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DOE OSTI2026

Investigation and Diagnosis of Faulty Data Channels in CMS Outer Tracker Module Testing

The High-Luminosity Large Hadron Collider (HL-LHC) is currently undergoing upgrades to improve its luminosity. In parallel, this requires an upgrade to the Compact Muon Solenoid (CMS)’s Outer Tracker, consisting of Pixel-Strip (PS) and Strip-Strip (2S) modules that can accurately track the path of charged particles originating from the collisions. It follows that such complex modules call for extensive testing, requiring a sophisticated Data Acquisition (DAQ) system that can perform specific tests to assess their performance. In addition, errors caused by the hardware of a given testing station, and its associated data channel, need to be accurately identified to guarantee proper testing of modules. We have developed a software extension to the Phase-II Outer Tracker Analyzer of Test Outputs (POTATO), which is a specialized software designed to analyze and grade all of the module tests through a centralized database. This extension categorizes and analyzes module test results by its station and data channel. Its analysis can be used to identify trends in grading that indicate issues in these channels’ grading process rather than in the individual modules. This poster shows our methodology and results for identifying faulty data channels. Using this extension, we can quickly diagnose and address problems in our DAQ system, ensuring proper evaluation corrections for each module.

Chen, Angus [Fermilab]↗

DOE OSTI2026

Investigation and Diagnosis of Faulty Data Channels in CMS Outer Tracker Module Testing

Chen, Angus [Fermilab]↗

DOE OSTI2021

Modeling impurity concentrations in liquid argon detectors

Impurities in noble liquid detectors used for neutrino and dark matter experiments can significantly impact the quality of data. We present an experimentally verified model for describing the dynamics of impurity distributions in liquid argon (LAr) detectors. The model considers sources, sinks, and transport of impurities within and between the gas and liquid argon phases. Measurements of oxygen concentrations in a 20-L LAr multi-purpose test stand are compared to calculations made with this model to show that an accurate description of the concentrations under various operational conditions can be obtained. A result of this analysis is a determination of Henry’s coefficient for oxygen in LAr. Additionally, these calculations also show that some processes have small effects on the impurity dynamics and excluding them yields a solution as a sum of two exponential terms. This solution provides a simple way to extract Henry’s coefficient with negligible approximation error. It is applied to the data and the Henry’s coefficient for oxygen in LAr is obtained as 0.84 +0.09 -0.05 , consistent with literature results. Finally, based on the analysis of the data with the model, we further suggest that, for a large liquid argon detector, barriers to flow (“baffles”) installed in the gas phase to restrict flow can help reduce the ultimate impurity concentration in the LAr.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Fast Z Z free entangling gates for superconducting qubits assisted by a driven resonator

Engineering high-fidelity two-qubit gates is an indispensable step toward practical quantum computing. For superconducting quantum platforms, one important setback is the stray interaction between qubits, which causes significant coherent errors. For transmon qubits, protocols for mitigating such errors usually involve fine-tuning the hardware parameters or introducing usually noisy flux-tunable couplers. In this work, we propose a simple scheme to cancel these stray interactions. The coupler used for such cancelation is a driven high-coherence resonator, where the amplitude and frequency of the drive serve as control knobs. Through the resonator-induced-phase interaction, the static Z Z coupling can be entirely neutralized. We numerically show that such a scheme can enable short and high-fidelity entangling gates, including cross-resonance controlled-not (cnot) gates within 40 ns and adiabatic controlled- Z gates within 140 ns. Our architecture is not only Z Z -free, but also contains no extra noisy components, such that it preserves the coherence times of fixed-frequency transmon qubits. With the state-of-the-art coherence times, the error of our cross-resonance cnot gate can be reduced to below 10 − 4 .

Huang, Ziwen↗

DOE OSTI2023

Design Rules for Two–Dimensional Organic Semiconductor–Incorporated Perovskites (OSiP) Gleaned from Thousands of Simulated Structures

Two-dimensional (2D) halide perovskites are an attractive class of hybrid perovskites that have additional optoelectronic tunability due to their accommodation of relatively large organic ligands. Nevertheless, contemporary ligand design depends on either expensive trial-and-error testing of whether a ligand can be integrated within the lattice or conservative heuristics that unduly limit the scope of ligand chemistries. Here, the structural determinants of stable ligand incorporation within Ruddlesden-Popper (RP) phase perovskites are established by molecular dynamics (MD) simulations of over ten-thousand RP-phase perovskites and training of machine learning classifiers capable of predicting structural stability based solely on generalizable ligand features. The simulation results show near-perfect predictions of positive and negative literature examples, predict trade-offs between several ligand features and stability, and ultimately predict an inexhaustibly large 2D-compatible ligand design-space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Progressive Tree-Based Compression of Large-Scale Particle Data

Scientific simulations and observations using particles have been creating large datasets that require effective and efficient data reduction to store, transfer, and analyze. However, current approaches either compress only small data well while being inefficient for large data, or handle large data but with insufficient compression. Toward effective and scalable compression/decompression of particle positions, we introduce new kinds of particle hierarchies and corresponding traversal orders that quickly reduce reconstruction error while being fast and low in memory footprint. Our solution to compression of large-scale particle data is a flexible block-based hierarchy that supports progressive, random-access, and error-driven decoding, where error estimation heuristics can be supplied by the user. For low-level node encoding, we introduce new schemes that effectively compress both uniform and densely structured particle distributions. Our proposed methods thus target all three phases of a tree-based particle compression pipeline, namely tree construction, tree traversal, and node encoding. In conclusion, the improved efficacy and flexibility of these methods over existing compressors are demonstrated through extensive experimentation, using a wide range of scientific particle datasets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Beam Performance of the Positron Transport Line for CEBAF Positron Upgrade

The Low Energy Recirculator Facility (LERF) at Jefferson Lab, formerly operated for the Free-Electron Laser program, has been proposed as the injector complex for the planned 12 GeV CEBAF positron upgrade (Ce+BAF), with an additional pathway to support a potential 22 GeV CEBAF electron upgrade. In this configuration, LERF would generate and pre-accelerate positrons to 123 MeV, matching the present injection energy into the North Linac. Due to the relatively large emittance expected from the positron source, a comprehensive acceptance study has been performed from LERF through the CEBAF recirculating linacs and beam transport lines to the experimental halls. The objective is to establish the positron phase-space acceptance and provide design feedback to the positron production and capture systems. Furthermore, given CEBAF’s capability to deliver highly polarized beams, spin-tracking simulations have been carried out including magnet imperfections, alignment errors, and synchrotron-radiation–induced energy spread. Particular attention is given to the evolution of the spin tune and the corresponding depolarization mechanisms along the beam delivery path, especially for providing longitudinal polarization at the experimental halls. These results inform injector design choices and assess the overall feasibility of delivering high-polarization positron beams in CEBAF.

Ogur, S. [Thomas Jefferson National Accelerator Fa↗

DOE OSTI2024

Development of a Practical Secondary Control for Hardware Microgrids

Practical, vendor-agnostic interoperability guidelines for the secondary control architecture of microgrids (MGs) with multiple grid-forming (GFM) inverter-based resources (IBRs) have not yet been developed. Therefore, this paper proposes a generic and vendor-agnostic secondary control architecture that operates with all GFM IBRs and synchronous generators. This secondary control does not require the use of additional measurement devices in the MG and utilizes the inherent communication systems of the GFM units, such as Modbus TCP/IP. The practical challenges of Modbus registers, such as packet loss and quantization error, and their detrimental impacts on secondary control actions are investigated. The proposed three-stage modification for any secondary control architecture to mitigate these impacts includes: 1) averaging the data read, 2) situational event-triggering of the controller, and 3) finite iteration of the controlling action. The proposed method is validated using a three-phase electric power, 480 V, 60 Hz, 500 kVA laboratory hardware microgrid with commercial two GFM IBRs and one diesel generator. The experimental results corroborates the fact the proposed modification in the secondary control architecture is advantageous for practical usage under erroneous measurements.

grid-forming inverter↗

DOE OSTI2025

MODIS Aerosol and Low‐Cloud Retrievals: Orographic Effects in the Wake of Macquarie Island

Abstract Visible/infrared imagery from passive satellites is commonly relied upon to study low cloud microphysics over oceanic regions, including for the Southern Ocean (SO), but relatively little validation has been undertaken for the SO. In this article, we compare low‐cloud effective radius ( r e ), cloud droplet number concentration ( N d ) and cloud liquid water path (LWP) retrievals from the NASA Moderate Imaging Spectroradiometer (MODIS) with surface measurements collected during the Macquarie Island Cloud and Radiation Experiment (MICRE). MODIS 3.7‐μm band retrievals show little bias and moderately good correlation relative to MICRE retrievals for liquid‐phase low clouds when restricted to Solar Zenith Angles <65° on spatial scales of 50–100 km. However, the low overall bias in 3.7‐μm band effective radius ( r e 3.7 ) retrievals partly results from cancellation of errors: r e 3.7 is overestimated for non‐to‐lightly precipitating clouds, and underestimated for heavier drizzling clouds by ∼1–1.5 μm. 1.6‐μm and 2.1‐μm band r e retrievals are biased high. N d may likewise be slightly under‐ or overestimated depending on the concentration, but there is insufficient data to provide confidence in this result. Interestingly, a composite of MODIS retrievals from 2002 to 2020 show a distinct region of enhanced cloud cover and N d (and lower r e ) in the wake of Macquarie Island associated with orographic cloud formation. MODIS retrievals of aerosol optical depth (AOD) and Angstrom Exponent (AE) upwind and downwind of the island do not differ significantly. Comparison with MICRE measurements suggests that MODIS Collection 6 AOD retrievals are reasonable, while AE is problematically large.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Simultaneous x-ray diffraction measurements of nine pressure calibrants to 140 GPa

Accurate pressure calibration is fundamental to quantitative high-pressure science, yet inconsistencies among widely used secondary pressure scales persist. In this work, we report simultaneous synchrotron x-ray diffraction meatogether in diamond anvil cells to 140 GPa. This simultaneous-measurement strategy minimizes transitive errors and run-to-run inconsistencies inherent to paired-measurement approaches, yielding direct experimental volume-volume (V-V) relations among the calibrants. These V-V relations link the compression state of each phase to that of every other phase measured in the same experiment. By anchoring these V-V relations to the reduced 300 K equation of state (EOS) of copper derived from ramp-compression measurements, we derive an internally consistent set of Cu-referenced pressure scales via Vinet EOS fits. Because the primary experimental result is the V-V dataset, it provides an EOS-independent constraint that can be rereferenced if primary standards are revised. The dataset further enables a direct experimental cross-check of consistency between the adopted reduced 300 K Cu EOS and the corresponding reduced 300 K ramp-derived EOSs for Pt, Au, Ta, and hcp-Fe under a fixed V-V constraint. The residuals quantify small, material-dependent mismatches between each phase EOS and the Cu reference EOS.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Robust Distributed State Estimator for Interconnected Transmission and Distribution Networks (Final Report RPPR-1)

This project’s objective is to develop a combined transmission and distribution state estimator which accounts for very large system size and model complexity (by way of distributing the computations) and large number of solar PV units connected to the distribution system on multiple feeders. The project not only provides a robust formulation and solution to this problem but also tests the solution by implementing it on a well-established large utility system. It introduces several improvements with respect to the state of the art in existing state estimation software: (a) The developed state estimator (SE) allows robust and accurate monitoring of bidirectional flows in distribution systems which result due to the distributed energy sources which are not observable and thus not incorporated in generation dispatch; (b) Large utility systems with tens of thousands of transmission buses and hundreds of thousands of distribution nodes are difficult to model as a single integrated system. This shortcoming is addressed by developing a “scalable distributed computational framework” which allows splitting the ultra large system models into several small subsystems and coordinating their solution by a robust and practical state estimation formulation; (c) Measurement errors irrespective of their locations are detected and removed by the developed state estimator. Historically, transmission and distribution systems were analyzed and operated as two independent systems. Given the non-transposed short feeder sections, unevenly loaded phases, strictly radial configuration and unidirectional power flows in the absence of remote generation, distribution system analysis was customized to account for these characteristics. However, some of these assumptions are no longer valid (non-radial configuration, bidirectional power flows) and thus distribution system analysis should be revisited. Furthermore, in the past, the interaction between the transmission and distribution systems was quite passive, where distribution substations were modeled as lumped loads in the transmission system model. With substantial generation injected by renewable generation located in the distribution systems, such modeling will no longer be accurate. The developed state estimator facilitates proper monitoring of the interactions between the transmission and distribution systems and enables smart dispatch of these units which are made observable by the state estimator.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Leveraging machine learning to enhance aerosol classification using Single-Particle Mass Spectrometry

Advancing automated classification of atmospheric aerosols from Single-Particle Mass Spectrometry (SPMS) data remains challenging due to overlapping ion signatures, compositional diversity, and limited labeled data. This study evaluates supervised and semi-supervised learning frameworks to enhance aerosol identification by jointly leveraging labeled and unlabeled spectra. Four models were compared: a supervised Support Vector Machine (SVM), a self-training SVM, a stacked autoencoder classifier, and a stacked autoencoder trained using a temporal-ensembling Mean Teacher approach. All models achieved high and stable accuracies (90.0 %–91.1 %), surpassing previous results on the same dataset (87 %) and matching the performance of state-of-the-art deep learning methods. Despite small global metric differences (≤ 1 %), semi-supervised variants yielded up to 5 %–10 % improvements for compositionally rare particle types – such as soot (0.77 % of spectra, F1-score: 0.93–0.97) and hazelnut pollen (0.98 % of spectra, F1-score: 0.97–1.00) – equating to roughly ∼ 187 additional correctly classified spectra. These gains are scientifically significant, as such rare particles exert disproportionate influence on radiative absorption and ice nucleation processes; their improved detection reduces modeled uncertainties in aerosol absorption optical depth and mixed-phase cloud ice nucleation rates. The models' residual misclassifications (≈ 9 %) largely arise from true spectral overlap among chemically adjacent species (e.g., Na- vs. K-feldspar, coated vs. uncoated feldspars), reflecting physical compositional continuity rather than algorithmic error. Collectively, these findings demonstrate that leveraging unlabeled data to learn robust spectral representations and refine classification enhances both fidelity and interpretability, bridging data-driven analysis with aerosol–climate process understanding.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Development and Validation of a Simulation Testbed for the Intelligent Building Agents Laboratory (IBAL) Using TRNSYS

This paper documents the development and validation of a dynamic primary cooling and thermal storage system simulation testbed. The system simulation testbed, sIBAL, is based on the Intelligent Building Agents Laboratory (IBAL) at the National Institute of Standards and Technology (NIST), which is a research infrastructure and testbed for the development, evaluation, and demonstration of intelligent control algorithms. The sIBAL testbed developed in this project will serve as a virtual twin of the real facility for future control algorithm development. The details of the methodologies used to develop and validate the simulation testbed, which replicates the dynamic behaviors of the primary cooling and ice storage system in the IBAL facility, are presented. The simulation testbed was developed in TRNSYS using built-in component models and MATLAB functions to replicate the two water-cooled chillers, a thermal storage tank, pumps, valves and other components for four different operation modes. Experiments on IBAL components were designed and executed to generate experimental data for model development and verification of the simulation platform. The validation of the simulation results was carried out in two phases: 1) independent component simulations for the chillers and thermal storage tank, and 2) a combined testbed simulation of the entire hydronic system. Comparison of simulation results to the experimental data obtained from the IBAL facility showed errors within 1 °C for the temperatures outputs of both the chiller and the thermal storage model. The error is within an acceptable range for further intelligent control algorithms development. The findings from the study are summarized and presented along with areas where additional research is needed. In addition, data filtering procedures and model refinement measures utilized to improve the accuracy and accelerate the computation time of the simulation are presented.

cooling systems↗

DOE OSTI2020

SNS: A Solution-Based Nonlinear Subspace Method for Time-Dependent Model Order Reduction

Several reduced order models have been successfully developed for nonlinear dynamical systems. To achieve a considerable speed-up, a hyper-reduction step is needed to reduce the computational complexity due to nonlinear terms. Many hyper-reduction techniques require the construction of nonlinear term basis, which introduces a computationally expensive offline phase. A novel way of constructing nonlinear term basis within the hyper-reduction process is introduced. In contrast to the traditional hyper-reduction techniques where the collection of nonlinear term snapshots is required, the SNS method avoids collecting the nonlinear term snapshots. Instead, it uses the solution snapshots that are used for building a solution basis, which enables avoiding an extra data compression of nonlinear term snapshots. As a result, the SNS method provides a more efficient offline strategy than the traditional model order reduction techniques, such as the DEIM, GNAT, and ST-GNAT methods. The SNS method is theoretically justified by the conforming subspace condition and the subspace inclusion relation. It is useful for model order reduction of large-scale nonlinear dynamical problems to reduce the offline cost. It is especially useful for ST-GNAT that has shown promising results, such as a good accuracy with a considerable online speed-up for hyperbolic problems in a recent paper by Choi and Carlberg [SIAM J. Sci. Comput., 41 (2019), pp. A26--A58], because ST-GNAT involves an expensive offline cost related to collecting nonlinear term snapshots. Error analysis for the SNS method is presented. Numerical results support that the accuracy of the solution from the SNS method is comparable to the traditional methods and a considerable speed-up (i.e., a factor of two to a hundred) is achieved in the offline phase.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Mitigating spectral bias in neural operators via high-frequency scaling for physical systems

Neural operators have emerged as powerful surrogates for modeling complex physical problems. However, they suffer from spectral bias making them oblivious to high-frequency modes, which are present in multiscale physical systems. Therefore, they tend to produce over-smoothed solutions, which is particularly problematic in modeling turbulence and for systems with intricate patterns and sharp gradients such as multi-phase flow systems. In this work, we introduce a new approach named high-frequency scaling (HFS) to mitigate spectral bias in convolutional-based neural operators. By integrating HFS with proper variants of UNet, we demonstrate a higher prediction accuracy by mitigating spectral bias in single and two-phase flow problems. Unlike Fourierbased techniques, HFS is directly applied to the latent space, thus eliminating the computational cost associated with the Fourier transform. Additionally, we investigate alternative spectral bias mitigation through a diffusion model conditioned on neural operators. While the diffusion model integrated with the standard neural operator may still suffer from significant errors, these errors are substantially reduced when the diffusion model is integrated with a HFS-enhanced neural operator.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Multiresonant metasurfaces for arbitrarily broad bandwidth pulse chirping and dispersion compensation

Here, we show that ultrathin metasurfaces with a specific multiresonant response can enable simultaneously arbitrarily strong and arbitrarily broadband dispersion compensation, pulse (de)chirping, and compression or broadening. This breakthrough overcomes the fundamental limitations of both conventional nonresonant approaches (bulky) and modern singly resonant metasurfaces (narrowband) for quadratic phase manipulations of electromagnetic signals. The required nonuniform trains of resonances in the electric and magnetic sheet conductivities that completely control phase delay, group delay, and chirp are rigorously derived and the limitations imposed by fundamental physical constraints are thoroughly discussed. Subsequently, a practical, truncated approximation by finite sequences of physically realizable linear resonances is constructed and the associated error is quantified. By appropriate spectral ordering of the resonances, operation can be achieved either in transmission or reflection mode, enabling full space coverage. The proposed concept is not limited to dispersion compensation, but introduces a generic and powerful ultrathin platform for the spatiotemporal control of broadband real-world signals with a myriad of applications in modern optics, microwave photonics, radar, and communication systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Exactness of OPF Relaxation on Three-phase Radial Networks with Delta Connections

Simulations have shown that while semi-definite relaxations of AC optimal power flow (AC-OPF) on three-phase radial networks with only wye connections tend to be exact, the presence of delta connections seem to render them inexact. This paper shows that such inexactness originates from the non-uniqueness of relaxation solutions and numerical errors amplified by the non-uniqueness. This finding motivates two algorithms to recover the exact solution of AC-OPF in unbalanced distribution networks featuring both wye and delta connections. In simulations using IEEE 13, 37 and 123-bus systems, the proposed algorithms provide exact optimal solutions up to numerical precision.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Measuring orbit responses with oscillating trajectories in the Fermilab Linac

Recording changes in beam transverse positions and longitudinal phase reported by Beam Position Monitors (BPMs) in response to a beam deflection by an upstream dipole corrector or RF cavity phase (orbit response) is a powerful tool for analysis of accelerator optics and assisting with machine tuning. In Fermilab Linac, orbit responses were recorded by oscillating the corrector currents and cavity phases in a sinusoidal manner parasitically during regular operation, simultaneously oscillating up to 19 correctors and 7 cavities at distinct frequencies, providing faster, drift-resistant measurements through frequency-domain analysis. This report describes the technique, including error estimations and consistency checks and shows examples of the measurements.

Shemyakin, Alexander [Fermilab] (ORCID:00000001501↗