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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Constraints on fifth forces and ultralight dark matter from OSIRIS-REx target asteroid Bennu

It is important to test the possible existence of fifth forces, as ultralight bosons that would mediate these are predicted to exist in several well-motivated extensions of the Standard Model. Recent work indicated asteroids as promising probes, but applications to real data are lacking so far. Here we use the OSIRIS-REx mission and ground-based tracking data for the asteroid Bennu to derive constraints on fifth forces. Our limits are strongest for mediator masses m ~ (10 -18 -10 -17 ) eV, where we currently achieve the tightest bounds. These can be translated to a wide class of models leading to Yukawa-type fifth forces, and we demonstrate how they apply to U(1) B dark photons and baryon-coupled scalars. Our results demonstrate the potential of asteroid tracking in probing well-motivated extensions of the Standard Model and ultralight bosons near the fuzzy dark matter range.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

How data science methods can improve the quality and efficiency of ICF and HEDP research

Data Science methods (many that are Bayesian based) are widely used in the physical sciences to estimate model parameters from experimental data, synthesize heterogeneous data, calibrate models, design experiments, and determine statistical significance of data. These methods provide a wealth of advantages over traditional analysis techniques because: 1) uncertainties are rigorously defined and propagated naturally through complex systems including covariance, 2) prior information is captured within the analysis framework (including rad-MHD and rad-hydro simulations), 3) competing models can be selected and/or ruled out using quantitative criteria, and 4) complex, heterogeneous data can be incorporated simultaneously. While these methods have been widely adopted as the gold standard in fields such as particle physics, astronomy, and biology, they have been slow to catch on in Inertial Confinement Fusion (ICF) and High Energy Density Physics (HEDP) research. Recently, several teams at LLNL, SNL, LANL, and the LLE have been exploring the use of these tools in their research and have found success. Here we propose that a concerted effort to consolidate these independent research efforts by developing and deploying common tools for use across the complex can revolutionize the way we approach data analysis, assimilation of theory and experiment, and decision making. The Bayesian formalism provides a means to accomplish this, but we are lacking certain infrastructure to make it happen on a large scale. Furthermore, once adopted, these techniques can be used to develop standards by which discoveries can be judged, similar to the so-called 5σ rule in high energy particle physics. Such standards may be used in the future to address the issue of unknown reproducibility in ICF and HED experiments caused by low shot rate and high cost per experiment. Our goals as a group are to advance the state of the art in HED measurement science by enabling: 1) better inferences from data with well-defined uncertainties, 2) better use of the data we have and continue to collect, 3) intelligent synthesis of data, 4) evaluation of the statistical significance of our data, and 5) informed decision making regarding the design of new experiments and instruments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Search for single vector-like $B$ quark production and decay via $B$ → $bH$($b\overline{b}$) in $pp$ collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

A search is presented for single production of a vector-like B quark decaying into a Standard Model b-quark and a Standard Model Higgs boson, which decays into a $b\overline{b}$ pair. The search is carried out in 139 fb -1 of $\sqrt{s}$ = 13 TeV proton-proton collision data collected by the ATLAS detector at the LHC between 2015 and 2018. No significant deviation from the Standard Model background prediction is observed, and mass-dependent exclusion limits at the 95% confidence level are set on the resonance production cross-section in several theoretical scenarios determined by the couplings c W , c Z and c H between the B quark and the Standard Model W, Z and Higgs bosons, respectively. For a vector-like B occurring as an isospin singlet, the search excludes values of c W greater than 0.45 for a B resonance mass (m B ) between 1.0 and 1.2 TeV. For 1.2 TeV < m B < 2.0 TeV, c W values larger than 0.50–0.65 are excluded. If the B occurs as part of a (B, Y) doublet, the smallest excluded c Z coupling values range between 0.3 and 0.5 across the investigated resonance mass range 1.0 TeV < m B < 2.0 TeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

SLAB: simultaneous labeling and binding affinity prediction for protein–ligand structures

Machine learning models are often used as scoring functions to predict the binding affinity of a protein–ligand complex. These models are trained with limited amounts of data with experimentally measured binding affinity values. A large number of compounds are labeled inactive through single-concentration screens without measuring binding affinities. These inactive compounds, along with the active ones, can be used to train binary classification models, while regression models are trained using compounds with binding affinities only. However, the classification and regression tasks are often handled separately, without sharing the learned feature representations. In this paper, we propose a novel model architecture that jointly performs regression and classification objectives, aiming to maximize data utilization and improve predictive performance by leveraging two complementary tasks. In our setup, the regression yields the binding affinity, whereas the classification task yields the label as active or inactive. We demonstrate our method using PDBbind, the standard 3D structure database, as well as a dataset of flavivirus protease compounds with binding affinity data. Our experiments show that the new joint training strategy improves the accuracy of the model, increasing applicability in various practical drug screening scenarios.

Biological and medical sciences↗
DOE OSTI2023

Status of DUNE Offline Computing

We summarize the status of Deep Underground Neutrino Experiment (DUNE) Offline Software and Computing program. We describe plans for the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE experiment and its prototypes in pursuit of the experiment's physics goals of precision measurements of neutrino oscillation parameters, detection of astrophysical neutrinos, measurement of neutrino interaction properties and searches for physics beyond the Standard Model. In contrast to traditional HEP computational problems, DUNE's Liquid Argon Time Projection Chamber data consist of simple but very large (many GB) data objects which share many characteristics with astrophysical images. We have successfully reconstructed and simulated data from 4% prototype detector runs at CERN. The data volume from the full DUNE detector, when it starts commissioning late in this decade will present memory management challenges in conventional processing but significant opportunities to use advances in machine learning and pattern recognition as a frontier user of High Performance Computing facilities capable of massively parallel processing. Our goal is to develop infrastructure resources that are flexible and accessible enough to support creative software solutions as HEP computing evolves.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

An improved dataset for predicting mammal infecting viruses from genetic sequence information

There have been several attempts to develop machine learning (ML) models to identify human infecting viruses from their genomic sequences, with varying degrees of success. Direct comparison between models is problematic, because these models are typically trained and evaluated on different datasets with alternative data splitting schemes, features, and model performance metrics. In this paper we present a standardized dataset of mammal infecting and non-infecting viral pathogens, refined from the previous work of Mollentze et al. to include the latest literature evidence, roughly doubling the number of curated host-virus records available to the community, and new host target labels, primate and mammal. The new host labels were included for several reasons, including previous reports that classification performance is better at broader taxonomic ranks and the idea that there may be more data for primate infection that might serve as a suitable proxy for zoonotic potential and avoidance of false positives for human infection due to absence of evidence. On this dataset, we report the performance of eight machine learning models for predicting mammal-infecting viruses from their genomic sequences. We find that randomly assigning cases in our improved dataset to training/testing sets, when compared to the original assignments into training/testing in Mollentze et al., increases the overall average ROC AUC of prediction of human infection from 0.663 ± 0.070 to 0.784 ± 0.013, consistent with the reduction in phylogenetic distance between train and test sets (relative entropy change from 3.00 to 0.08). The broadest host category of mammal infection can be predicted most reliably at 0.850 ± 0.020. We share our improved dataset and code to enable standardized comparisons of machine learning methods to predict human host infections. Overall, we have presented preliminary evidence that classification of virus host infection is more tractable at higher taxonomic ranks, that unsurprisingly reducing the phylogenetic distance between training and test sets can improve predictive performance, that peptide kmer features appear to be harmful to out of sample model performance, and we are left with the question of whether models for virus host prediction can reasonably be expected to perform well in out of sample scenarios given the likelihood that viruses do not share a common ancestor. Consistent with this concern, when the data is resampled such that there is no overlap between viral families in training and test sets (relative entropy > 24), models perform no better than random chance at prediction of human infection regardless of whether kmers are included (ROC AUC 0.50 ± 0.08) or not (ROC AUC 0.50 ± 0.04).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Improved Acquisition and Reconstruction for Wavelength-Resolved Neutron Tomography

Wavelength-resolved neutron tomography (WRNT) is an emerging technique for characterizing samples relevant to the materials sciences in 3D. WRNT studies can be carried out at beam lines in spallation neutron or reactor-based user facilities. Because of the limited availability of experimental time, potential imperfections in the neutron source, or constraints placed on the acquisition time by the type of sample, the data can be extremely noisy resulting in tomographic reconstructions with significant artifacts when standard reconstruction algorithms are used. Furthermore, making a full tomographic measurement even with a low signal-to-noise ratio can take several days, resulting in a long wait time before the user can receive feedback from the experiment when traditional acquisition protocols are used. In this paper, we propose an interlaced scanning technique and combine it with a model-based image reconstruction algorithm to produce high-quality WRNT reconstructions concurrent with the measurements being made. The interlaced scan is designed to acquire data so that successive measurements are more diverse in contrast to typical sequential scanning protocols. The model-based reconstruction algorithm combines a data-fidelity term with a regularization term to formulate the wavelength-resolved reconstruction as minimizing a high-dimensional cost-function. Using an experimental dataset of a magnetite sample acquired over a span of about two days, we demonstrate that our technique can produce high-quality reconstructions even during the experiment compared to traditional acquisition and reconstruction techniques. In summary, the combination of the proposed acquisition strategy with an advanced reconstruction algorithm provides a novel guideline for designing WRNT systems at user facilities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

On detrending stream velocity time series for robust tidal flow turbulence characterization

We investigated the impact of detrending techniques on turbulence quantities from tidal stream flow data, focusing on the autocorrelation function, $ρ_{uu}$, and velocity spectrum, $Φ(f)$. Standard detrending methods, including high-pass frequency-based and polynomial-based techniques, are examined, alongside a proposed alternative method, the empirical mode decomposition (EMD). Our results highlight that intervals of flow acceleration and deceleration, typical in tidal and riverine flows, significantly affect the estimation of turbulence quantities using high-pass frequency filtering and polynomial detrending of varying orders. These methods can strongly influence $ρ_{uu}$ and $Φ(f)$, thereby affecting the accurate estimation of derived quantities. We examine two variations of detrending data using EMD; the first removes only the EMD residue, and the second removes both the residue and the largest scale intrinsic mode function (IMF). By comparing the detrended spectra with the modeled von Kármán spectra, we demonstrate that the second variation (i.e., removing the residue and the largest scale IMF) successfully removed the large-scale trend of the data while retaining the energy of other scales.

Detrend↗
DOE OSTI2024

Search for long-lived particles decaying in the CMS muon detectors in proton-proton collisions at s = 13 TeV

A search for long-lived particles (LLPs) decaying in the CMS muon detectors is presented. A data sample of proton-proton collisions at s = 13 TeV corresponding to an integrated luminosity of 138 fb − 1 , recorded at the LHC in 2016–2018, is used. The decays of LLPs are reconstructed as high multiplicity clusters of hits in the muon detectors. In the context of twin Higgs models, the search is sensitive to LLP masses from 0.4 to 55 GeV and a broad range of LLP decay modes, including decays to hadrons, τ leptons, electrons, or photons. No excess of events above the standard model background is observed. The most stringent limits to date from LHC data are set on the branching fraction of the Higgs boson decay to a pair of LLPs with masses below 10 GeV. This search also provides the best limits for various intervals of LLP proper decay length and mass. Finally, this search sets the first limits at the LHC on a dark quantum chromodynamic sector whose particles couple to the Higgs boson through gluon, Higgs boson, photon, vector, and dark-photon portals, and is sensitive to branching fractions of the Higgs boson to dark quarks as low as 2 × 10 − 3 . © 2024 CERN, for the CMS Collaboration 2024 CERN

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Searching for Neutrino Tridents in the NOvA Near Detector

This dissertation presents a search for neutrino trident production in the NOvA near detector through the coherent ``dimuon" channel: $\nu_\mu +\hspace{1pt}\text{X} \rightarrow \nu_\mu + \mu^- + \mu^+ +\hspace{1pt}\text{X}$. Trident production is a rare, purely electroweak process with sensitivity to physics beyond the Standard Model. The theoretical background, motivation for studying the process, and previous experimental measurements are reviewed. The analysis uses data collected by the NOvA near detector (ND) from Fermilab's Neutrinos at the Main Injector (NuMI) beam between November 2014 and February 2024, corresponding to an exposure of $25.5\times 10^{20}$ protons on target. The ND is a segmented tracking calorimeter located 800~m from the beam target, receiving neutrinos with a mean energy of 2~GeV. A multi-pass background reduction strategy is implemented, including the development of a novel dimuon-specific tracking technique. Trident candidates are identified using a boost ed decision tree classifier trained on simulated signal and background events. Limited background Monte Carlo statistics necessitate the use of functional fits to sideband data, which are extrapolated to estimate backgrounds in the signal region. The unblinded data contain 9 trident-like events, with an estimated background of 5.66 $\pm$ 5.15 events. This yields a best fit estimate of 3.34 tridents compared to the Standard Model prediction of 4.66. A profiled Feldman-Cousins method is used to determine a 90\% confidence interval of [0,9.1] on the number of signal events, corresponding to an upper limit of 1.95$\times$ the Standard Model prediction. This result represents the lowest energy search for trident events to date, and the first experimental contribution to the process in 27 years.

Bowles, Reed Scott [Indiana U.]↗
DOE OSTI2021

Applying Waveform Correlation to Reduce Seismic Analyst Workload Due to Repeating Mining Blasts

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high–quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment to detect and identify mining blasts for two regions, Wyoming (U.S.A.) and Scandinavia, using waveform templates recorded by multiple International Monitoring System stations of the Preparatory Commission for the Comprehensive Nuclear–Test–Ban Treaty Organization (CTBTO PrepCom) for up to 10 yr prior to the time of interest. We discuss approaches for template selection, threshold setting, and event detection that are specialized for characterizing mining blasts using a sparse, global network. We apply the approaches to one week of data for each of the two regions to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be generally applied to operational monitoring systems with a sparse network. Here, we compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to assess potential reduction in analyst workload.

58 GEOSCIENCES↗
DOE OSTI2026

Wasserstein normalized autoencoder for anomaly detection

A novel anomaly detection algorithm is presented. The Wasserstein normalized autoencoder (WNAE) is a normalized probabilistic model that minimizes the Wasserstein distance between the learned probability distribution—a Boltzmann distribution where the energy is the reconstruction error of the autoencoder (AE)—and the distribution of the training data. This algorithm has been developed and applied to the identification of semivisible jets—conical sprays of visible standard model (SM) particles and invisible dark matter states—with the CMS experiment at the CERN LHC. Trained on jets of particles from simulated SM processes, the WNAE is shown to learn the probability distribution of the input data in a fully unsupervised fashion, such that it effectively identifies new physics jets as anomalies. The model exhibits stable, convergent training and recovers strong classification performance for a wide range of signals against the selected background process, for which a standard AE fails because of outlier reconstruction. In addition, the model improves upon standard normalized autoencoders while remaining fully agnostic to the signal. The WNAE directly tackles the problem of outlier reconstruction, a common failure mode of autoencoders in anomaly detection tasks.

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗
DOE OSTI2023

Beam Dynamics Challenges in the Muon g-2 Experiment

The muon&s;s anomalous magnetic moment $a_{\mu}$ has hinted at physics beyond the standard model for nearly 20 years. The Muon $g-2$ experiment at Fermilab aims to measure $a_{\mu}$ to 140 parts per billion (ppb) precision. The 460 ppb result from its first data run (Run-1), released in 2021, agreed with the previous 2006 Brookhaven Muon $g-2$ result. The experimental average stands in tension with the standard model theory $a_{\mu}$ prediction by $4.2 \sigma$. The result of Run-2/3 data analysis is set be released in summer 2023, and will improve on the Run-1 precision by a factor of two. With the data collected in all six runs, the experiment is on track to produce a 140 ppb measurement of $a_{\mu}$. If the experiment and theory central values are both unchanged, the tension would exceed $5 \sigma$. The measurement is accomplished by injecting muons into a magnetic storage ring and precisely measuring two observable frequencies: $\omega_a$, the muons&s; anomalous precession frequency, and $\tilde{\omega}&s;_p$, the precession frequency of protons which determines the magnetic field strength experienced by the muons. This thesis presents a selection of muon beam dynamics effects which are critical for reaching the experiment precision goal. A system of detectors assists with the challenging beam injection into the storage ring, and a measurement of the injected beam provides input for simulating the stored beam dynamics. A new method is introduced to reduce a critical systemic caused by time dependence in the stored beam momentum, enabled by a detector which directly profiles the stored beam. Finally the analysis of $\tilde{\omega}&s;_p$, the muon-weighted magnetic field, for the Run-2/3 result is presented. Systematics of $\tilde{\omega}&s;_p$ due to beam effects are evaluated in detail, and shown to be sub-dominant.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Forty years of durability assessment of nuclear waste glass by standard methods

Abstract Standard methods to assess the durability of vitrified radioactive waste were first developed in the 1980’s and, over the last 40 years, have evolved to yield a range of responses depending on experimental conditions and glass composition. Mechanistic understanding of glass dissolution has progressed in parallel, enhancing our interpretation of the data acquired. With the implementation of subsurface disposal for vitrified radioactive waste drawing closer, it is timely to review the available standard methodologies and reflect upon their relative advantages, limitations, and how the data obtained can be interpreted to support the post-closure safety case for radioactive waste disposal.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

AmeriFlux FLUXNET-1F US-CLF Cole Farm

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-CLF Cole Farm. This is the FLUXNET version of the carbon flux data for the site US-CLF Cole Farm produced by applying the standard ONEFlux (1F) software. Site Description - The Cole Farm catchment (0.65km2) is located ~ 4 km southwest of the Shale Hills site, draining orthogonally to a syncline axis of the Wills Creek Formation, a calcareous shale containing interbedded siltstone, sandstone, shaly limestone, and dolomite. Even though the farm adopted no-till practices in the 1970s, the axial channel of Cole Farm flows over a thick (>2.5 m) package of sediment in the valley floor. Soils range in texture from silty clay at the ridge top to sandy loam in the valley floor. Data was collected and funded by the Critical Zone Observatory Network.

Davis, Kenneth J. [Department of Meteorology, Eart↗
DOE OSTI2022

Review and Experimental Comparison of the Durability of Iodine Waste Forms in Semi-Dynamic Leach Testing

To ensure the safe disposal of radioiodine-containing nuclear wastes, a durable iodine waste form (IWF) is required. Many IWFs have been developed and evaluated for chemical durability using a wide range of test methods, making direct comparisons difficult as highlighted in this work through a review of available IWF corrosion data. To move toward a common dataset, a semi-dynamic leach test suite was experimentally demonstrated using several IWF types, providing standard comparisons. The results highlighted clear differences in corrosion resistance between various IWFs. This test suite is suggested for future studies to generate comparable data for a common dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Data transfer for STAR grid jobs

The Solenoidal Tracker at RHIC (STAR) is a multipurpose experiment at the Relativistic Heavy Ion Collider (RHIC) with the primary goal to study the formation and properties of the quark-gluon plasma. STAR is an international collaboration of member institutions and laboratories from around the world. Yearly data-taking period produces PBytes of raw data collected by the experiment. STAR primarily uses its dedicated facility at BNL to process this data, but has routinely leveraged distributed systems, both high throughput (HTC) and high performance (HPC) computing clusters, to significantly augment the processing capacity available to the experiment. The ability to automate the efficient transfer of large data sets on reliable, scalable, and secure infrastructure is critical for any large-scale distributed processing campaign. For more than a decade, STAR computing has relied upon GridFTP with its x509-based authentication to build such data transfer systems and integrate them into its larger production workflow. The end of support by the community for both GridFTP and the x509 standard requires STAR to investigate other approaches to meet its distributed processing needs. In this study we investigate two multi-purpose data distribution systems, Globus.org and XRootD, as alternatives to GridFTP. We compare both their performance and the ease by which each service is integrated into the type of secure and automated data transfer systems STAR has previously built using GridFTP. The presented approach and study may be applicable to other distributed data processing use cases beyond STAR.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Constraining Galaxy-Halo connection using machine learning

We investigate the potential of machine learning (ML) methods to model small-scale galaxy clustering for constraining Halo Occupation Distribution (HOD) parameters. Our analysis reveals that while many ML algorithms report good statistical fits, they often yield likelihood contours that are significantly biased in both mean values and variances relative to the true model parameters. This highlights the importance of careful data processing and algorithm selection in ML applications for galaxy clustering, as even seemingly robust methods can lead to biased results if not applied correctly. ML tools offer a promising approach to exploring the HOD parameter space with significantly reduced computational costs compared to traditional brute-force methods if their robustness is established. Using our ANN-based pipeline, we successfully recreate some standard results from recent literature. Properly restricting the HOD parameter space, transforming the training data, and carefully selecting ML algorithms are essential for achieving unbiased and robust predictions. Among the methods tested, artificial neural networks (ANNs) outperform random forests (RF) and ridge regression in predicting clustering statistics, when the HOD prior space is appropriately restricted. We demonstrate these findings using the projected two-point correlation function (w p (r p )), angular multipoles of the correlation function (ξ ℓ (r)), and the void probability function (VPF) of Luminous Red Galaxies from Dark Energy Spectroscopic Instrument mocks. Our results show that while combining w p (r p ) and VPF improves parameter constraints, adding the multipoles ξ 0 , ξ 2 , and ξ 4 to w p (r p ) does not significantly improve the constraints.

cosmology↗