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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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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Effective field theory analysis of the first LUX dark matter search

The Large Underground Xenon (LUX) dark matter search was a 250-kg active mass dual-phase time projection chamber that operated by detecting light and ionization signals from particles incident on a xenon target. In December 2015, LUX reported a minimum 90% upper C.L. of 6 x 10 -46 cm 2 on the spin-independent WIMP-nucleon elastic scattering cross section based on a 1.4 x 10 4 kg ∙ day exposure in its first science run. Tension between experiments and the absence of a definitive positive detection suggest it would be prudent to search for WIMPs outside the standard spin-independent/spin-dependent paradigm. Recent theoretical work has identified a complete basis of 14 independent effective field theory (EFT) operators to describe WIMP-nucleon interactions. In addition to spin-independent and spin-dependent nuclear responses, these operators can produce novel responses such as angular-momentum-dependent and spin-orbit couplings. Here we report on a search for all 14 of these EFT couplings with data from LUX's first science run. Limits are placed on each coupling as a function of WIMP mass.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Applications of targeted proteomics in metabolic engineering: advances and opportunities

Optimization of metabolically engineered organisms requires good understanding of producing balanced level of pathway proteins. Targeted proteomics via selected-reaction monitoring (SRM) has been increasingly used in metabolic engineering studies to detect and quantify sets of proteins with high selectivity, multiplexity, and reproducibility. In combination with metabolomics and other omics tools, targeted proteomics has helped optimize the production of many bio-based chemicals in various metabolic engineering cell factories. In this review, we present recent applications of targeted proteomics in metabolic engineering studies and highlight several successful cases of targeted proteomics in boosting production of commodity and high value chemicals. Additionally, we also discuss challenges and limitations of current targeted proteomics and map opportunities for future research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

In-vivo Raman microspectroscopy reveals differential nitrate concentration in different developmental zones in Arabidopsis roots

Abstract Background Nitrate (NO 3 − ) is one of the two major forms of inorganic nitrogen absorbed by plant roots, and the tissue nitrate concentration in roots is considered important for optimizing developmental programs. Technologies to quantify the expression levels of nitrate transporters and assimilating enzymes at the cellular level have improved drastically in the past decade. However, a technological gap remains for detecting nitrate at a high spatial resolution. Using extraction-based methods, it is challenging to reliably estimate nitrate concentration from a small volume of cells (i.e., with high spatial resolution), since targeting a small or specific group of cells is physically difficult. Alternatively, nitrate detection with microelectrodes offers subcellular resolution with high cell specificity, but this method has some limitations on cell accessibility and detection speed. Finally, optical nitrate biosensors have very good ( in-vivo ) sensitivity (below 1 mM) and cellular-level spatial resolution, but require plant transformation, limiting their applicability. In this work, we apply Raman microspectroscopy for high-dynamic range in-vivo mapping of nitrate in different developmental zones of Arabidopsis thaliana roots in-situ . Results As a proof of concept, we have used Raman microspectroscopy for in-vivo mapping of nitrate content in roots of Arabidopsis seedlings grown on agar media with different nitrate concentrations. Our results revealed that the root nitrate concentration increases gradually from the meristematic zone (~ 250 µm from the root cap) to the maturation zone (~ 3 mm from the root cap) in roots grown under typical growth conditions used for Arabidopsis, a trend that has not been previously reported. This trend was observed for plants grown in agar media with different nitrate concentrations (0.5–10 mM). These results were validated through destructive measurement of nitrate concentration. Conclusions We present a methodology based on Raman microspectroscopy for in-vivo label-free mapping of nitrate within small root tissue volumes in Arabidopsis. Measurements are done in-situ without additional sample preparation. Our measurements revealed nitrate concentration changes from lower to higher concentration from tip to mature root tissue. Accumulation of nitrate in the maturation zone tissue shows a saturation behavior. The presented Raman-based approach allows for in-situ non-destructive measurements of Raman-active compounds.

Fernández González, Alma↗
DOE OSTI2023

Propidium Monoazide (PMAxx)-Recombinase Polymerase Amplification Exo (RPA Exo) Assay for Rapid Detection of Burkholderia cepacia Complex in Chlorhexidine Gluconate (CHX) and Benzalkonium Chloride (BZK) Solutions

Both sterile and non-sterile pharmaceutical products, which include antiseptics, have been recalled due to Burkholderia cepacia complex (BCC) contamination. Therefore, minimizing the frequency of outbreaks may be conducive to the development of a quick and sensitive approach that can distinguish between live and dead loads of BCC. We have assessed an exo probe-based recombinase polymerase amplification (RPA) with 10 µM propidium monoazide (PMAxx) for selective detection of live/dead BCC cells in various concentrations of antiseptics (i.e., chlorhexidine gluconate (CHX) and benzalkonium chloride (BZK) solutions) after 24 h. The optimized assay conducted using a set of primer–probes targeting gbpT was performed at 40 °C for 20 min and shows a detection limit of 10 pg/µL of genomic DNA from B. cenocepacia J2315, equivalent to 10 4 colony-forming units (CFU/mL). The specificity of a newly designed primer and probe was 80% (20 negatives out of 25). The readings for total cells (i.e., without PMAxx) from 200 µg/mL CHX using PMAxx-RPA exo assay was 310 relative fluorescence units (RFU), compared to 129 RFU with PMAxx (i.e., live cells). Furthermore, in 50–500 µg/mL BZK-treated cells, a difference in the detection rate was observed between the PMAxx-RPA exo assay in live cells (130.4–459.3 RFU) and total cells (207.82–684.5 RFU). This study shows that the PMAxx-RPA exo assay appears to be a valid tool for the simple, rapid and presumptive detection of live BCC cells in antiseptics, thereby ensuring the quality and safety of pharmaceutical products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Utilization of Unmanned Aircraft Systems for Environmental Purposes at the Savannah River Site – 26578

Born in the 1950s, the Savannah River Plant was constructed as a plutonium and tritium production plant. As the Cold War era came and went, a change of direction was signaled when the name of the facility changed from Savannah River Plant to Savannah River Site (1989) – the main mission at Savannah River shifted from “production” to “cleanup.” The Department of Energy is responsible for managing the 310 square miles of land that is the Savannah River Site and the cleanup/remediation activities that occur. The Savannah River National Laboratory utilizes unmanned aircraft systems to assist with some of those environmental monitoring and remediation activities. One function of unmanned aircraft systems at the Savannah River National Laboratory is conducting aerial photography and videography. Various infrastructure evaluations have been performed with unmanned aircraft – facility rooftop inspections, water tower lock verifications, earthen dam surveys, etc. An unmanned aircraft system has been used for progress footage of remediation projects – Dunbarton Bay remediation, Saltstone Disposal Unit construction, etc. Thermal cameras on an unmanned aircraft system are used to help identify cracks/leaks in structures from vantage points not readily accessible by personnel. Virtual tours of different waste units are conducted with unmanned aircraft systems for Site groups and federal/state regulators to save costs and travel time. Additionally, advanced remote sensing technologies are used on unmanned aircraft systems at the Savannah River Site. Light Detection and Ranging uses laser pulses to measure distances to the Earth's surface or other targets to create highly detailed topographic maps that accurately portray terrain (e.g., elevation changes, slopes, and contours). Data collection with a Light Detection and Ranging unit mounted on an unmanned aircraft system is quick and efficient – large-area surveys are conducted in shorter time frames. Because Light Detection and Ranging can penetrate through foliage and vegetation to ground level, it is being used in conjunction with a watershed model to study the General Separations Area of the Site to determine stormwater flow volume and direction, monitor stream stability, and forecast river flow behavior. A hyperspectral camera captures a wide spectrum of electromagnetic wavelengths across numerous narrow bands, beyond what the human eye can see. It provides detailed spectral information about the objects or surfaces in an image, which can be used to identify and analyze materials based on their spectral signatures. A hyperspectral camera mounted on an unmanned aircraft system has been used at the Savannah River Site for non-destructive evaluation of concrete and concrete structures. Unmanned aircraft systems at the Savannah River Site are also used for the application of herbicide on identified rooftops. P-Reactor and R-Reactor are entombed nuclear reactors at the Savannah River Site. Unwanted vegetation is often present on these rooftops – over time roots can penetrate seams, cracks, and joints of the general roof and concrete caps, leading to water ingress and deterioration of the structural components. For time and cost efficiencies as well as in the interest of personnel safety, an unmanned aircraft is used to dispense herbicide on rooftop areas where vegetation is noticed to help alleviate the issues/hazards.

Lorier, Troy [Savannah River National Laboratory (↗
DOE OSTI2021

The Modeled Performance of the ORNL Matched Filter Detector for Seismic Signatures of Multi-Axle Tractor Trailers

The MINOS program seeks methods to passively monitor the transfer of special materials, which are reproducible and quantitatively defensible. To date, we have demonstrated that short-period seismometers deployed near vehicle tracks can detect and monitor signatures of multi-axle tractor trailers impacting small road obstacles as means to detect and identify some transfer activities. In this report, we model the seismic velocity signal output from a tractor-trailer impacting a small obstacle (a template waveform) to parameterize a matched filter that identifies signals with similar shapes buried in noise, and that indicate repeating events (target waveforms). We then use this model to perform thousands of synthetic experiments, in which we apply our detector to identify noisy waveforms output from a similarly modeled vehicle data set; we process all data in the same manner as we process real data collected from the Oak Ridge National Laboratory (ORNL) seismic network. Our experiments, while limited, reveal that the matched filter shows a high discrimination capability, and at very low SNR values (at visibility thresholds). This means that template waveforms can match target waveforms that are sourced by models that share the same vehicle speed (target vehicles), and have a low probability of mis-identification. In particular, a matched filter designed to detect a five-axle tractor-trailer will reliably identify that target vehicle down to an SNR of 11.25 dB, and within ±0.1 km/hr, while successfully screening target vehicles with three axles.

42 ENGINEERING↗
DOE OSTI2022

Redshift evolution of the H I detection rate in radio-loud active galactic nuclei

We present a search for associated H I 21 cm absorption in a sample of 29 radio-loud active galactic nuclei (AGNs) at 0.7 < z < 1, carried out with the upgraded Giant Metrewave Radio Telescope. We detect H I 21 cm absorption against none of our target AGNs, obtaining 3σ upper limits to the optical depth of ≲1% per 50 km s –1 channel. The radio luminosity of our sources is lower than that of most AGNs searched for H I 21 cm absorption at similar redshifts in the literature, and, for all targets except two, the UV luminosity is below the threshold 10 23 W Hz –1 , above which the H I in the AGN environment has been suggested to be completely ionised. We stacked the H I 21 cm spectra to obtain a more stringent limit of ≈0.17% per 50 km s –1 channel on the average H I 21 cm optical depth of the sample. Furthermore, the sample is dominated by extended radio sources, 24 of which are extended on scales of tens of kiloparsecs. Including similar extended sources at 0.7 < z < 1.0 from the literature, and comparing with a low-z sample of extended radio sources, we find statistically significant (≈3σ) evidence that the strength of H I 21 cm absorption towards extended radio sources is weaker at 0.7 < z < 1.0 than at z < 0.25, with a lower detection rate of H I 21 cm absorption at 0.7 < z < 1.0. Redshift evolution in the physical conditions of H I is the likely cause of the weaker associated H I 21 cm absorption at high redshifts, due to either a low H I column density or a high spin temperature in high-z AGN environments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Demonstration of the HeRALD superfluid helium detector concept

The SPICE/HeRALD collaboration is performing research and development to enable studies of sub-GeV dark matter models using a variety of target materials. Here we report our recent progress on instrumenting a superfluid 4 He target mass with a transition-edge sensor based calorimeter to detect both atomic signals (scintillation) and 4 He quasiparticle (phonon and roton) excitations. The sensitivity of HeRALD to the critical “quantum evaporation” signal from 4 He quasiparticles requires us to block the superfluid film flow to the calorimeter. We have developed a heat-free film-blocking method employing an unoxidized Cs film, which we implemented in a prototype “HeRALD v0.1” detector of ~10 g target mass. This article reports initial studies of the atomic and quasiparticle signal channels. Here, a key result of this work is the measurement of the quantum evaporation channel’s gain of 0.15±0.01, which will enable 4 He-based dark matter experiments in the near term. With this gain the HeRALD detector reported here has an energy threshold of 145 eV at 5⁢σ, which would be sensitive to dark matter masses down to 220 MeV/c 2 .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Laser-driven collisionless shock acceleration of ions from near-critical plasmas

Here, this paper overviews experimental and numerical results on acceleration of narrow energy spread ion beams by an electrostatic collisionless shockwave driven by 1 μm (Omega EP) and 10 μm (UCLA Neptune Laboratory) lasers in near critical density CH and He plasmas, respectively. Shock waves in CH targets produced high-energy ~50 MeV protons (ΔE/E of ≤30%) and 314 MeV C 6+ ions (ΔE/E of ≤10%). Observation of acceleration of both protons and carbon ions to similar velocities is consistent with reflection of particles off the moving potential of a shock front. For shocks driven by CO 2 laser in a gas jet, ~30 MeV peak in He ion spectrum was detected. Particle-in-cell simulations indicate that regardless of the target further control over its density profile is needed for optimization of accelerated ion beams in part of energy spread, yield and maximum kinetic energy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Review of Intrusion Detection Methods and Tools for Distributed Energy Resources

Recent trends in the growth of distributed energy resources (DER) in the electric grid and newfound malware frameworks that target internet of things (IoT) devices is driving an urgent need for more reliable and effective methods for intrusion detection and prevention. Cybersecurity intrusion detection systems (IDSs) are responsible for detecting threats by monitoring and analyzing network data, which can originate either from networking equipment or end-devices. Creating intrusion detection systems for PV/DER networks is a challenging undertaking because of the diversity of the attack types and intermittency and variability in the data. Distinguishing malicious events from other sources of anomalies or system faults is particularly difficult. New approaches are needed that not only sense anomalies in the power system but also determine causational factors for the detected events. In this report, a range of IDS approaches were summarized along with their pros and cons. Using the review of IDS approaches and subsequent gap analysis for application to DER systems, a preliminary hybrid IDS approach to protect PV/DER communications is formed in the conclusion of this report to inform ongoing and future research regarding the cybersecurity and resilience enhancement of DER systems.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Space charge effects in the SπRIT time projection chamber

Time projection chambers (TPCs) are widely used in nuclear and particle physics. They are particularly useful when measuring reaction products from heavy ion collisions. Most nuclear experiments at low energy are performed in a fixed target configuration, in which the unreacted beam will pass through the detection volume. As the beam intensity increases, the buildup of positive ions created from the ionization of the detector gas by the beam creates the main source of space charge, distorting the nominal electric field of the TPC. This has a profound effect on the accuracy of the measured momenta of the emitted particles. In this paper we will discuss the magnitude of the effects and construct an observable appropriate for fixed target experiments to study the effects. Finally, we will present an algorithm for correcting the space charge and discuss some of the implications it has on the momentum determination.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
DOE OSTI2020

Inferring transmission heterogeneity using virus genealogies: estimation and targeted prevention

Spread of HIV typically involves uneven transmission patterns where some individuals spread to a large number of individuals while others to only a few or none. Such transmission heterogeneity can impact how fast and how much an epidemic spreads. Further, more efficient interventions may be achieved by taking such transmission heterogeneity into account. To address these issues, we developed two phylogenetic methods based on virus sequence data: 1) to generally detect if significant transmission heterogeneity is present, and 2) to pinpoint where in a phylogeny high-level spread is occurring. We derive inference procedures to estimate model parameters, including the amount of transmission heterogeneity, in a sampled epidemic. We show that it is possible to detect transmission heterogeneity under a wide range of simulated situations, including incomplete sampling, varying levels of heterogeneity, and including within-host genetic diversity. When evaluating real HIV-1 data from different epidemic scenarios, we found a lower level of transmission heterogeneity in slowly spreading situations and a higher level of heterogeneity in data that included a rapid outbreak, while R0 and Sackin’s index (overall tree shape statistic) were similar in the two scenarios, suggesting that our new method is able to detect transmission heterogeneity in real data. We then show by simulations that targeted prevention, where we pinpoint high-level spread using a coalescence measurement, is efficient when sequence data are collected in an ongoing surveillance system. Such phylogeny-guided prevention is efficient under both single-step contact tracing as well as iterative contact tracing as compared to random intervention.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Use of Longitudinal Serum Analysis and Machine Learning to Develop a Classifier for Cancer Early Detection

Early detection of solid tumors through a simple screening process, such as the proteomic analysis of biofluids, has the potential to significantly alter the management and outcomes of cancers. The application of advanced targeted proteomics measurements and data analysis strategies to uniformly collected serum or plasma samples would enable longitudinal studies of cancer risk, progression, and response to therapy that have the potential to significantly reduce cancer burden in general. In this article, we describe a generalizable workflow combining robust, multiplexed targeted proteomics measurements applied to longitudinal samples from the Department of Defense Serum Repository with a Random Forest machine learning method for developing and initially evaluating the performance of candidate biomarker panels for early detection of cancers. The effectiveness of this approach was demonstrated in a cohort of 175 head and neck squamous cell carcinoma patients. The outlined protocols include methods for sample preparation, instrument analysis, and data analysis and interpretation using this workflow.

Longitudinal analysis, machine learning, cancer, e↗
DOE OSTI2022

Hubble Asteroid Hunter

Context: The Hubble Space Telescope (HST) archives constitute a rich dataset of high-resolution images to mine for strong gravitational lenses. While many HST programmes specifically target strong lenses, they can also be present by coincidence in other HST observations. Aims: Our aim is to identify non-targeted strong gravitational lenses, without any prior selection on the lens properties, in almost two decades of images from the ESA HST archive (eHST). Methods: We used crowdsourcing on the Hubble Asteroid Hunter (HAH) citizen science project to identify strong lenses, along with asteroid trails, in publicly available large field-of-view HST images. We visually inspected 2354 objects tagged by citizen scientists as strong lenses to clean the sample and identify the genuine lenses. Results: We report the detection of 252 strong gravitational lens candidates, which were not the primary targets of the HST observations. A total of 198 of them are new, not previously reported by other studies, consisting of 45 A grades, 74 B grades and 79 C grades. The majority are galaxy-galaxy configurations. The newly detected lenses are, on average, 1.3 magnitudes fainter than previous HST searches. This sample of strong lenses with high-resolution HST imaging is ideal to follow up with spectroscopy for lens modelling and scientific analyses. Conclusions: This paper presents the unbiased search of lenses that enabled us to find a wide variety of lens configurations, including exotic lenses. We demonstrate the power of crowdsourcing in visually identifying strong lenses and the benefits of exploring large archival datasets. This study shows the potential of using crowdsourcing in combination with artificial intelligence for the detection and validation of strong lenses in future large-scale surveys such as ESA’s Euclid mission or in James Webb Space Telescope (JWST) archival images.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Millimeter-wave Detections of Symbiotic Stars in SPT and ACT Data

We present the results of a joint targeted search of candidate symbiotic stars at millimeter wavelengths using the South Pole Telescope (SPT) and the Atacama Cosmology Telescope (ACT). Candidates are selected from the New Online Database of Symbiotic Variables, restricting to objects that are within either the SPT-3G or ACT~DR6 footprint, covering most of the southern hemisphere and up to a declination of $+20^\circ$. Forced photometry on the 828 candidate symbiotic star locations in SPT and ACT data results in 31 unique objects detected with more than a $3σ$ significance using two frequency bands: 18 confirmed and 13 suspected symbiotic stars. We provide the SPT and ACT 95/98, 150, and 220~GHz light curves, along with optical and infrared light curves from 2016--2026, as well as spectral energy distributions, physical parameters from the literature, and brief summaries regarding the nature of each individual object. Using Herschel SPIRE data from 2013, we place upper limits on millimeter flux for CN Cha near the beginning of the optical rise in its 2012/2013 nova, which suggests a strong variability and lag at millimeter wavelengths and results in a rare observance of a Galactic millimeter slow transient. In addition, we provide coadded thumbnails and light curves for the remaining 797 candidate symbiotic stars that did not pass our detection thresholds. Millimeter-wave emission from symbiotic stars is primarily a combination of free-free emission of the ionization region and optically thick blackbody emission of the cooler dust components of the system. When combined with contemporaneous multi-wavelength observations, millimeter-wave observations can be used to test binary models of symbiotic stars and provide insight on the geometry and physical properties of these systems.

Tandoi, C. [Illinois U., Urbana, Astron. Dept.] (O↗
DOE OSTI2024

Model-Based Detection of Coordinated Attacks (DCA) in Distribution Systems

The fast-paced growth in digitization of smart grid components enhances system observability and remote-control capabilities through efficient communication. However, enhanced connectivity results in heightened system vulnerability towards cybersecurity risks in the cyber-physical power system. Coordinated cyber-attacks (CCA), when undetected, lead to system-wide impact in terms of large disturbances or widespread outages. Detecting CCA in the cyber layer is critical to thwart cyber-attacks in real-time before the attack impacts the physical system. The challenge of locating CCA stems from the complex grid dynamics, making it difficult to distinguish between normal operational variations and cyber-attack impact. CCA often employs multiple attack vectors targeting geographically distributed components, further complicating CCA identification. Existing research in intrusion detection is primarily focused on the transmission network and limited to detecting individual attacks. In this paper, a novel proactive DCA strategy is proposed for early detection of CCA by establishing correlations among distinct attack events through model-based reinforcement learning that utilizes abductive reasoning to conclude the attacker goal. The solution includes understanding the system model, learning the system dynamics, and correlating individual cyber-attacks to extract the attacker’s objective. The developed learning algorithm identifies the most probable attack path to reach the attacker’s objective by predicting the next attack steps. A DNP3-based cyber-physical co-simulation testbed is developed to test the proposed algorithm using the IEEE 13-node test feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Longitudinal spin transfer to Λ hyperons in semi-inclusive deep inelastic scattering with the CLAS12 spectrometer

The polarization of Λ hyperons is preserved in the angular distribution of their decay products. This property allows one to study the spin structure of the Λ. In semi-inclusive deep inelastic scattering where a high energy lepton interacts with a nucleon target and one or more hadrons and the scattered lepton are detected in the final state, the probability for a struck quark to impart the polarization of the lepton to the Λ may be measured. In particular, in electron-proton scattering this quantity may be related to the longitudinal light quark polarization of the Λ. Currently, limited experimental data cannot discriminate between different models of Λ spin structure. This work reports on the measurement of the longitudinal spin transfer 𝐷$^{Λ}_{𝐿⁢𝐿′}$ to the Λ using data taken by the CLAS12 spectrometer at Jefferson Lab with a 10.6 GeV longitudinally polarized electron beam and an unpolarized hydrogen target. This measurement is the most precise to date, and, in comparison with theory predictions, it offers valuable insight into the relative dominance of current and target fragmentation in Λ production.

deep inelastic scattering↗