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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

From benchtop to raceway: spectroscopic signatures of dynamic biological processes in algal communities

Major scientific challenges hinder the success of an industrial-scale algal biofuels program. Four broad areas of R&D needs have been identified for economically viable, industrial-scale cultivation of algae: culture sustainability; system productivity; nutrient source scaling and sustainability; and water conservation, management, and recycling. Progress in each of these areas is limited by significant knowledge gaps in fundamental algal biology. This SAND report summarizes research conducted as part of an LDRD project (FY10- FY12) to address this shortcoming. We have developed a novel, multidisciplinary, multiscale approach utilizing Sandia’s core expertise in bioanalytical spectroscopy, chemical imaging, remote sensing, genomics, and computational modeling in collaboration with researchers at University of New Mexico and Arizona State University to investigate the effects that dynamic abiotic and biotic stressors have on algal photosynthesis, growth, and lipid production. The discoveries will enable gains in productivity and sustainability that are critical for cost-effective, industrial-scale algal facilities.

Timlin, Jerilyn Ann↗
DOE OSTI2017

A Bright Short Period M-M Eclipsing Binary from the KELT Survey: Magnetic Activity and the Mass–Radius Relationship for M Dwarfs

We report the discovery of KELT J041621-620046, a moderately bright (J ∼ 10.2) M-dwarf eclipsing binary system at a distance of 39 ± 3 pc. KELT J041621-620046 was first identified as an eclipsing binary using observations from the Kilodegree Extremely Little Telescope (KELT) survey. The system has a short orbital period of ∼1.11 days and consists of components with M{sub 1}=0.447{sub +0.052}{sup −0.047} M{sub ⊙} and M{sub 2}=0.399{sub +0.046}{sup −0.042} M{sub ⊙} in nearly circular orbits. The radii of the two stars are R{sub 1}=0.540{sub +0.034}{sup −0.032} R{sub ⊙} and R{sub 2}=0.453±0.017 R{sub ⊙}. Full system and orbital properties were determined (to ∼10% error) by conducting an EBOP (Eclipsing Binary Orbit Program) global modeling of the high precision photometric and spectroscopic observations obtained by the KELT Follow-up Network. Each star is larger by 17%–28% and cooler by 4%–10% than predicted by standard (non-magnetic) stellar models. Strong Hα emission indicates chromospheric activity in both stars. The observed radii and temperature discrepancies for both components are more consistent with those predicted by empirical relations that account for convective suppression due to magnetic activity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Genesis Data Card Schema, Template and Supporting Tools

Genesis Data Cards provide a standardized template and schema for documenting scientific datasets in support of discovery, access, interoperability, reusability, governed use, and AI usability. This release of the Genesis Data Card repository includes a versioned Markdown template, a LinkML schema with generated Pydantic and JSON artifacts, schema documentation, and example completed data cards. Validation tooling is provided to ensure that completed data cards conform to the schema prior to submission. Accompanying documentation for the structured metadata is provided as a Field Reference Guide. The schema and accompanying template provided in this repository address the call for actionable context that enables humans and AI systems to find, access, interpret, cite, and reuse data, and, when appropriate, integrate it into AI and machine learning workflows. The data card is intended to serve as a common metadata artifact intended to support standardized, cross-program dataset documentation across Department of Energy (DOE)-aligned efforts, including but not limited to Genesis Mission-related implementations, the Office of Science, National Nuclear Security Administration (NNSA), and Advanced Simulation and Computing (ASC) data governance and stewardship initiatives.

data card↗
DOE OSTI2015

Recent advances in automotive catalysis for NOx emission control by small-pore microporous materials

The ever increasing demand to develop highly fuel efficient engines coincides with the need to minimize air pollution originating from the exhaust gases of internal combustion engines. Dramatically improved fuel efficiency can be achieved at air-to-fuel ratios much higher than stoichiometric. In the presence of oxygen in large excess, however, traditional three-way catalysts are unable to reduce NOx. Among the number of lean-NOx reduction technologies, selective catalytic reduction (SCR) of NOx by NH3 over Cu- and Fe-ion exchanged zeolite catalysts has been extensively studied over the past 30+ years. Despite the significant advances in developing a viable practical zeolite-based catalyst for lean NOx reduction, the insufficient hydrothermal stabilities of the zeolite structures considered cast doubts about their real-world applicability. During the past decade a renewed interest in zeolite-based lean NOx reduction was spurred by the discovery of the very high activity of Cu-SSZ-13 (and the isostructural Cu-SAPO-34) in the NH3 SCR of NOx. These new, small-pore zeolite-based catalysts not only exhibited very high NOx conversion and N2 selectivity, but also exhibited exceptional high hydrothermal stability at high temperatures. In this review we summarize the key discoveries of the past ~5 years that lead to the introduction of these catalysts into practical application. The review first briefly discusses the structure and preparation of the CHA structure-based zeolite catalysts, and then summarizes the key learnings of the rather extensive (but not complete) characterisation work. Then we summarize the key findings of reaction kinetics studies, and provide some mechanistic details emerging from these investigations. At the end of the review we highlight some of the issues that are still need to be addressed in automotive exhaust control catalysis. Funding A.M.B. and I.L.G. would like to thank EPSRC for funding. F.G., C.H.F.P. and J.Sz. gratefully acknowledge financial support from the US Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Program.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2014

Metal-poor stars observed with the Magellan telescope. II. Discovery of four stars with [Fe/H] ≤ –3.5

We report on the discovery of seven low-metallicity stars selected from the Hamburg/ESO Survey, six of which are extremely metal-poor (EMP, [Fe/H] ≤ –3.0), with four having [Fe/H] ≤ –3.5. Chemical abundances or upper limits are derived for these stars based on high-resolution (R ∼ 35,000) Magellan/MIKE spectroscopy, and are in general agreement with those of other very and extremely metal-poor stars reported in the literature. Accurate metallicities and abundance patterns for stars in this metallicity range are of particular importance for studies of the shape of the metallicity distribution function of the Milky Way's halo system, in particular for probing the nature of its low-metallicity tail. In addition, taking into account suggested evolutionary mixing effects, we find that six of the program stars (with [Fe/H] ≤ –3.35) possess atmospheres that were likely originally enriched in carbon, relative to iron, during their main-sequence phases. These stars do not exhibit overabundances of their s-process elements, and hence may be, within the error bars, additional examples of the so-called CEMP-no class of objects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

TU-H-206-01: An Automated Approach for Identifying Geometric Distortions in Gamma Cameras

Purpose: To develop a clinically-deployable, automated process for detecting artifacts in routine nuclear medicine (NM) quality assurance (QA) bar phantom images. Methods: An artifact detection algorithm was created to analyze bar phantom images as part of an ongoing QA program. A low noise, high resolution reference image was acquired from an x-ray of the bar phantom with a Philips Digital Diagnost system utilizing image stitching. NM bar images, acquired for 5 million counts over a 512×512 matrix, were registered to the template image by maximizing mutual information (MI). The MI index was used as an initial test for artifacts; low values indicate an overall presence of distortions regardless of their spatial location. Images with low MI scores were further analyzed for bar linearity, periodicity, alignment, and compression to locate differences with respect to the template. Findings from each test were spatially correlated and locations failing multiple tests were flagged as potential artifacts requiring additional visual analysis. The algorithm was initially deployed for GE Discovery 670 and Infinia Hawkeye gamma cameras. Results: The algorithm successfully identified clinically relevant artifacts from both systems previously unnoticed by technologists performing the QA. Average MI indices for artifact-free images are 0.55. Images with MI indices < 0.50 have shown 100% sensitivity and specificity for artifact detection when compared with a thorough visual analysis. Correlation of geometric tests confirms the ability to spatially locate the most likely image regions containing an artifact regardless of initial phantom orientation. Conclusion: The algorithm shows the potential to detect gamma camera artifacts that may be missed by routine technologist inspections. Detection and subsequent correction of artifacts ensures maximum image quality and may help to identify failing hardware before it impacts clinical workflow. Going forward, the algorithm is being deployed to monitor data from all gamma cameras within our health system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

VISION: a modular AI assistant for natural human-instrument interaction at scientific user facilities

Scientific user facilities, such as synchrotron beamlines, are equipped with a wide array of hardware and software tools that require a codebase for human-computer-interaction. This often necessitates developers to be involved to establish connection between users/researchers and the complex instrumentation. The advent of generative AI presents an opportunity to bridge this knowledge gap, enabling seamless communication and efficient experimental workflows. Here we present a modular architecture for the Virtual Scientific Companion by assembling multiple AI-enabled cognitive blocks that each scaffolds large language models (LLMs) for a specialized task. With VISION, we performed LLM-based operation on the beamline workstation with low latency and demonstrated the first voice-controlled experiment at an x-ray scattering beamline. The modular and scalable architecture allows for easy adaptation to new instruments and capabilities. Development on natural language-based scientific experimentation is a building block for an impending future where a science exocortex—a synthetic extension to the cognition of scientists—may radically transform scientific practice and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2012

The story of the Tevatron accelerators: Accelerator science and technology breakthroughs, achievements and lessons

For almost a quarter of a century, the Tevatron proton-antiproton collider was the centerpiece of the world's high energy physics program - since it began operation in December of 1985, until it was overtaken by LHC in 2011. The aim of this unique scientific instrument was to explore the elementary particle physics reactions with center of mass collision energies of up to 1.96 TeV. The initial design luminosity of the Tevatron was 10{sup 30} cm{sup -2} s{sup -1}, however as a result of two decades of upgrades, the accelerator has been able to deliver 430 times higher luminosities to each of two high luminosity experiments, CDF and D0. The Tevatron has been shut off since September 30, 2011. The collider was arguably one of the most complex research instruments ever to reach the operation stage and is widely recognized for many technological breakthroughs and numerous physics discoveries. In this paper, we briefly present the history of the Tevatron, major advances in accelerator physics, technology implemented during the long quest for better and better performance, and the lessons learned from our experience.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Search for Dark Matter using mono-Higgs and the ATLAS Pixel Detector (Final Report)

A large component of the mass-energy of the universe is composed of dark matter (DM), whose properties and interactions with known particles are not yet understood. Searches for DM at the Large Hadron Collider (LHC) provide important information, complementary to direct and indirect detection experiments, that is necessary to determine whether an observed signal indeed stems from DM. Furthermore, the discovery of the Higgs boson provides a unique avenue to search for DM because the potential interaction of the Higgs with DM would lead to the unique signature of a Higgs boson recoiling against DM. This process is typically referred to as mono-Higgs because DM does not interact strongly with most known particles and will therefore pass unseen through the detector, leading to a single detected Higgs boson and a large imbalance of momentum. Due to the strength of the interactions of the Higgs with Standard Model particles, it is unlikely for a Higgs boson to be radiated from initial state quarks. Therefore, the observation of this process would provide direct insight into the mechanism by which DM couples to known particles. The objective of this research program is to search for the dark matter particles produced in association with a Higgs boson at the LHC using the ATLAS (A Toroidal LHC Apparatus) detector, specifically when the Higgs boson decays to two bottom quarks. This research will benefit greatly from the development of innovative identification techniques of boosted Higgs bosons. The program includes the upgrade of the ATLAS Pixel readout system, which is critical to maintain high performance of tracking, vertexing, and boosted Higgs tagging in the planned higher luminosity run of the LHC.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response

Cells respond to stressful conditions by coordinating a complex, multi-faceted response that spans many levels of physiology. Much of the response is coordinated by changes in protein phosphorylation. Although the regulators of transcriptome changes during stress are well characterized in Saccharomyces cerevisiae , the upstream regulatory network controlling protein phosphorylation is less well dissected. Here, we developed a computational approach to infer the signaling network that regulates phosphorylation changes in response to salt stress. We developed an approach to link predicted regulators to groups of likely co-regulated phospho-peptides responding to stress, thereby creating new edges in a background protein interaction network. We then use integer linear programming (ILP) to integrate wild type and mutant phospho-proteomic data and predict the network controlling stress-activated phospho-proteomic changes. The network we inferred predicted new regulatory connections between stress-activated and growth-regulating pathways and suggested mechanisms coordinating metabolism, cell-cycle progression, and growth during stress. We confirmed several network predictions with co-immunoprecipitations coupled with mass-spectrometry protein identification and mutant phospho-proteomic analysis. Results show that the cAMP-phosphodiesterase Pde2 physically interacts with many stress-regulated transcription factors targeted by PKA, and that reduced phosphorylation of those factors during stress requires the Rck2 kinase that we show physically interacts with Pde2. Conclusively, our work shows how a high-quality computational network model can facilitate discovery of new pathway interactions during osmotic stress.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap

One year ago, the AISLE roadmap argued that autonomous laboratories operated as isolated islands and proposed a grassroots network organized around five critical dimensions. The field has since moved faster than that roadmap anticipated: multi-agent systems have produced experimentally validated hypotheses, self-driving laboratories have grown more interoperable and orchestrated, reasoning-trained and domain foundation models have raised the capability ceiling, and the Genesis Mission has placed autonomous experimentation at the center of U.S. federal science strategy, with industry emerging as a primary actor. Progress has met a sobering counter-current, including a corrected flagship discovery result, benchmarks showing that agents which rival experts on closed-ended questions still complete only a fraction of open-ended research, and fabricated citations surfacing at leading venues. We read this as the defining tension of the field: producing a candidate discovery is no longer the hard part, but verifying it is, and this asymmetry now limits autonomous science more than raw model capability. Accordingly, we update the roadmap around seven dimensions, revisiting the original five and elevating two former cross-cutting concerns, trust, verification, and reproducibility, and safety, security, and governance, to first-class status. We assess the original milestones (M1 through M14) as achieved, partially achieved, reframed, or open, add four new milestones (M15 through M18) for the elevated dimensions, and scope the path forward to a two-year horizon, with the first year concentrating on interfaces, protocol adoption, and the scaffolding of verification, and the second targeting federation, zero-trust coordination, and governance. Throughout, we position the grassroots network as the interoperability fabric that lets national programs, international initiatives, and commercial platforms connect rather than re-silo.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

The SPAN512 mid-latitude pulsar survey at the Nançay Radio Telescope

The large number of ongoing surveys for pulsars and transients at various radio observatories is motivated by the science obtained from these sources. Timing and polarisation analysis of relativistic binaries can place strong constraints on theories of gravity. The observation of a growing number of millisecond pulsars (MSPs) spread over the celestial sphere may allow the detection of a stochastic gravitational wave background arising from supermassive black hole binaries. A more complete sample of young pulsars improves our knowledge of neutron star birth and evolution. Transients such as fast radio bursts can serve to probe the intergalactic medium. The SPAN512 pulsar survey covers intermediate Galactic latitudes using the L-band receiver of the Nançay Radio Telescope (NRT). The survey covers 224 sq. deg. of the sky for a total exposure time of 2200 h. Population syntheses predict the discovery of 3 to 19 new normal pulsars and a few MSPs. We present detailed modelling of the NRT beam with its L-band receiver and its sensitivity which we used to precisely assess the expected survey yield. We used the flexible Pulsar Arecibo L-band Feed Array data processing pipeline to search the 47 TB of SPAN512 data for pulsars and transients. The SPAN512 survey discovered two new MSPs and one new middle-aged pulsar. We focus on the analysis of the 2.4-ms spin period pulsar J2205+6012 for which we also report the detection of gamma-ray pulsations. Its narrow pulse width (35µs at an observing frequency of 2.55 GHz) allows for sub-microsecond timing precision over 8 yr, with exciting prospects for pulsar timing array programs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

CHESS 2025: Waveform LiDAR data from NEON AOP surveys

This dataset provides Level 1 (L1) full-waveform light detection and ranging (LiDAR) data collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). These data were acquired to enable characterization of vegetation structure and other three-dimensional features of the land surface, and to evaluate structural changes that may have occurred between a prior LiDAR acquisition in 2018 and the 2025 overflight. Waveform LiDAR data can provide more detailed information about objects on the ground than discrete point clouds typically do, and they are often used for granular target segmentation and characterization of subcanopy vegetation. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. LiDAR data were acquired using the Optech Galaxy Prime Airborne LiDAR Terrain Mapper onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). These are the primary waveform LiDAR data delivered by NEON and are provided per flightline in compressed Pulsewaves format, an open-source binary file standard. A Pulsewaves object comprises a two files: a pulse (.pls) file, which stores the geographic origin, outgoing vector, and metadata for every laser pulse emitted by the scanner, and a wave file (.wvs), which stores the sequential amplitude samples of the outgoing pulse and the returning signals. The files are published here in their compressed forms (.plz, .wvz). All waveform data were processed following the theoretical workflow described in the NEON L0-to-L1 Waveform LiDAR Algorithm Theoretical Basis Document (Krause and Goulden 2022a); however, the Pulsewaves output format differs from a legacy format described in that document. Waveform amplitude samples are recorded at 1 nanosecond intervals. All coordinates are provided in meters. Horizontal coordinates are referenced in Universal Transverse Mercator (UTM) zone 13N and the World Geodetic System (WGS) 1984 ensemble datum. Elevations are referenced to Geoid12A. Waveform data for the UPTA survey area were collected without incident and the published records are complete. However, both the ALMO and CRBU collections experienced issues that resulted in incomplete data for those areas. On collection day 2018-06-16 a hardware failure caused the waveform digitizer to lose data from the eastern edge of the ALMO site (Figure 22). The waveform data for flightlines 2–20 could not be extracted from the digitizer, and the data proved unrecoverable. As a result, a portion of the site does not have coverage with waveform data. Although no hardware failure was observed during collection over the CRBU area, final waveform files generated by vendor software contained only ~25% of the expected number of return pulses. After discovery, NEON initiated troubleshooting with the vendor. The root cause of the data ablation had not been identified at the time of publication. Additional data will be published in an update to this package if further recovery proves successful. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2024

Enabling Capabilities and Resources: 2024 Principal Investigator Meeting Proceedings

As a major supporter of basic genome-enabled research, BER’s Biological Systems Science Division (BSSD) fosters scientific discovery by funding - fundamental biological research across disciplines in conjunction with enabling investigational tools and computational capabilities that include world-class user facilities. The overarching goal of BSSD is to provide the necessary fundamental science to understand, predict, manipulate, and design biological systems that underpin innovations for bioenergy and bioproduct production and enhance understanding of natural, DOE-relevant environmental processes (Biological Systems Science Division Strategic Plan, 2021). To accelerate the U.S. bioeconomy, BSSD pursues innovative science underpinning advances in sustainable biofuels and bioproducts and the development of next-generation technologies and computational resources for systems biology research. The 2024 BSSD Enabling Capabilities and Resources (ECR) Principal Investigator (PI) meeting brought together PIs across the BSSD ECR portfolio to confer on shared interests and opportunities. The meeting was held concurrently with the Genomic Science program (GSP) PI meeting to optimize collaboration on research to advance bioenergy and the bioeconomy. Rick Stevens of Argonne National Laboratory gave a keynote on How Generative Artificial Intelligence Can Impact Biological Research (see Keynote: How Generative Artificial Intelligence Can Impact Biological Research, this page). Plenary presentations included several joint sessions that illuminated the integration and understanding of the larger BSSD mission. GSP’s objective is to provide systems-level understanding of plants, microbes, and their communities through its Bioenergy Research, Biosystems Design, and Environmental Microbiome Research portfolios. The objective of the ECR portfolio is to support development of computational and instrumental platforms to advance fundamental GSP research—and BER more broadly— toward the overall goal of understanding the functional principles of living systems and their response to environmental challenges.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Global polarization of hyperons and spin alignment of vector mesons in quark matters

Relativistic heavy ion collider (RHIC) as a dedicated nuclear facility has made a few major discoveries in physics. This year marks the 30th year STAR Collaboration formation and the 23th year of STAR detector operation and data collection at RHIC. In the last two decades, STAR has collected many datasets, exhibiting scientific versatility and flexibility of the RHIC facility. The total dataset in the first year is less than 1 million good events, and currently there are about 1 billion events per dataset. The Global Hyperon Polarization was proposed in 2004. This immediately prompted the STAR Collaboration to search for this phenomenon from the early datasets. The null results were presented at Quark Matter Conference in Shanghai in 2006 and subsequently published. Although there were peripheral and continuous efforts in the following decade, no positive result has been observed experimentally. This situation changed in the following decade with the upgrade of high data rate and time-of-flight (TOF) detector and the progress of the Beam Energy Scan Phase I (BES-I). The experimental discoveries of the global polarization of hyperons in 2017 and the spin alignment of vector mesons in 2023 at RHIC-STAR confirm the theory which was established nearly twenty years ago. The theory and these measurements open the way to studying the properties of the hot and dense nuclear matter created in high-energy heavy ion collisions from a new degree of freedom, spin. We briefly review these discoveries from the proposals of theory to the experimental measurements, and summarize the related measurements at the existing facilities and the theoretical explanations to the original proposal. The basic understanding and the original proposal are still valid and fundamental, that is, the angular momentum of system can transform into a spin effect observable in experiment. However, it appears that in each case a new model is needed to explain the new experimental observation. We need a more basic theory to help us unify all these spin related phenomena. Over the past five years, STAR has successfully installed 3 new detectors and we have begun to see the physical analysis results from datasets with those new functions. What makes the STAR detector viable after 20 years of operation is its continuous evolution through successful upgrades, with new scientific programs added year by year. The next big thing is to forward upgrade a tracking system (3 layers of silicon strips and 4 layers of sTGC chambers) and a calorimetry system (electromagnetic and hadronic calorimeters). In addition to studying the spin structure of protons by using the polarized proton beams at RHIC, the upgrades also provide a unique ability to investigate the origin of Λ Global Polarization as a function of rapidity and rapidity (de-)correlations in Au+Au collisions.

Physics↗
DOE OSTI2026

Sensitivity to low-mass WIMPs with an improved liquid argon ionization response model within the DarkSide program

Dark matter detection experiments using liquid argon rely on a precise characterization of the ionization response to nuclear recoils, especially in the keV energy range relevant for light dark matter interactions. In this work, we present a comprehensive analysis that combines new measurements from the ReD setup, part of the DarkSide experimental program, with calibration data from DarkSide-50, as well as results from the ARIS and SCENE experiments. These combined datasets enable improved constraints on atomic screening effects in the modeling of the ionization response of liquid argon to nuclear recoils. The analysis is performed within the Thomas-Imel recombination framework adopted in previous DarkSide studies, and is here further constrained by the inclusion of ReD data, which allow the screening function to be determined from calibration measurements. By including the updated ionization model into the DarkSide-50 analysis framework, we obtain stronger exclusion limits on low-mass weakly interacting massive particle (WIMP) interactions, setting new world-leading constraints in the 1 – 3 GeV / c 2 WIMP mass range. Finally, we recast the sensitivity projections for the upcoming DarkSide-20k detector, demonstrating a significantly enhanced discovery potential for low-mass dark matter candidates.

Acerbi, F. [Fond. Bruno Kessler, Trento]↗
DOE OSTI2018

Final technical report: From colliders to cosmology in the LHC era

Now that the Higgs boson has been discovered, the question is: why is it so light when its mass is expected to blow up to the highest mass scale of the particles to which it couples? Supersymmetry tames these quadratic divergences, yet there is as yet no sign of superparticles at LHC. Sparticle mass limits and the Higgs mass suggest sparticles which reside in the multi-TeV range if at all. This situation heightens the Little Hierarchy or SUSY naturalness question: why the mass gap between weak scale and sparticle mass scale? In the past several years, our group has been highly focused on understanding the naturalness question (H. Baer). During this period, we have made some major clarifications in naturalness, showing how conventional measures overestimate fine-tuning and showing that ``natural SUSY'' requires light higgsino states-- with mass the closer to the 100 GeV scale the better. Such an inverted electroweakino spectrum leads to new, distinctive signatures at LHC (e.g. same-sign diboson production) while the ILC is expected to be a higgsino factory. We have found a deep connection between naturalness and the Peccei-Quinn scale. Dark matter is expected to occur as a higgsino-like WIMP plus axion admixture and detection of both is ultimately expected. Additional objectives of our research program are making more precise predictions for the production and decay rates of new particles, developing techniques and computer codes to discover these particles and to measure their properties, and searching for new phenomena and new interactions at present and future colliders. In addition, we will investigate direct and indirect searches for supersymmetric dark matter: both WIMPs and axions. % To carry out these studies, we will calculate production rates for SUSY particles, other new particles in various extensions to the Standard Model and physics backgrounds, as well as evaluate QCD and electroweak radiative corrections. Since the cross sections of most signals are expected to be small relative to those of the backgrounds, we plan on developing analysis techniques suitable for extracting small signals. In this project, we propose continuing along these productive investigation lines so as to facilitate discoveries which can be made at LHC, ILC and at various dark matter detectors. These projects facilitate the DoE goals at the Energy and Cosmic Frontiers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

LENS: Learning Enabled Network Synthesis

RTRC and UMD have developed novel machine learning based methods under the ARPA-E DIFFERENTIATE program for rapid acceleration of hypothesis generation in complex architecture design spaces involving both discrete choices of component inclusion and interconnection and continuous parametric decisions. The project named Learning Enabled Network Synthesis (LENS) further demonstrated the developed methods on challenging electrical power converter design problems by identifying the most suitable circuit topologies and simultaneously selecting the most appropriate components to achieve optimized design of power converter with improved performances. We demonstrated that LENS could enable exploration of very large design space of circuit topologies and components by addressing the limitations of conventional design process in non-linear, high switching speed, multi-dimensional power converter design and optimization. The key innovation developed in LENS is the seamless integration of statistical learning and logical reasoning techniques and building on the individual strengths of these techniques for rapid hypothesis discovery. The main component of LENS comprises of: 1) Graph Reasoning Engine (GRE) to enforce composition rules that rapidly reject all discrete architectures that are composed incorrectly and generates an adaptive database of feasible designs which can be used by ML modules, 2) Graph Generative Learning module which is a deep neural network based generative model for graph architectures which can enable design space exploration beyond the dataset generated by the GRE, 3) Graph Reduced Order Model (ROM) for graph domains for accelerating computation of output metrics, and 4) Active learning and Rule Discovery module for sample efficient learning and extracting logical rules from the learned ML models which will be integrated in the GRE to enhance the filtering effectiveness. LENS approach can be applied to any design domains where designs can be represented as multi-attribute graphs. The LENS team integrated the various technical innovations listed above into an optimization pipeline and exercised the optimization pipeline on the converter design problem. The LENS project demonstrated that the developed AI/ML technologies can be used to generate novel converter circuits >45x faster than experts on chosen use-cases. This can enable faster design space exploration and identification of new designs which are not considered by experts due to the increasing design space complexity. This has significant potential impact on the public and energy needs of the country. It is currently estimated that 30% of all electrical powers generated passes through power converters. The future estimate is that 80% of all power generated would be passing through converters. LENS fills a critical gap in this space since by accelerating the design process the designers would be able to generate more efficient converters which can lead to significant energy savings for the country.

42 ENGINEERING↗