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At least 163 records · Page 9

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DOE OSTI2024

Unpaired image translation to mitigate domain shift in liquid argon time projection chamber detector responses

Deep learning algorithms often are developed and trained on a training dataset and deployed on test datasets. Any systematic difference between the training and a test dataset may severely degrade the final algorithm performance on the test dataset—what is known as the domain shift problem . This issue is prevalent in many scientific domains where algorithms are trained on simulated data but applied to real-world datasets. Typically, the domain shift problem is solved through various domain adaptation (DA) methods. However, these methods are often tailored for a specific downstream task, such as classification or semantic segmentation, and may not easily generalize to different tasks. This work explores the feasibility of using an alternative way to solve the domain shift problem that is not specific to any downstream algorithm. The proposed approach relies on modern Unpaired Image-to-Image (UI2I) translation techniques, designed to find translations between different image domains in a fully unsupervised fashion. In this study, the approach is applied to a domain shift problem commonly encountered in Liquid Argon Time Projection Chamber (LArTPC) detector research when seeking a way to translate samples between two differently distributed LArTPC detector datasets deterministically. This translation allows for mapping real-world data into the simulated data domain where the downstream algorithms can be run with much less domain-shift-related performance degradation. Conversely, using the translation from the simulated data to a real-world domain can increase the realism of the simulated dataset and reduce the magnitude of any systematic uncertainties. To evaluate the quality of the translations, we use both pixel-wise metrics and a downstream task to measure the effectiveness of UI2I methods for mitigating the domain shift problem. We adapted several popular UI2I translation algorithms to work on scientific data and demonstrated the viability of these techniques for solving the domain shift problem with LArTPC detector data. To facilitate further development of DA techniques for scientific datasets, the ‘Simple Liquid-Argon Track Samples’ dataset used in this study is also published.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Enhancing dimensionality prediction in hybrid metal halides via feature engineering and class-imbalance mitigation

We present a machine learning (ML) framework for predicting the structural dimensionality of hybrid metal halides (HMHs), including organic-inorganic perovskites, using a combination of chemically-informed feature engineering and advanced class-imbalance handling techniques. This study is motivated by the small and highly imbalanced nature of experimentally available HMH datasets, which limits the applicability and reliability of conventional ML approaches. The dataset, consisting of 494 HMH structures, is highly imbalanced across dimensionality classes (0D, 1D, 2D, 3D), posing significant challenges to predictive modeling. To mitigate this limitation, the dataset was augmented to 1336 samples using the synthetic minority oversampling technique, enabling improved learning of underrepresented dimensionality classes while preserving chemically meaningful feature relationships. We developed interaction-based descriptors designed to capture coupled steric and polarity effects relevant to dimensionality prediction, which are not readily captured by standard single-parameter or composition-only descriptors. These descriptors are integrated into a multi-stage workflow combining feature selection, ensemble stacking, and performance optimization. Our approach significantly improves F1-scores for underrepresented classes, achieving robust cross-validation performance across all dimensionalities. This work demonstrates a generalizable strategy for extracting reliable and interpretable structure–dimensionality relationships from limited experimental data, enabling pre-synthesis screening of organic cations and providing a practical blueprint for small-data ML in hybrid materials systems.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Mitigating cosmic-ray-like correlated events with a modular quantum processor

Quantum processors based on superconducting qubits are being scaled to larger qubit numbers, enabling the implementation of small-scale quantum error-correction codes. However, catastrophic chip-scale correlated errors have been observed in these processors, attributed to, e.g., cosmic ray impacts, which challenge conventional error-correction codes such as the surface code. These events are characterized by a temporary but pronounced suppression of the qubit-energy relaxation times. Here, in this study, we explore the potential for modular quantum computing architectures to mitigate such correlated energy decay events. We measure cosmic-ray-like events in a quantum processor comprising a motherboard and two flip-chip bonded daughterboard modules, each module containing two superconducting qubits. We monitor the appearance of correlated qubit decay events within a single module and across the physically separated modules. We find that while decay events within one module are strongly correlated (over 85%), events in separate modules only display approximately 2% correlations. We also report coincident decay events in the motherboard and in either of the two daughterboard modules, providing further insight into the nature of these decay events. These results suggest that modular architectures, combined with bespoke errorcorrection codes, offer a promising approach for protecting future quantum processors from chip-scale correlated errors.

Wu, Xuntao [Univ. of Chicago, IL (United States)] ↗

DOE OSTI2026

Error-mitigated nonorthogonal quantum eigensolver via shadow tomography

We present a shadow-tomography-enhanced nonorthogonal quantum eigensolver (NOQE) for more efficient and accurate electronic structure calculations on near-term quantum devices. By integrating shadow tomography into the NOQE, the measurement cost scales linearly rather than quadratically with the number of reference states, while also reducing the required qubits and circuit depth by half. This approach enables extraction of all matrix elements via randomized measurements and classical postprocessing. We analyze its sample complexity and show that, for small systems, it remains constant in the high-precision regime, while for larger systems, it scales linearly with the system size. We further apply shadow-based error mitigation to suppress noise-induced bias without increasing quantum resources. Demonstrations on the hydrogen molecule in the strongly correlated regime achieve chemical accuracy under realistic noise, showing that our method is both resource-efficient and noise-resilient for practical quantum chemistry simulations in the near term.

quantum algorithms & computation↗

DOE OSTI2025

Moving lens effect: Simulations, forecasts, and foreground mitigation

The peculiar motion of massive objects across the line of sight imprints a dipolar temperature anisotropy pattern on the cosmic microwave background known as the moving lens effect. This effect provides a unique probe of the transverse components of the peculiar velocity field, but has not yet been detected due to its small size. We implement and validate a stacking estimator for the moving lens signal using a galaxy catalog as a tracer of massive haloes combined with reconstructed velocities from the galaxy number density field. Using simulations, we forecast detection prospects for the moving lens signal from current and upcoming microwave background and galaxy surveys. Here, we demonstrate a new foreground mitigation strategy likely sufficient for current datasets, and discuss various sources of systematic error and noise. Upcoming galaxy surveys will provide high-significance statistical detections of the moving lens effect.

Astrophysical & cosmological simulations↗

DOE OSTI2024

Mitigating Scattering in a Quantum System Using Only an Integrating Sphere

Strong quantum correlated sources are essential but delicate resources for quantum information science and engineering protocols. Decoherence and loss are the two main disruptive processes that lead to the loss of nonclassical behavior in quantum correlations. In quantum systems, scattering can contribute to both decoherence and loss. In this work, we present an experimental scheme capable of significantly mitigating the adverse impact of scattering in quantum systems. Our quantum system is composed of a two-mode squeezed light generated with the four-wave-mixing process in hot rubidium vapor and a scatterer is introduced to one of the two modes. An integrating sphere is then placed after the scatterer to recollect the scattered photons. We use mutual information between the two modes as the measure of quantum correlations and demonstrate a 47.5% mutual information recovery from scattering, despite an enormous photon loss of greater than 85%. Our scheme is the very first step toward recovering quantum correlations from disruptive random processes and thus has the potential to bridge the gap between proof-of-principle demonstrations and practical real-world implementations of quantum protocols. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Role of the isovector spin-orbit potential in mitigating the CREX-PREX dilemma

Pioneering electroweak measurements of the neutron skin thickness in lead-208 and calcium-48 are challenging our understanding of nuclear dynamics. Many theoretical models suggest that the slope of the symmetry energy controls the development of a neutron skin in neutron-rich nuclei. This led to the expectation that if lead-208 exhibits a large neutron skin, calcium-48 should as well. Given that the PREX Collaboration reported a relatively thick neutron skin in lead, we anticipated that calcium would also have a significant neutron skin. Instead, the CREX Collaboration reported a thin neutron skin in calcium. Although many suggestions have been proposed, the “CREX-PREX dilemma” remains unsolved. Recently, an intriguing scenario has emerged, suggesting that an enhanced isovector spin-orbit interaction could simultaneously account for both results. Following this approach, we performed relativistic mean-field calculations with an increased isovector spin-orbit potential. Our findings indicate that, while this modification significantly affects the structure of calcium-48, it has only a marginal impact on lead-208, thereby bringing the results into better agreement with experiment. Furthermore, the strong enhancement required to mitigate the CREX-PREX dilemma destroys the agreement with a successful spin-orbit phenomenology, primarily by modifying the well-known ordering of spin-orbit partners.

Neutron skin thickness↗

DOE OSTI2025

Seasonal Reconfiguration of Electrical Distribution Systems to Mitigate the Impact of Electric Vehicle Charging

Power grids face challenges in their infrastructure related to the integration of electric vehicles (EV). In particular, EV charging stations may induce instability in key system parameters such as substantial voltage drops, active power losses, and transformer overload due to high demand during charging periods. This article presents a seasonal reconfiguration strategy based on the differential evolution (DE) algorithm, aimed at enhancing system performance under highly variable and stochastic load profiles, particularly those driven by EV charging. The DEA algorithm is hybridized with the find-union (FU) algorithm to efficiently ensure network radiality throughout the optimization process. The proposed methodology is validated on a hybrid distribution system composed of the IEEE 33-bus network, a modified IEEE 13-bus system, and a specific 13-bus microgrid. Results have demonstrated that seasonal reconfiguration significantly reduces active power losses and mitigates transformer loading during critical demand hours, thereby quantifiably increasing the system’s performance. As an integral component of the proposed approach, an analysis of CO2 emissions associated with energy losses is included, allowing a contextualized assessment of the environmental benefits of seasonal reconfiguration in various geographical areas.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

A Sequential Model Predictive and Deep Reinforcement Learning-Based Controller for Distribution System Outage Mitigation under Hurricane Events

This paper proposes a proactive outage mitigation framework for power distribution networks to withstand hurricane-induced disruptions. It leverages Model Predictive Control (MPC) to identify safe lines for proactive switching during hurricanes, minimizing the risk of cascading failures and voltage violations. The switching strategies optimized by MPC are sequentially integrated with a Deep Reinforcement Learning agent using the Advantage Actor-Critic algorithm, enabling dynamic line switching to maximize connected buses and minimize voltage violations in real time. Using a probabilistic hurricane model, the framework predicts line failures and adapts to varying conditions to enhance grid resilience. Simulations on the IEEE 123-bus system demonstrate its effectiveness in maintaining high connectivity and minimizing disruptions. Real-time testing with an RTDS confirms the practicality and reliability of the proposed approach.

Selim, Alaa [University of Connecticut]↗

DOE OSTI2025

Mitigation of Boiling-Induced Thermal Degradation Using Microporous Nickel Inverse Opals Structures

Engineered microporous structures have received much attention in high-heat-flux electronics cooling due to their high thermal conductivity and permeability, and large surface area for heat transfer, but are susceptible to boiling-induced thermal degradation. Here, this study investigates the efficacy of nickel inverse opals (NiIOs) in mitigating structural degradation caused by corrosion-assisted erosion during pool boiling with water as the working fluid. First, we compared the reliability of NiIOs to copper inverse opals (CuIOs) for a 3-day pool boiling test at constant heat flux. The NiIOs demonstrated superior resistance to thermal degradation due to their inherent corrosion resistance and mechanical strength. Subsequently, we conducted a more controlled experiment to show the effect of heat flux on the degradation of the NiIOs while excluding the effect of temperature variations. Pool boiling tests of 20-μm-thickness NiIOs covering an area of ∼11 × 11 mm 2 with a 2.5 × 2.5 mm 2 heater at the center were conducted at heat flux levels of 20%, 40%, and 60% of the critical heat flux (CHF) for 3 days. The NiIOs subjected to heat flux levels of 20% and 40% CHF showed minimal degradation, while the sample subjected to 60% CHF showed erosion on the top surface due to higher bubble formation and departure rate. These results show the potential of NiIOs as a promising solution for long-term thermal management in high-power electronic devices, although design considerations for maximum allowable heat flux are necessary for reliable operation.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Mitigation of Pitting on Nitrogen-Doped Niobium Surfaces through Two-Step Electropolishing

Surface processing of niobium superconducting RF cavities used in particle accelerators is crucial for determining their performance. The nitrogen (N)-doping method, developed at Fermilab, enhances the cavities’ quality factor by doping the cavity’s interior surface with N atoms at 800 °C. After N-doping, electropolishing (EP) is employed to remove the niobium nitride phases and attain the desired concentration of interstitial N atoms. This study explores the effects of EP conditions on N-doped niobium samples. The samples were electropolished at 5 to 40 °C and voltages between 3 and 22 V. Different EP voltage ranges produced two types of pits, classified based on their sizes. Larger pits, linked to higher voltages, are also affected by the reaction rate. Pitting could be reduced at a lower temperature and adequate EP voltage. However, to effectively minimize gas evolution forming large-sized pitting, peeling off the top nitride layer before EP might be beneficial. This was achieved through precise low-voltage etching, forming a two-step EP process. The process involves removing the top 100–200 nm layer at a low voltage, followed by standard EP to remove the required material. This two-step EP process effectively mitigated the pitting risk associated with gas evolution on N-doped surfaces.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Optimal Mitigation Planning For Adversarial Scenarios

We propose a generalized framework which performs an optimal partitioning of a limited budget into various organizational sectors in order to improve the cybersecurity of a smart device or component in the Cyber Physical Energy System (CPS). The framework identifies the adversarial threats and possible attack sequences which can be performed to exploit cyber vulnerabilities of the component. Thereafter, we formulate an Mixed Integer Linear Programming (MILP) optimization problem which aims to evaluate the optimal budget partitions in order to minimize the number of highly likely attack sequences. Though we provide results for using the framework in CPES, the proposed methodology can be extended for multiple domains with a set of known adversarial and mitigation actions.

Purohit, Sumit [Pacific Northwest National Laborat↗

DOE OSTI2024

Mitigative Strategies for Recovering From Large Language Model Trust Violations

In this study, we investigated strategies to address trust issues arising from errors in large language models (LLMs). The study examined the impact of confidence scores, system capability explanations, and user feedback on trust restoration post-error. 68 participants viewed the responses of an LLM to 20 general trivia questions, with an error introduced on the third trial. Each participant was presented with one mitigation strategy. Participants rated their overall trust in the model and the reliability of the answer. Results showed an immediate drop in trust after the error; however, there were no differences across the three strategies in trust recovery. All conditions had a logarithmic trend in trust recovery following error. Differences in overall trust were predicted by perceived reliability of the answer, suggesting that participants were evaluating results critically and using that to inform their trust in the model. Qualitative data supported this finding; participants expressed lasting distrust despite the LLM’s later accuracy. Results showcase the need to prioritize accuracy in LLM deployment, because early errors may irrevocably damage user trust calibration and later adoption.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

TRANSFER LEARNING FOR FIELD EMISSION MITIGATION IN CEBAF SRF CAVITIES

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab operates hundreds of super-conducting radio frequency (SRF) cavities in its two linear accelerators (linacs). Field emission (FE) is an ongoing operational challenge in higher gradient SRF cavities. FE generates high levels of neutron and gamma radiation leading to damaged accelerator hardware and a radiation hazard environment. During machine development periods, we performed gradient scans to record data capturing the relationship between cavity gradients and radiation levels measured throughout the linacs. However, the field emission environment at CEBAF varies considerably over time as the configuration of the radio frequency (RF) gradients changes and due to the changing behaviour of field emitters. An artificial intelligence/machine learning (AI/ML) approach with transfer learning could be a valuable tool to mitigate FE and lower the radiation levels. In this work, we mainly focus on leveraging the RF trip data gathered during CEBAF operations. We develop a transfer learning-based surrogate model for radiation detector readings given RF cavity gradients to track the CEBAF?s changing configuration and environment. Then, we could use the developed model as an optimization process for redistributing the RF gradients within a linac to minimize radiation levels.

Ahammed, K.↗

DOE OSTI2024

Strategies for mitigating residual magnetic field effect on pre-production PIP-II SSR2 cryomodule performance

This paper outlines the strategy aimed at mitigating the adverse effects of residual magnetic fields on the performance of pre-production SSR2 superconducting cavities within the context of the PIP-II project at Fermilab. Residual magnetic fields can significantly impact cavity performance, leading to reduced quality factor. To address this challenge, our strategy integrates various approaches including magnetic shielding, careful selection of materials, quality controls aimed at measuring magnetic permeability, magnetic hygiene to reduce residual magnetic field at the installation phase. Additionally, experimental studies are being planned to analyze the behavior of the cavities under different magnetic field conditions, and the effectiveness of advanced demagnetization procedures.

43 PARTICLE ACCELERATORS↗

DOE OSTI2026

Mitigating Transition in the Fermilab Booster Using a Triple Phase Jump

The PIP-II project will significantly enhance neutrino production for DUNE, Fermilab’s flagship long-baseline neutrino oscillation experiment, by doubling the beam power delivered by the accelerator complex. To achieve this, the total charge injected from the new PIP-II linac into the Booster rapid cycling synchrotron (RCS) will increase from $4.5\times 10^{12}$ to $6.5\times 10^{12}$ protons per pulse. Simultaneously, the Booster’s ramp rate will rise from 15 to 20 Hz, while the injection energy will go from 400 MeV to 800 MeV. The Booster accelerates the beam to 8 GeV and crosses transition at $\gamma_t=5.45$. In current operations, no dedicated $\gamma_t$ jump system is employed; instead, longitudinal emittance growth is controlled via an active quadrupole-mode feedback system. Downstream, the Recycler Ring uses slip-stacking to accumulate beam and increase bunch intensity. However, this process imposes a constraint on the longitudinal emittance at Booster extraction, limiting it to 0.1 eV-s (95\%) to avoid excessive particle loss. Because collective effects scale with intensity, additional measures to mitigate transition crossing may be required to stay below this limit. One potential approach is the so-called triple phase-jump technique. While the method has known limitations, it offers some advantages: it can be implemented using the existing digital low-level RF (LLRF) system, requires no additional magnets or pulsed power supplies, and remains compatible with quadrupole-mode feedback.

Ostiguy, J.-F. [Fermilab] (ORCID:0000000290883681)↗

DOE OSTI2024

TRANSFER LEARNING FOR FIELD EMISSION MITIGATION IN CEBAF SRF CAVITIES

Ahammed, K.↗

DOE OSTI2024

PLC Vulnerabilities and Mitigations

Programmable Logic Controllers (PLCs) are used extensively in many high-importance industrial and nonindustrial settings including controlling elevators, manufacturing machines, and utility facilities such as power and natural gas, however cybersecurity protection for them has been neglected. Within recent years, PLCs have been put under more security scrutiny and experts have advocated for changes from the addition of protocol encryption and network segmentation to intrusion detection systems on the PLCs themselves. While PLC security is critical, a large portion of the PLCs available today will never receive these changes due to being legacy or the difficulty of overhauling the security on existing systems. Due to the infeasibility of applying many recommended security measures towards currently available machines, we aim to provide realistic and affordable best practices for hardening PLCs. We will first conduct security analysis and consider attack vectors within our target PLC. Once we’ve analyzed the device’s security, we will evaluate a variety of mitigation methods and create guidelines to effectively reduce the threat posed by PLC attacks with minimal disruption to operations.

42 ENGINEERING↗