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DOE OSTI2025

AI Applications to Physics Experiments at Jefferson Lab

We survey how AI/ML is being deployed across Jefferson Lab's experimental and accelerator programs. In EPSCI, Hydra applies computer vision to automate real-time data-quality monitoring across all four experimental halls, replacing manual inspection of hundreds to thousands of histograms per shift. AIEC (AI Experiment Controls) uses ML to stabilize drift chamber gains and is now part of standard CEBAF production running, while AI Optimized Polarization (AIOP) targets autonomous control of polarized targets and photon beam angular alignment. In CASA, cavity fault classification models identify faulted cavities and trip types from waveform data with ~85% and ~78% agreement to labeled data, respectively, and are deployed in production; a separate effort applies LLMs and hybrid search to make the CEBAF operations logbook AI-ready. QCD-focused work includes transformer- and GAN-based generative models for particle-level event simulation, with distributed GAN training scaling studies on Polaris. Additional efforts span ML-on-FPGA for the EIC and a new Data Science Department coordinating anomaly detection, uncertainty quantification, and HPC-scalable ML lab-wide. Collectively, these projects illustrate AI's growing role in improving efficiency across JLab's nuclear physics mission.

Mei, Xinxin [Thomas Jefferson National Accelerator↗

DOE OSTI2026

Soil metagenomics umbrella narrative

Implementing accessible, authentic research experiences in introductory courses is challenging, particularly at institutions serving diverse student populations. To address this gap, we developed and deployed a Course-based Undergraduate Research Experience (CURE) focused on plant-microbe interactions in General Biology II at Northeastern Illinois University (NEIU), a minority-serving institution with a diverse student body. Students grew sugar beets (Beta vulgaris), extracted DNA from the rhizoplane, and used the Department of Energy Systems Biology Knowledgebase (KBase) for bioinformatic analysis to compare microbial relative abundance in fertilized versus unfertilized soil. Over five semesters, the CURE engaged 103 students and leveraged the intuitive KBase platform to make complex sequencing data accessible. Pre/post-course survey data revealed significant increases in student self-assessed research skills, including the ability to explain results and determine the types of data to collect. Furthermore, students reported significant gains in confidence related to experimental design and hypothesis development, alongside a strong increase in familiarity with KBase. Informal faculty feedback indicated high student engagement and appreciation for the real-world connections (e.g. food systems, agriculture, and health). This scalable, low-cost model effectively integrates data science tools into the foundational curriculum, demonstrating a potent strategy for boosting research skills and broadening participation in authentic scientific inquiry among diverse undergraduate students.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Spatial Interpolation of Air Pollutant and Meteorological Variables in Central Amazonia

The Amazon Rainforest is highlighted by the global community both for its extensive vegetation cover that constantly suffers the effects of anthropic action and for its substantial biodiversity. This dataset presents data of meteorological variables from the Amazon Rainforest region with a spatial resolution of 0.001° in latitude and longitude, resulting from an interpolation process. The original data were obtained from the GoAmazon 2014/5 project, in the Atmospheric Radiation Measurement (ARM) repository, and then processed through mathematical and statistical methods. The dataset presented here can be used in experiments in the field of Data Science, such as training models for predicting climate variables or modeling the distribution of species.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Characterizing Families of Spectral Similarity Scores and Their Use Cases for Gas Chromatography–Mass Spectrometry Small Molecule Identification

Metabolomics provides a unique snapshot into the world of small molecules and the complex biological processes that govern the human, animal, plant, and environmental ecosystems encapsulated by the One Health modeling framework. However, this “molecular snapshot” is only as informative as the number of metabolites confidently identified within it. The spectral similarity (SS) score is traditionally used to identify compound(s) in mass spectrometry approaches to metabolomics, where spectra are matched to reference libraries of candidate spectra. Unfortunately, there is little consensus on which of the dozens of available SS metrics should be used. This lack of standard SS score creates analytic uncertainty and potentially leads to issues in reproducibility, especially as these data are integrated across other domains. In this work, we use metabolomic spectral similarity as a case study to showcase the challenges in consistency within just one piece of the One Health framework that must be addressed to enable data science approaches for One Health problems. Here, using a large cohort of datasets comprising both standard and complex datasets with expert-verified truth annotations, we evaluated the effectiveness of 66 similarity metrics to delineate between correct matches (true positives) and incorrect matches (true negatives). We additionally characterize the families of these metrics to make informed recommendations for their use. Our results indicate that specific families of metrics (the Inner Product, Correlative, and Intersection families of scores) tend to perform better than others, with no single similarity metric performing optimally for all queried spectra. This work and its findings provide an empirically-based resource for researchers to use in their selection of similarity metrics for GC-MS identification, increasing scientific reproducibility through taking steps towards standardizing identification workflows.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

THE MEASUREMENT, TREATMENT, AND IMPACT OF SPECTRAL COVARIANCE AND BAYESIAN PRIORS IN INTEGRAL -FIELD SPECTROSCOPY OF EXOPLANETS

The recovery of an exoplanet’s atmospheric parameters from its spectrum requires accurate knowledge of the spectral errors and covariances. Unfortunately, the complex image processing used in high-contrast integral-field spectrograph (IFS) observations generally produces spectral covariances that are poorly understood and often ignored. In this work, we show how to measure the spectral errors and covariances and include them self-consistently in parameter retrievals. By combining model exoplanet spectra with a realistic noise model generated from the Gemini Planet Imager (GPI) early science data, we show that ignoring spectral covariance in high-contrast IFS data can both bias inferred parameters and lead to unreliable confidence regions on those parameters. This problem is made worse by the common practice of scaling the χ {sup 2} per degree of freedom to unity; the input parameters then fall outside the 95% confidence regions in as many as ∼80% of noise realizations. The biases we observe can approach the typical levels of precision achieved in high-contrast spectroscopy. Accounting for realistic priors in fully Bayesian retrievals can also have a significant impact on the inferred parameters. Plausible priors on effective temperature and surface gravity can vary by an order of magnitude across the confidence regions appropriate for objects with weak age constraints; priors for objects with good age constraints are dominated by modeling uncertainties. Our methods are directly applicable to existing high-contrast IFSs including GPI and SPHERE, as well as upcoming instruments like CHARIS and, ultimately, WFIRST-AFTA.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Reconstruction of Galaxy Star Formation Histories through SED Fitting:The Dense Basis Approach

We introduce the dense basis method for Spectral Energy Distribution (SED) fitting. It accurately recovers traditional SED parameters, including M {sub *}, SFR, and dust attenuation, and reveals previously inaccessible information about the number and duration of star formation episodes and the timing of stellar mass assembly, as well as uncertainties in these quantities. This is done using basis star formation histories (SFHs) chosen by comparing the goodness-of-fit of mock galaxy SEDs to the goodness-of-reconstruction of their SFHs. We train and validate the method using a sample of realistic SFHs at z = 1 drawn from stochastic realizations, semi-analytic models, and a cosmological hydrodynamical galaxy formation simulation. The method is then applied to a sample of 1100 CANDELS GOODS-S galaxies at 1 10{sup 9.5} M{sub ⊙}. About 40% of the CANDELS galaxies have SFHs whose maximum occurs at or near the epoch of observation. The dense basis method is scalable and offers a general approach to a broad class of data-science problems.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

COMAP Early Science. V. Constraints and Forecasts at z ~ 3

We present the current state of models for the z ~ 3 carbon monoxide (CO) line intensity signal targeted by the CO Mapping Array Project (COMAP) Pathfinder in the context of its early science results. Our fiducial model, relating dark matter halo properties to CO luminosities, informs parameter priors with empirical models of the galaxy–halo connection and previous CO (1–0) observations. The Pathfinder early science data spanning wavenumbers k = 0.051–0.62 Mpc –1 represent the first direct 3D constraint on the clustering component of the CO (1–0) power spectrum. Our 95% upper limit on the redshift-space clustering amplitude A clust ≲ 70 μK 2 greatly improves on the indirect upper limit of 420 μK 2 reported from the CO Power Spectrum Survey (COPSS) measurement at k ~ 1 Mpc –1 . The COMAP limit excludes a subset of models from previous literature and constrains interpretation of the COPSS results, demonstrating the complementary nature of COMAP and interferometric CO surveys. Using line bias expectations from our priors, we also constrain the squared mean line intensity–bias product, ${\left\langle {Tb}\right\rangle }_{2}$ ≲ 50 μK 2 , and the cosmic molecular gas density, ρ H2 < 2.5 × 10 8 M ⊙ Mpc –3 (95% upper limits). Based on early instrument performance and our current CO signal estimates, we forecast that the 5 yr Pathfinder campaign will detect the CO power spectrum with overall signal-to-noise ratio of 9–17. Between then and now, we also expect to detect the CO–galaxy cross-spectrum using overlapping galaxy survey data, enabling enhanced inferences of cosmic star formation and galaxy evolution history.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Fermi Large Area Telescope Fourth Source Catalog

We present the fourth Fermi Large Area Telescope catalog (4FGL) of γ-ray sources. Based on the first eight years of science data from the Fermi Gamma-ray Space Telescope mission in the energy range from 50 MeV to 1 TeV, it is the deepest yet in this energy range. Relative to the 3FGL catalog, the 4FGL catalog has twice as much exposure as well as a number of analysis improvements, including an updated model for the Galactic diffuse γ-ray emission, and two sets of light curves (one-year and two-month intervals). The 4FGL catalog includes 5064 sources above 4σ significance, for which we provide localization and spectral properties. Seventy-five sources are modeled explicitly as spatially extended, and overall, 358 sources are considered as identified based on angular extent, periodicity, or correlated variability observed at other wavelengths. For 1336 sources, we have not found plausible counterparts at other wavelengths. More than 3130 of the identified or associated sources are active galaxies of the blazar class, and 239 are pulsars.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Finding Peas in the Early Universe with JWST

The Early Release Observations (EROs) of JWST beautifully demonstrate the promise of JWST in characterizing the universe at Cosmic Dawn. We analyze the Near Infrared Spectrograph ERO spectra of three z ~ 8 galaxies to determine their metallicities, gas temperatures, and ionization. These galaxies offer the first opportunity to understand the physical properties of Epoch-of-Reionization galaxies through detailed rest-optical emission-line spectroscopy. We show that these objects have metal abundances $12+\mathrm{log}[{\rm{O}}/{\rm{H}}]\,\approx$ 6.9–8.3, based on both the T e method and on a recent calibration of the R 23 metallicity indicator. Since the spectra are some of the earliest science data from JWST, we compare several line ratios with values expected from robust physics, to validate our measurement procedures. We compare the abundances and emission-line ratios to a nearby sample of Green Pea galaxies—a population of nearby emission-line galaxies whose UV properties resemble Epoch-of-Reionization galaxies, and which often have large Lyman continuum escape fractions. The JWST data show striking further similarities between these high-redshift galaxies and nearby Green Peas. The z ~ 8 galaxies span the metallicity range covered by Green Peas. They also show the compact morphology that is typical of emission-line-dominated galaxies at all redshifts. Based on these similarities with Green Peas, it is likely that these are the first rest-optical spectra of galaxies that are actively driving cosmological reionization.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Advanced Offshore Hazard Forecasting to Enable Resilient Offshore Operations

Paper prepared for the Offshore Technology Conference, 2024. Hazards in the offshore environment can imperil successful energy operations, whether those operations are conventional, renewable, or for decarbonization. The expanding accessibility of data science and the advanced applications of machine learning (ML) models creates an opportunity to assess potential hazards and the infrastructure they impact. We present a use case demonstrating the combined application of published ML tools to U.S. federal waters of the Gulf of Mexico, an actively explored region for offshore energy that is affected by variable metocean conditions and geologic processes contributing to potential hazards.

Mark-Moser, Mackenzie K.↗

DOE OSTI2025

Long Range Plan: Dense matter theory for heavy-ion collisions and neutron stars

Since the release of the 2015 Long Range Plan in Nuclear Physics, major events have occurred that reshaped our understanding of quantum chromodynamics (QCD) and nuclear matter at large densities, in and out of equilibrium. The US nuclear community has an opportunity to capitalize on advances in astrophysical observations and nuclear experiments and engage in an interdisciplinary effort in the theory of dense baryonic matter that connects low- and high-energy nuclear physics, astrophysics, gravitational waves physics, and data science. This is a white paper prepared by a group of nuclear physicists during the 2023 LRP process.

Lovato, Alessandro↗

DOE OSTI2023

Grand challenges in the digitalisation of wind energy

The availability of large amounts of data is starting to impact how the wind energy community works. From turbine design to plant layout, construction, commissioning, and maintenance and operations, new processes and business models are springing up. This is the process of digitalisation, and it promises improved efficiency and greater insight, ultimately leading to increased energy capture and significant savings for wind plant operators, thus reducing the levelised cost of energy. Digitalisation is also impacting research, where it is both easing and speeding up collaboration, as well as making research results more accessible. This is the basis for innovations that can be taken up by end users. But digitalisation faces barriers. This paper uses a literature survey and the results from an expert elicitation to identify three common industry-wide barriers to the digitalisation of wind energy. Comparison with other networked industries and past and ongoing initiatives to foster digitalisation show that these barriers can only be overcome by wide-reaching strategic efforts, and so we see these as “grand challenges” in the digitalisation of wind energy. They are, first, creating FAIR data frameworks; secondly, connecting people and data to foster innovation; and finally, enabling collaboration and competition between organisations. The grand challenges in the digitalisation of wind energy thus include a mix of technical, cultural, and business aspects that will need collaboration between businesses, academia, and government to solve. Working to mitigate them is the beginning of a dynamic process that will position wind energy as an essential part of a global clean energy future.

17 WIND ENERGY↗

DOE OSTI2024

Opportunities for Process Intensification with Membranes to Promote Circular Economy Development for Critical Minerals

Critical minerals are essential to the future of clean energy, especially energy storage, electric vehicles, and advanced electronics. In this paper, we argue that process systems engineering (PSE) paradigms provide essential frameworks for enhancing the sustainability and efficiency of critical mineral processing pathways. As a concrete example, we review challenges and opportu-nities across material-to-infrastructure scales for process intensification (PI) with membranes. Within critical mineral processing, there is a need to reduce environmental impact, especially con-cerning chemical reagent usage. Feed concentrations and product demand variability require flex-ible, intensified processes. Further, unique feedstocks require unique processes (i.e., no one-size-fits-all recycling or refining system exists). Membrane materials span a vast design space that allows significant optimization. Therefore, there is a need to rapidly identify the best opportunities for membrane implementation, thus informing materials optimization with process and infrastructure scale performance targets. Finally, scale-up must be accelerated and de-risked across the materials-to-process levels to fully realize the opportunity presented by membranes, thereby fostering the development of a circular economy for critical minerals. Tackling these challenges requires integrating efforts across diverse disciplines. We advocate for a holistic molecular-to-systems perspective for fully realizing PI with membranes to address sustainability challenges in critical mineral processing. The opportunities for PI with membranes are excellent applications for emerging research in machine learning, data science, automation, and optimization.

Dougher, Molly↗

DOE OSTI2012

Moving from Descriptive to Causal Analytics: Case Study of the Health Indicators Warehouse

The KDD community has described a multitude of methods for knowledge discovery on large datasets. We consider some of these methods and integrate them into an analyst s workflow that proceeds from the data-centric descriptive level to the model-centric causal level. Examples of the workflow are shown for the Health Indicators Warehouse, which is a public database for community health information that is a potent resource for conducting data science on a medium scale. We demonstrate the potential of HIW as a source of serious visual analytics efforts by showing correlation matrix visualizations, multivariate outlier analysis, multiple linear regression of Medicare costs, and scatterplot matrices for a broad set of health indicators. We conclude by sketching the first steps toward a causal dependence hypothesis.

Schryver, Jack C.↗

DOE OSTI2013

Mining Large Heterogeneous Graphs using Cray s Urika

Pattern discovery and predictive modeling from seemingly related Big Data represented as massive, ad-hoc, heterogeneous networks (e.g., extremely large graphs with complex, possibly unknown structure) is an outstanding problem in many application domains. To address this problem, we are designing graph-mining algorithms capable of discovering relationship-patterns from such data and using those discovered patterns as features for classification and predictive modeling. Specifically, we are: (i) exploring statistical properties, mechanics and generative models of behavior patterns in heterogeneous information networks, (ii) developing novel, automated and scalable graph-pattern discovery algorithms and (iii) applying our relationship-analytics (data science + network science) expertise to domains spanning healthcare to homeland security.

fraud detection↗

DOE OSTI2019

The National Energy Technology Laboratory’s New Unit Process Template Design - LCAXIX

This work was presented at the American Center for Life Cycle Assessment 2019 Conference (LCAXIX) in Tucson, AZ on September 25, 2019. The National Energy Technology Laboratory (NETL) maintains a large unit process (UP) library which holds all documentation for each unit process created by the NETL Life Cycle Analysis (LCA) team. Each NETL unit process includes a Microsoft Excel spreadsheet containing calculations and an accompanying PDF, which thoroughly documents the calculations, parameters, and underlying reasoning and assumptions contained in the Excel file. However, as more and larger public data sources become available, the NETL LCA team has incorporated more data science techniques into their unit process development. NETL is demonstrating a new template which will allow for more flexible and advanced calculation and documentation methods. The new template will support NETL’s dedication to transparency by continuing to provide all information necessary to replicate the UPs.

Krynock, Michelle↗

DOE OSTI2022

Bio-Optimized Technologies to Keep Thermoplastics out of Landfills and the Environment (BOTTLE)

Plastics have revolutionized modern life, but reliance on these fossil-based materials that persist for centuries is causing a pollution crisis and contributing to greenhouse gas (GHG) emissions. To develop new technologies to address this problem, the Bio-Optimized Technologies to keep Thermoplastics out of Landfills and the Environment (BOTTLE) Consortium will deliver selective, scalable technologies to enable cost-effective recycling, upcycling, and increased energy efficiency. BOTTLE is an interdisciplinary team of experts that aim to develop selective, scalable processes to deconstruct and upcycle today's plastics and thermosets, redesign tomorrow's plastics to be recyclable-by-design (RBD) and derived from both bio-based and plastic waste-based feedstocks, work with industrial partners across the value chain to catalyze the circular economy for plastics, and leverage AMO and BETO investments in analysis-guided R&D, integrated process development, chemical and biological catalysis, materials characterization, modeling, and data science. BOTTLE is guided by techno-economic analysis (TEA) and supply chain-based life-cycle assessment (LCA). BOTTLE comprises members from ten partner institutions. Primary outcomes to date include establishment of a full consortium, impactful, benchmarking analyses that will be important for the plastics recycling and upcycling community, and multiple impactful, high-impact publications across the breadth of our research portfolio.

bio-optimized↗

DOE OSTI2021

Response to Andrew Fowlie’s Comments

This Letter to the Editor of Harvard Data Science Review is a rejoinder to a Letter to the Editor by Andrew Fowlie, titled “Comment on Reproducibility and Replication of Experimental Particle Physics Results”.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗