spatiotemporal Fourier and Proper Orthogonal Decomposition spectra, the flow topology characterized by the Okubo–Weiss parameter, and the time autocorrelation function of turbulent fluctuations. Excellent agreement has also been obtained in the probability distribution function of particle displacements and the effective turbulent diffusivity. In-depth analyses of the latent space of turbulent states, choice of hyperparameters and alternative deep learning models for the time prediction are presented. Our results highlight the potential of Artificial Intelligence-based surrogate models to overcome the computational challenges in turbulence simulation, which can be extended to other situations such as geophysical fluid dynamics.

Artificial intelligence↗

DOE OSTI2025

Record magnetic field generation by short-pulse laser-driven capacitor-coil targets

Magnetic fields generated by capacitor-coil targets driven by intense short-pulse lasers have been characterized using ultrafast proton radiography. A 1-kJ, 15-ps laser at a center wavelength of 1053 nm irradiated the back plate of the capacitor with an intensity of ∼8.3 × 10 18 W/cm 2 , creating ultra large currents in the connecting coils. High-quality proton data obtained in the axial probing geometry show definitive signatures of magnetic field generation, allowing precision measurement of the field distribution and strength. The data show a coil current of 120 ± 10 kA producing 200 ± 20 Tesla magnetic fields at the coil center at 1.127 ns after the laser drive. In conclusion, this sets a record for magnetic field generation by the short-pulse-powered capacitor-coil targets.

Gao, Lan [Princeton Plasma Physics Laboratory (PPP↗

DOE OSTI2026

Accelerating kinetic plasma simulations with machine-learning-generated initial conditions

Computational models of plasma technologies often solve for the system operating conditions by time-stepping an initial value problem to a quasi-steady solution. However, the strongly nonlinear and multi-timescale nature of plasma dynamics often necessitate millions, or even hundreds of millions, of steps to reach convergence, reducing the effectiveness of these simulations for computer-aided engineering. We consider acceleration of kinetic plasma simulations via data-driven machine-learning-generated initial conditions, which initialize the simulations close to their final quasi-steady-state, thereby reducing the number of steps to reach convergence. Three machine-learning models are developed to predict the density and ion kinetic profiles of capacitively coupled plasma discharges relevant to the microelectronics industry. The models are trained on kinetic simulations over a range of device operating frequencies and pressures. Best performance was observed when simulations were initialized with ion kinetic profiles generated by a convolutional neural network, reducing the mean number of steps to reach convergence by 17.1× when compared to initialization with a zero-dimensional global model. We also outline a workflow for continuous data-driven model improvement and simulation speedup, with the aim of generating sufficient data for full device digital twins.

Artificial neural networks↗

DOE OSTI2026

An ultra-fast method for generating synthetic down-scattered neutron data for inertial confinement fusion implosions

In inertial confinement fusion experiments at the National Ignition Facility, asymmetries are probed by a variety of neutron diagnostics, including neutron imaging systems, real-time neutron activation diagnostics (RTNADs), and neutron spectrometers. It is often useful to generate synthetic data based on these diagnostics to validate and tune models. However, current methods of doing so using Monte Carlo particle tracing are time-consuming. In this paper, an ultra-fast method is presented for generating synthetic neutron images, RTNAD data, and spectrometry data using line integrals and 3D convolutions. While it does not contain as much physics as particle tracing codes, it is thousands of times faster and produces nearly identical data. This enables analysis techniques that depend on generating large amounts of synthetic data, which will prove very useful for the study of asymmetries going forward.

Deuterium↗

DOE OSTI2024

Evaluating Methods of Software Bill of Materials Generation to Enhance Nuclear Power Plant Cybersecurity

Instrumentation and control (I&C) systems in nuclear power plants (NPPs) are potential targets of cyberattacks and can prove deleterious for the safety of the NPPs. A Software Bill of Materials (SBOM) provides a detailed list of the various components and their dependencies in software, which helps in vulnerability and risk assessment for cyber hygiene and situational awareness. For an NPP, the process of generating an accurate SBOM report can be complex due to the legacy systems and firmware binaries involved. While most current SBOM tools are focused more on modern internet technology software, this research provides insights and guidelines for an NPP to generate an accurate and efficient SBOM. Here, the paper proposes a new methodology to help NPPs categorize software and use appropriate tools to generate SBOMs for their digital I&C systems.

SBOM↗

DOE OSTI2026

Delayed Neutron Precursor Group Parameter and Spectra Generation from Fast Fission of 235U in SCALE

Delayed neutron precursor (DNP) group data are important for modeling reactor dynamics. Although the data for individual DNPs have been developed over time, the DNP group data present in the Evaluated Nuclear Data Files (ENDF) have not been updated in the past 20 years. In this work, we use SCALE to recreate the Godiva experiment that was used to generate the original DNP group structure for fast fission of 235U. However, each DNP is modeled using up-to-date data, and the results are then converted into a newly updated group structure. This conversion uses an iterative linear least squares solver to minimize chi-squared. The approaches used in this work also enable energy spectrum generation and uncertainty tracking. The method used in this paper for fast 235U fission DNP group structure updating can be applied to different energies and fissile nuclides. Demonstration of the uncertainty tracking in reactor kinetics and dynamics simulations is shown using point reactor kinetics simulations. Results show that there are data discrepancies between the International Atomic Energy Agency database and data used in ORIGEN, which are currently being fixed. Results also show that the proposed method for group spectra generation performs well.

Seifert, Luke [University of Illinois]↗

DOE OSTI2024

Observational prospects of self-interacting scalar superradiance with next-generation gravitational-wave detectors

Abstract Current- and next-generation gravitational-wave observatories may reveal new, ultralight bosons. Through the superradiance process, these theoretical particle candidates can form clouds around astrophysical black holes and result in detectable gravitational-wave radiation. In the absence of detections, constraints—contingent on astrophysical assumptions—have been derived using LIGO-Virgo-KAGRA data on boson masses. However, the searches for ultralight scalars to date have not adequately considered self-interactions between particles. Self-interactions that significantly alter superradiance dynamics are generically present for many scalar models, including axion-like dark matter candidates and string axions. We implement the most complete treatment of particle self-interactions available to determine the gravitational-wave signatures expected from superradiant scalar clouds and revisit the constraints obtained in a past gravitational-wave search targeting the black hole in Cygnus X-1. We also project the reach of next-generation gravitational-wave observatories to scalar particle parameter space in the mass-coupling plane. We find that while proposed observatories have insufficient reach to self-interactions that can halt black hole spin-down, next-generation observatories are essential for expanding the search beyond gravitational parameter space and can reach a mass and interaction scale of ∼ 10 − 13 –10 −12 eV / c 2 and ≳ 10 17 GeV, respectively.

Collaviti, Spencer (ORCID:0009000372808236)↗

DOE OSTI2024

Self-driven ion deflectometry measurements using MeV fusion-driven protons and accelerated deuterons in the deuterated hybrid x-pinch on the MAIZE LTD generator

Abstract We report on the results of point-projection ion deflectometry measurements from a mid-size university z-pinch experiment. A 1 MA 8 kJ LTD generator at the University of Michigan (called MAIZE) drove a hybrid x-pinch (HXP) with a deuterated polyethylene fiber load to produce a point-like source of MeV ions for backlighting. In these experiments, 2.7 MeV protons were generated by DD beam-target fusion reactions. Due to the kinematics of beam-target fusion, the proton energies were down-shifted from the more standard 3.02 MeV proton energy that is released from the center-of-mass rest frame of a DD reaction. In addition to the 2.7 MeV protons, strongly anisotropic beams of 3 MeV accelerated deuterons were detected by ion diagnostics placed at a radial distance of 90 mm from the x-pinch. Numerical reconstruction of experimental data generated by deflected hydrogen ion trajectories evaluated the total current in the vacuum load region. Numerical ion-tracking simulations show that accelerated deuteron beams exited the ion source region at large angles with respect to the pinch current direction.

Physics↗

DOE OSTI2025

Spatial profile of argon (1s 5 ) metastables in an electron beam generated plasma

Electron beams with an applied magnetic field generate a secondary cold plasma with a selective chemical composition, featuring low-energy ions and metastable species in the discharge periphery, ideal for low-damage plasma treatment of material substrates. In this work, we studied the plasma generated by an e-beam using a 4 kV voltage in a pure argon gas environment under a magnetic field of 150 G and in the pressure range of 25–90 mTorr. We measured the absolute spatial density profile of argon (1 s 5 ) metastables in an electron beam generated plasma by laser-induced fluorescence and found it to be of the order of 10 16 m −3 . The electron temperature and the electron density measured by a Langmuir probe were of the order of 10 16 m −3 and less than an eV respectively. Electron-impact quenching was identified as a significant loss mechanism for the Ar(1s 5 ) state, leading to the saturation of the metastable density at higher pressures. Outside the primary ionization region, the spatial distribution of argon metastables followed a linear diffusion profile, indicating negligible additional production in those regions.

EEDF↗

DOE OSTI2025

Analysis of the linear and nonlinear stability of Alfven eigenmodes and fish-bones in JET DT discharges: mode identification and shear flows generation

The plasma in future nuclear fusion reactors will be heated by neutral beam injectors (NBIs) and high frequency electromagnetic waves as well as fusion born alpha particles. Energetic particles (EPs), with energies up to two orders of magnitude larger than the thermal plasma, can trigger EP driven modes and induce harmful EP losses, reducing the plasma heating efficiency and the economical viability of the reactor. The present study is dedicated to analyze the Alfven Eigenmode (AE) activity in JET D–T discharges, the closest experiment to reactor-like operation performed until now. There, EP driven modes are induced by the combined effect of tangential NBIs and ion cyclotron resonance heating (ICRH) driven EP. Linear and nonlinear simulations are performed with the gyro-fluid FAR3d code to analyze the AE activity observed in the discharge 99896. The linear simulations reproduce the unstable n = 3 to 5 toroidal AEs (TAE) at the inner plasma region observed in the experiment, triggered by highly energetic passing deuterium populations injected by the tangential NBIs, further accelerated by the effect of the ICRH up to 1 MeV. In addition, fish-bones triggered by energetic trapped hydrogen induced by the ICRH are also reproduced. On the other hand, the alpha particles density is too small to destabilize AEs in the experiment. Nonetheless, increasing artificially the alpha density by one order of magnitude, an n = 1 beta induced AE can be destabilized in the inner plasma region. Nonlinear simulations indicate the generation of zonal structures during the AE/fish-bone saturation phase. TAE and fish-bones causes a rather weak increase of the passing D and trapped H EP (around 2%), respectively. Shear flows and zonal currents are generated during the saturation of TAE and fish-bones. Nonlinear simulations performed for D–T and pure deuterium thermal plasma indicate AE/fish-bone activity is weaker and shear flows are less intense in the pure deuterium case, trends consistent with the experimental observations that also indicates a deterioration of the thermal plasma confinement. Therefore, both numerical studies and experimental evidence indicate the generation of shear flows by AE/fish-bones could be connected with an improvement of the thermal plasma confinement.

AE↗

DOE OSTI2025

Kinetic modeling of hot tail runaway electron generation during plasma disruptions using the JOREK code

The generation of runaway electrons (REs) during disruptions poses a significant challenge for the operation of tokamaks. The production of these high-energy electrons can cause substantial damage, particularly when the plasma current is high, making it a critical concern for ITER. For the high-temperature plasmas anticipated in ITER, the primary generation of REs may be dominated by the hot tail mechanism, which consists of the acceleration of hot electrons from the pre-disruption population which have not yet thermalized with the bulk following the rapid cooling of the plasma. To account for the significant 3D effects on RE production, a hot tail modeling framework has been developed within the non-linear 3D extended MHD code JOREK. This paper presents the structure of this framework, which is based on test electrons evolving in MHD fields. The verification of the method shows good agreement with the reference DREAM code for 0D test cases, as well as for axisymmetric simulations of 15 MA ITER H-mode disruption scenarios. Furthermore, a proof-of-principle application to a DIII-D case demonstrates the framework’s capability to capture for the first time the hot tail generation in 3D MHD simulations in realistic geometry. Preliminary results suggest that the production of REs is significantly reduced by stochastic losses.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Modeling the effect of MHD activity on runaway electron generation during SPARC disruptions

Magnetohydrodynamic (MHD) instabilities and runaway electrons (REs) interact in several ways, making it important to self-consistently model these interactions for accurate predictions of RE generation and the design of mitigation strategies, such as massive gas injection (MGI). Using M3D-C1 – an extended MHD code with a RE fluid model – we investigate the effects of 3-D nonlinear MHD activity, material injection, and 2-D axisymmetric vertical displacement events (VDEs) on RE evolution during disruptions on SPARC – a high-field, high-current tokamak designed to achieve a fusion gain Q > 1. Several cases, comprising different combinations of neon (Ne) and deuterium (D 2 ) injection, are considered. Here, our results demonstrate key effects that arise from the self-consistent RE + MHD coupling, such as an initial increase in RE generation due to MHD instability growth, decreased saturation energies of the m/n = 1/1 mode driving sawteeth-like activity, RE losses in stochastic magnetic fields, and subsequent RE confinement and plateau formation due to re-healing of flux surfaces. Large RE plateaus (>5 MA) are obtained with Ne-only injection (2-5 × 10 21 atoms), while combined D 2 + Ne injection (2 × 10 21 Ne atoms; 1.8 × 10 22 D 2 molecules) produces a lower RE current (<2 MA). With D 2 + Ne injection, a post thermal quench "cold" VDE terminates the RE beam, preventing a steady plateau. These simulations couple REs, 3-D MHD instabilities, MGI, and axisymmetric VDEs for the first time in SPARC disruption simulations and represent a crucial step in understanding RE generation and mitigation in high-current devices like SPARC.

Datta, Rishabh [Massachusetts Inst. of Technology ↗

DOE OSTI2026

Accelerating LHC event generation with simplified pilot runs and fast PDFs

High-precision calculations are an indispensable ingredient for the success of the LHC physics programme, yet their poor computing efficiency has been a growing cause for concern, threatening to become a paralysing bottleneck in the coming years. We present solutions to eliminate the apprehension by focussing on two major components of generalpurpose Monte Carlo event generators: the evaluation of parton distribution functions, and the generation of perturbative matrix elements. We show that for the cost-driving event samples employed by the ATLAS experiment to model omnipresent, irreducible Standard Model backgrounds, such as weak boson or top-quark pair production in association with jets, these computational components dominate the overall run time by up to 80 %. We demonstrate that a reduction of the computing footprint of LHAPDF and SHERPA by factors of around 40 can be achieved for multi-leg NLO event generation.

Bothmann, Enrico [Gottingen U.]↗

DOE OSTI2025

Event-driven readout development: testing of the EDWARD65P1 chip with integrated event generators

Building on a prototype readout integrated circuit for segmented silicon sensors with the EDWARD event-driven readout architecture, the front-end in each pixel was replaced by a hardware generator to verify readout performance, ensuring no data loss, consistent priority handling, and speed verification. Here, this generator produces Poisson-distributed readout requests with individually tunable rates per pixel via a digitally controlled oscillator. The resulting EDWARD65P1 test ASIC is a 32×32 pixel matrix with a 100 μm pitch, equipped with digital event generators simulating radiation hits at user-defined rates. Test results for this new design are presented.

47 OTHER INSTRUMENTATION↗