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DOE OSTI2025

Model-agnostic search for dijet resonances with anomalous jet substructure in proton–proton collisions at $\sqrt{s}$ = 13 TeV

This paper presents a model-agnostic search for narrow resonances in the dijet final state in the mass range 1.8-6 TeV. The signal is assumed to produce jets with substructure atypical of jets initiated by light quarks or gluons, with minimal additional assumptions. Search regions are obtained by utilizing multivariate machine-learning methods to select jets with anomalous substructure. A collection of complementary anomaly detection methods - based on unsupervised, weakly supervised, and semisupervised algorithms - are used in order to maximize the sensitivity to unknown new physics signatures. These algorithms are applied to data corresponding to an integrated luminosity of 138 fb -1 , recorded by the CMS experiment at the LHC, at a center-of-mass energy of 13 TeV. No significant excesses above background expectations are seen. Exclusion limits are derived on the production cross section of benchmark signal models varying in resonance mass, jet mass, and jet substructure. Many of these signatures have not been previously sought, making several of the limits reported on the corresponding benchmark models the first ever. When compared to benchmark inclusive and substructure-based search strategies, the anomaly detection methods are found to significantly enhance the sensitivity to a variety of models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Downscaled CMIP5 projections of physical fire risk understate historical trends

Reliable projections of wildfire risk are important for multi-sector impacts analysis. Statistically downscaled and bias-corrected Earth system model ensemble products are routinely used to analyze regional physical wildfire risk, but evaluations of historical observed trends and variability are lacking. Here, we evaluate physical fire risk over the western United States using the Canadian Forest Fire Weather Index (FWI) by comparing model outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), statistically downscaled via the Multivariate Adaptive Constructed Analogs (MACA) approach, against the observational target dataset gridMET, a gridded high-resolution surface meteorological product. We analyze multidecadal trends and interannual variability in seasonal average FWI for the historical period and future projections under two emissions scenarios, and we compare MACA-CMIP5 ensemble results with a simple time series model that generates historical and future projections of seasonal FWI based on bootstrapping observed historical trends and variability. Our findings indicate that MACA-CMIP5 accurately captures the magnitude and spatial patterns of seasonally averaged FWI but tends to underestimate historical decadal trends. We show that future increases in fire risk may be underestimated relative to the simple time series model that projects historical variability into the future. We also highlight that model biases in relative humidity contribute significantly to model-data differences. Our results underscore the importance of historical hindcasting exercises for informing broader multi-sector applications.

FWI↗

DOE OSTI2024

Real-time confinement regime detection in fusion plasmas with convolutional neural networks and high-bandwidth edge fluctuation measurements

Abstract A real-time detection of the plasma confinement regime can enable new advanced plasma control capabilities for both the access to and sustainment of enhanced confinement regimes in fusion devices. For example, a real-time indication of the confinement regime can facilitate transition to the high-performing wide-pedestal (WP) quiescent H-mode, or avoid unwanted transitions to lower confinement regimes that may induce plasma termination. To demonstrate real-time confinement regime detection, we use the 2D beam emission spectroscopy (BES) diagnostic system to capture localized density fluctuations of long wavelength turbulent modes in the edge region at a 1 MHz sampling rate. BES data from 330 discharges in either L-mode, H-mode, quiescent H (QH)-mode, or WP QH-mode were collected from the DIII-D tokamak and curated to develop a high-quality database to train a deep-learning classification model for real-time confinement detection. We utilize the 6×8 spatial configuration with a time window of 1024 µ s and recast the input to obtain spectral-like features via fast Fourier transform preprocessing. We employ a shallow 3D convolutional neural network for the multivariate time-series classification task and utilize a softmax in the final dense layer to retrieve a probability distribution over the different confinement regimes. Our model classifies the global confinement state on 44 unseen test discharges with an average F 1 score of 0.94, using only ∼1 ms snippets of BES data at a time. This activity demonstrates the feasibility for real-time data analysis of fluctuation diagnostics in future devices such as ITER, where the need for reliable and advanced plasma control is urgent.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography

Chemical and conformational changes underlie the functional cycles of proteins. Comparative crystallography can reveal these changes over time, over ligands, and over chemical and physical perturbations in atomic detail. A key difficulty, however, is that the resulting observations must be placed on the same scale by correcting for experimental factors. We recently introduced a Bayesian framework for correcting (scaling) X-ray diffraction data by combining deep learning with statistical priors informed by crystallographic theory. To scale comparative crystallography data, we here combine this framework with a multivariate statistical theory of comparative crystallography. By doing so, we find strong improvements in the detection of protein dynamics, element-specific anomalous signal, and the binding of drug fragments.

Hekstra, Doeke R. [Harvard Univ., Cambridge, MA (U↗

DOE OSTI2026

Highly boosted dielectron identification in proton-proton collisions at $\sqrt{s}$ = 13 TeV

A new technique is developed to identify dielectrons (e$^+$e$^-$) with Lorentz boost $γ_\mathrm{L}$$\gt$ 20 that produce one single merged cluster in the electromagnetic calorimeter of the CMS detector. The identification uses two multivariate models: one for the case where both electron tracks are reconstructed, and another where only one of the tracks is reconstructed. The efficiency is determined using proton-proton collision data collected at a center-of-mass energy of 13 TeV. Boosted J/$ψ$ mesons decaying into e$^+$e$^-$ pairs are used to estimate the efficiency of the model with two tracks, yielding an overall efficiency of 80%. The Z $\to$ $μ^+μ^-γ$ events, where the photon converts into a collimated dielectron, are used for the model with a single track, yielding an efficiency of about 60%. A dedicated energy correction for dielectron candidates is also developed using B$^\pm$ $\to$ J/$ψ$K$^\pm$ $\to$ e$^+$e$^-$K$^\pm$ data.

Hayrapetyan, A. [Yerevan Phys. Inst.]↗

DOE OSTI2024

Quantum error mitigation by layerwise Richardson extrapolation

A widely used method for mitigating errors in noisy quantum computers is Richardson extrapolation, a technique in which the overall effect of noise on the estimation of quantum expectation values is captured by a single parameter that, after being scaled to larger values, is eventually extrapolated to the zero-noise limit. We generalize this approach by introducing layerwise Richardson extrapolation (LRE), an error mitigation protocol in which the noise of different individual layers (or larger chunks of the circuit) is amplified and the associated expectation values are linearly combined to estimate the zero-noise limit. The coefficients of the linear combination are analytically obtained from the theory of multivariate Lagrange interpolation. LRE leverages the flexible configurational space of layerwise unitary folding, allowing for a more nuanced mitigation of errors by treating the noise level of each layer of the quantum circuit as an independent variable. Furthermore, we provide numerical simulations demonstrating scenarios where LRE achieves superior performance compared to traditional (single-variable) Richardson extrapolation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Role of the likelihood for elastic scattering uncertainty quantification

In the last decade, uncertainty quantification (UQ) for optical model potentials (OMPs) has become a focal point for nuclear reaction theory, and several competing approaches for OMP UQ have recently been developed. Here, we clarify recent efforts to compare frequentist and Bayesian approaches in the context of OMP UQ [G. B. King et al., Phys. Rev. Lett. 122, 232502 (2019)]. We replicate a portion of that OMP UQ study but use independent statistical tools. Specifically, we compare two methods for OMP parameter inference from elastic scattering data: the Levenberg-Marquardt algorithm for χ 2 minimization on one hand and Markov chain Monte Carlo (MCMC) sampling on the other. Separately, we assess the common practice of using a renormalized likelihood (χ 2 /N), N being the number of data points, instead of the canonical weighted-least-squares likelihood (χ 2 ), as a way of accounting for unknown data correlations. Here, we show that for a generic linear model and for a five-parameter OMP analysis, frequentist and uniform-prior Bayesian approaches recover the same optimum and uncertainty estimates—not systematically larger uncertainties for the Bayesian approach, as was concluded in G. B. King et al., Phys. Rev. Lett. 122, 232502 (2019). Further, we show that if an additional, near-degenerate parameter is introduced into the same OMP analysis such that the parameter posterior becomes non-Gaussian, then covariance-based estimates of uncertainty become unreliable. Finally, we show that regardless of optimization approach, if χ 2 /N is used for the likelihood, the resulting parametric uncertainties increase by $\sqrt{N}$, and that this is responsible for the conclusions drawn in the revisited study. Based on our replication results, we find that a fortuitous cancellation of unreported errors and the renormalization factor can lead to improvement in empirical coverages, as was the case in the original comparative study. We emphasize that developing and applying a realistic likelihood function is an essential task in a UQ analysis, and that several recent UQ studies that employed a renormalized likelihood (i.e., including a 1/N factor) may have yielded unrealistically large uncertainties for elastic-scattering observables. If the parameter posterior deviates from multivariate-normal, a sampling-based approach like MCMC has a clear advantage over methods that assume the Laplace approximation holds. We note that empirical coverage can serve as an important internal check for the analyst whose model or data may have additional, unaccounted-for uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Measurement of single top-quark production in association with a W boson in pp collisions at $\sqrt{s}$ =13 TeV with the ATLAS detector

The inclusive cross section for the production of a single top quark in association with a Formula Presented boson is measured using Formula Presented of proton-proton collision data collected with the ATLAS detector at $\sqrt{s}$ =13 TeV. Events containing two charged leptons and at least one jet identified as originating from a b-quark are selected. A multivariate discriminant is constructed to separate the tW signal from the $t\bar{t}$ background. The cross section is extracted using a profile likelihood fit to the signal and control regions and it is measured to be σ tW = 75$^{+15}_{–14}$, in good agreement with the Standard Model prediction. The measured cross section is used to extract a value for the left-handed form factor at the Wtb vertex times the Cabibbo-Kobayashi-Maskawa matrix element |$f$ LV $V$ tb | of 0.97±0.10.

Cabibbo–Kobayashi–Maskawa matrix↗

DOE OSTI2024

Streamlining latent spaces in machine learning using moment pooling

Many machine learning applications involve learning a latent representation of data, which is often high-dimensional and difficult to directly interpret. In this work, we propose “moment pooling,” a natural extension of deep sets networks which drastically decreases the latent space dimensionality of these networks while maintaining or even improving performance. Moment pooling generalizes the summation in deep sets to arbitrary multivariate moments, which enables the model to achieve a much higher effective latent dimensionality for a fixed learned latent space dimension. We demonstrate moment pooling on the collider physics task of quark/gluon jet classification by extending energy flow networks (EFNs) to moment EFNs. We find that moment EFNs with latent dimensions as small as 1 perform similarly to ordinary EFNs with higher latent dimension. This small latent dimension allows for the internal representation to be directly visualized and interpreted, which in turn enables the learned internal jet representation to be extracted in closed form. Published by the American Physical Society 2024

Gambhir, Rikab (ORCID:0000000251080448)↗

DOE OSTI2024

Search for Baryon Number Violation in Top Quark Production and Decay Using Proton-Proton Collisions at $\sqrt{𝑠}$ = 13 TeV

A search is presented for baryon number violating interactions in top quark production and decay. The analysis uses data from proton-proton collisions at a center-of-mass energy of 13 TeV, collected with the CMS detector at the LHC with an integrated luminosity of 138 fb −1 . Candidate events are selected by requiring two oppositely charged leptons (electrons or muons) and exactly one jet identified as originating from a bottom quark. Multivariate discriminants are used to separate the signal from the background. No significant deviation from the standard model prediction is observed. Upper limits are placed on the strength of baryon number violating couplings. For the first time the production of single top quarks via baryon number violating interactions is studied. This allows the search to set the most stringent constraints to date on the branching fraction of the top quark decay to a lepton, an up-type quark (𝑢 or 𝑐), and a down-type quark (𝑑, 𝑠, or 𝑏). The results improve the previous bounds by 3 to 6 orders of magnitude based on the fermion flavor combination of the baryon number violating interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Dual Mechanism for Transient Capacitance Anomaly in Improper Ferroelectrics

Negative capacitance (NC) effects in ferroelectrics can potentially break fundamental limits of power dissipation known as “Boltzmann tyranny.” However, the origin of transient NC of ferroelectrics, which is attributed to two different mechanisms involving free-energy landscape and nucleation, is under intense debate. Here, we report the coexistence of transient NC and an S-shaped anomaly during the switching of ferroelectric hexagonal ferrites capacitor in an RC circuit. The early-stage NC arises from the nucleation process, while the late-stage S-shaped anomaly corresponds to a nascent NC associated with the free-energy landscape. The entire waveform can be reproduced using a hybrid model that simultaneously incorporates these two mechanisms. These results highlight the multivariable free-energy landscape of hexagonal ferrites that enables an abrupt change of the internal field and demonstrate that the two mechanisms are not mutually exclusive, resolving the long-standing debate. In conclusion, the behavior of the S-shaped anomaly also provides a pathway to extract parameters of free-energy landscape and switching dynamics.

Capacitance↗

DOE OSTI2026

Quantum Sensing of Displacements with Stabilized Gottesman-Kitaev-Preskill States

We demonstrate how recent protocols developed for the stabilization of Gottesman-Kitaev-Preskill states can be used for the estimation of two-quadrature displacement sensing, with sensitivities approaching the multivariate quantum Cramer-Rao bound. Thanks to the stabilization, this sensor is backaction evading and can function continuously without reset, making it well suited for the detection of itinerant signals. Additionally, we provide numerical simulations showing that the protocol can unconditionally surpass the Gaussian limit of displacement sensing with prior information, even in the presence of realistic noise. Our work shows how reservoir engineering in bosonic systems can be leveraged for quantum metrology, with potential applications in force sensing, waveform estimation, and quantum channel learning.

Labarca, Lautaro [Univ. of Sherbrooke, QC (Canada)↗

DOE OSTI2026

Search for Light Pseudoscalar Bosons, Pair-Produced in Higgs Boson Decays in the Four-Electron Final State in Proton-Proton Collisions at $\sqrt{s}=13$ TeV

A search for pairs of light neutral pseudoscalar bosons (𝐴) resulting from the decay of a Higgs boson is performed. The search is conducted using LHC proton-proton collision data at $\sqrt{s}=13$ TeV, collected with the CMS detector in 2016–2018 and corresponding to an integrated luminosity of 138 fb −1 . The 𝐴 boson decays into a highly collimated electron-positron pair. A novel multivariate algorithm using tracks and calorimeter information is developed to identify these distinctive signatures, and events are selected with two such merged electron-positron pairs. No significant excess above the standard model background predictions is observed. Upper limits on the branching fraction for 𝐻 → 𝐴⁢𝐴 → 4⁢𝑒 are set at 95% confidence level, for masses between 10 and 100 MeV and proper decay lengths below 100 μ⁢m, reaching branching fraction sensitivities as low as 10 −5 . This is the first search for Higgs boson decays to four electrons via light pseudoscalars at the LHC. It significantly improves the experimental sensitivity to axionlike particles with masses below 100 MeV.

Hayrapetyan, A. [Yerevan Physics Institute]↗

DOE OSTI2026

Search for Higgsinos in final states with low-momentum lepton-track pairs at 13 TeV

We present a search for the pair production of Higgsinos in final states with large missing transverse momentum and either two reconstructed muons or a reconstructed lepton (muon or electron) and an isolated track. The analyzed data correspond to proton-proton collisions with an integrated luminosity of 137 fb −1 , collected by the CMS experiment at $\sqrt{𝑠}$ =13 TeV in 2016, 2017, and 2018. The signal scenario assumes four nearly mass degenerate Higgsino mass eigenstates: two neutralino states $\tilde{𝜒}^0_2$ and $\tilde{𝜒}^0_1$ with a small mass difference in the range 1–10 GeV and two chargino states $\tilde{𝜒}^±_1$ with an intermediate mass. The analysis focuses on the decay of the heavier neutralino into the lighter one and a virtual 𝑍 boson, which decays into two same-flavor leptons. The leptons have small transverse momentum and/or a small opening angle between the identified muons. An isolated track is used to recover events in which only one of the two leptons is identified. Multivariate discriminants are used to enhance the sensitivity by efficiently rejecting backgrounds from SM processes or misreconstructed tracks and/or leptons. The search explores a unique phase space and probes a previously unexplored region of the signal model parameter space. Mass differences between the two neutralinos are probed down to 1.5 GeV, assuming a Higgsino mass of 100 GeV. The maximum excluded Higgsino mass is 115 GeV.

Hayrapetyan, A. [Yerevan Physics Institute]↗

DOE OSTI2025

Search for displaced leptons in $\sqrt{𝑠}$ =13 TeV and 13.6 TeV 𝑝⁢𝑝 collisions with the ATLAS detector

A search for leptons displaced from the primary vertex is performed with the ATLAS detector at the Large Hadron Collider. The search includes the full proton-proton collision dataset collected during Run 2 at $\sqrt{𝑠}$ = 13 TeV and a partial dataset collected during Run 3 in 2022–2023 at $\sqrt{𝑠}$ = 13.6 TeV, corresponding to integrated luminosities of 140 fb −1 and 56.3 fb −1 , respectively. Final states with displaced electrons or muons are considered, and novel triggers introduced in Run 3 are employed that use large impact parameter tracking to reconstruct displaced tracks with low momentum. In addition, photon reconstruction and multivariate techniques are employed to broaden the sensitivity to channels with large background rates or highly displaced electrons, respectively. The results are consistent with the Standard Model background expectations and are used to set model-independent limits on the production of displaced electrons and muons. The analysis is also interpreted in the context of a gauge-mediated supersymmetry breaking model with pair-produced long-lived sleptons and a dark sector model with pair-produced chargino-like states. The results include 95% confidence level exclusions of selectrons with lifetimes from 4 ps to 60 ns and a mass of 150 GeV, and exclusions of selectrons, smuons, and staus with a lifetime of 0.3 ns for masses up to 740, 830, and 440 GeV, respectively. Dark charginos with masses up to 380 GeV are excluded for a mass difference with the neutral state of 40 GeV, and mass differences down to 17 GeV are excluded for dark charginos with a 100 GeV mass.

supersymmetric models↗

DOE OSTI2024

Automated Framework for Groundwater Monitoring Using DWT with LSTM and Transformers

Environmental monitoring is critical for safeguarding public health and ecological well-being. Traditional data structuring and workflow monitoring methods consume significant time and effort, hindering timely insights and effective decision-making. Our study addresses this challenge by presenting an AI framework that automates data cleaning, structuring, and modeling processes, specifically targeting applications in groundwater monitoring. By leveraging automation for data processing and model training, our framework establishes a novel and efficient paradigm for environmental monitoring, with its potential application to the vast network of over a hundred Department of Energy Environmental Management (DoE-EM) cleanup sites across the country. It analyzes data streams from a network of groundwater Internet-of-Things (IoT) sensors deployed at the Savannah River Site (SRS) for prediction modeling. This allows human experts to focus on analysis and decision-making, ultimately leading to better environmental outcomes.The framework employs multivariate time-series forecasting methods to study and model the behavior of varying chemical analytes. The continuous learning process is enabled by utilizing deep learning techniques. It allows the framework to become more nuanced in its analysis over time, adapting to the specific characteristics of the environmental site and the evolving nature of contaminant behavior. Deep learning models known for sequence modeling, LSTM, and Transformers are employed for time series forecasting. Data processing and structuring are essential components significantly impacting the final model's performance. This hypothesis was proven by presenting a comparative analysis of model performance with processed and unprocessed data. The feature engineering approach utilized was the Discrete Wavelet Transform, which works well with time series data.

Discrete Wavelet Transform (DWT)↗

DOE OSTI2024

Probing Signal-Based Inertia and Frequency Response Estimation for Power Systems With High Levels of Inverter-Based Resources

Power system inertia is the inherent capability of a power system to resist changes in its frequency during disturbances. Real-time inertia estimation technology has become more important due to the low-inertia issues caused by the increasing integration levels of inverter-based resources (IBRs) from renewable energy; however, existing inertia estimation methods hardly consider multiple frequency response controls that act within the same time frame as conventional inertial response, thus making measured inertia values vary under different testing conditions. To resolve this issue, this paper proposes a novel real-time estimation method to simultaneously estimate a power system's inertia constant and frequency response droop constant using a well-designed probing signal. First, we formulate the inertia and frequency response model of a power system with IBRs. Second, through the integration and manipulation of the developed model, we propose a multivariate linear regression-based estimation method that is resilient to measurement noise. Third, we design a probing signal that can be injected by IBRs to incite the required transients for estimation. Finally, we validate the proposed estimation method through comprehensive power-hardware-in-the-loop experiments using inverter hardware and a realistic island power system model. The results demonstrate that the proposed method can accurately estimate the inertia and droop value of the power system with grid-following IBRs and grid-forming IBRs with virtual synchronous machine control.

frequency response↗

DOE OSTI2024

Establishing reference ranges for circulating biomarkers of drug‐induced liver injury in healthy human volunteers 1

Aims The potential of mechanistic biomarkers to improve prediction of drug‐induced liver injury (DILI) and hepatic regeneration is widely acknowledged. We sought to determine reference intervals for new biomarkers of DILI and regeneration, as well as to characterize their natural variability and impact of diurnal variation. Methods Serum samples from 227 healthy volunteers were recruited as part of a cross‐sectional study; of these, 25 subjects had weekly serial sampling over 3 weeks, while 23 had intensive blood sampling over a 24h period. Alanine aminotransferase (ALT), MicroRNA‐122 (miR‐122), High Mobility Group Box‐1 (HMGB1), total Keratin‐18 (K18), caspase‐cleaved Keratin‐18 (ccK18), Glutamate Dehydrogenase (GLDH) and Macrophage Colony‐Stimulating Factor‐1 (CSF‐1) were assayed. Results Reference intervals were established for each biomarker based on the 97.5% quantile (90% CI) following the assessment of fixed effects in univariate and multivariable models. Intra‐individual variability was found to be non‐significant, and there was no significant impact of diurnal variation. Conclusion Reference intervals for novel DILI biomarkers have been described. An upper limit of a reference range might represent the most appropriate mechanism to utilize these data. These data can now be used to interpret data from exploratory clinical DILI studies and to assist their further qualification as required by regulatory authorities.

Jorgensen, Andrea L. [Department of Health Data Sc↗