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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The Importance of Modeling Carbon Dioxide Transportation and Geologic Storage in Energy System Planning Tools

Energy system planning tools suggest that the cost and feasibility of climate-stabilizing energy transitions are sensitive to the cost of CO 2 capture and storage processes (CCS), but the representation of CO 2 transportation and geologic storage in these tools is often simple or non-existent. We develop the capability of producing dynamic-reservoir-simulation-based geologic CO 2 storage supply curves with the Sequestration of CO 2 Tool (SCO 2 T) and use it with the ReEDS electric sector planning model to investigate the effects of CO 2 transportation and geologic storage representation on energy system planning tool results. We use a locational case study of the Electric Reliability Council of Texas (ERCOT) region. Our results suggest that the cost of geologic CO 2 storage may be as low as $3/tCO 2 and that site-level assumptions may affect this cost by several dollars per tonne. At the grid level, the cost of geologic CO 2 storage has generally smaller effects compared to other assumptions (e.g., natural gas price), but small variations in this cost can change results (e.g., capacity deployment decisions) when policy renders CCS marginally competitive. The cost of CO 2 transportation generally affects the location of geologic CO 2 storage investment more than the quantity of CO 2 captured or the location of electricity generation investment. We conclude with a few recommendations for future energy system researchers when modeling CCS. For example, assuming a cost for geologic CO 2 storage (e.g., $5/tCO 2 ) may be less consequential compared to assuming free storage by excluding it from the model.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

A Geospatial Cost Comparison of CO2 Plume Geothermal (CPG) Power and Geologic CO2 Storage

CO 2 Plume Geothermal (CPG) power plants can use gigatonne-levels of CO 2 sequestration to generate electricity, but it is unknown if the resources that support low-cost CPG power align with the resources that support low-cost CO 2 sequestration. Here, we estimate and compare the geospatially-distributed cost of CPG and CO 2 storage across a portion of North America. We find that the locations with lowest-cost CO 2 storage are different than the locations with lowest-cost CPG. There are also locations with low-cost CO 2 storage (<$5/tCO 2 ) that do not support CPG power generation due to insufficient reservoir transmissivity or temperature. Thus, CPG development may require electricity prices that are greater than the levelized cost of electricity (LCOE) to offset the increased cost of sequestration. We introduce the “Additional Cost of Electricity (ACOE)” metric to account for this cost and add it to the LCOE to calculate breakeven electricity prices that are required for CPG development. We find that breakeven prices are lower when new CO 2 injection wells are drilled specifically for CPG (i.e., “greenfield” CPG development) compared to if only existing CO 2 sequestration injection wells are used (i.e., “brownfield” CPG development). This is because comparatively few wells are needed for sequestration-only, and the increased power capacity from having more CPG wells outweighs the increased costs from more drilling. We also find that sequestered CO 2 could be used to approximately triple the United States geothermal electricity power capacity via a single CPG “sweet spot” in South Dakota, but that breakeven electricity price for this development is on the order of $200/MW e h.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Coastal inundation regime moderates the short-term effects of sediment and soil additions on seawater oxygen and greenhouse gas dynamics: a microcosm experiment

The frequency and persistence of tidal inundation varies along the coastal terrestrial-aquatic interface, from frequently inundated wetlands to rarely inundated upland forests. This inundation gradient controls soil and sediment biogeochemistry and influence the exchange of soils and sediments from terrestrial to aquatic domains. Although a rich literature exist on studies of the influence of tidal waters on the biogeochemistry of coastal ecosystem soils, few studies have experimentally addressed the reverse question: How do soils (or sediments) from different coastal ecosystems influence the biogeochemistry of the tidal waters that inundate them? To better understand initial responses of coastal waters that flood coastal wetlands and uplands, we conducted short-term laboratory experiments where seawater was amended with sediments and soils collected across regional gradients of inundation exposure (i.e., frequently to rarely inundated) for 14 sites across the Mid-Atlantic, USA. Measured changes in dissolved oxygen and greenhouse gas concentrations were used to calculate gas consumption or production rates occurring during seawater exposure to terrestrial materials. We also measured soil and water physical and chemical properties to explore potential drivers. We observed higher oxygen consumption rates for seawater incubated with soils/sediments from frequently inundated locations and higher carbon dioxide production for seawater incubated with soils from rarely inundated transect locations. Incubations with soil from rarely inundated sites produced the highest global warming potential, primarily driven by carbon dioxide and secondarily by nitrous oxide. We also found environmental drivers of gas rates varied notably between transect locations. Our findings indicate that seawater responses to soil and sediment inputs across coastal terrestrial-aquatic interfaces exhibit some consistent patterns and high intra- and inter-site variability, suggesting potential biogeochemical feedback loops as inundation regimes shift inland.

coastal inundation↗
DOE OSTI2023

Assessing residential activity in a home plumbing system simulator: monitoring the occurrence and relationship of major opportunistic pathogens and phagocytic amoebas

Opportunistic premise plumbing pathogens (OPPPs) have been detected in buildings’ plumbing systems causing waterborne disease outbreaks in the United States. In this study, we monitored the occurrence of OPPPs along with free-living amoeba (FLA) and investigated the effects of residential activities in a simulated home plumbing system (HPS). Water samples were collected from various locations in the HPS and analyzed for three major OPPPs: Legionella pneumophila, nontuberculous mycobacterial species (e.g., Mycobacterium avium, M. intracellulare, and M. abscessus), and Pseudomonas aeruginosa along with two groups of amoebas (Acanthamoeba and Vermamoeba vermiformis). A metagenomic approach was also used to further characterize the microbial communities. Results show that the microbial community is highly diverse with evidence of spatial and temporal structuring influenced by environmental conditions. L. pneumophila was the most prevalent pathogen (86% of samples), followed by M. intracellulare (66%) and P. aeruginosa (21%). Interestingly, M. avium and M. abscessus were not detected in any samples. The data revealed a relatively low prevalence of Acanthamoeba spp. (4%), while V. vermiformis was widely detected (81%) across all the sampling locations within the HPS. Locations with a high concentration of L. pneumophila and M. intracellulare coincided with the highest detection of V. vermiformis, suggesting the potential growth of both populations within FLA and additional protection in drinking water. After a period of stagnation lasting at least 2-weeks, the concentrations of OPPPs and amoeba immediately increased and then decreased gradually back to the baseline. Furthermore, monitoring the microbial population after drainage of the hot water tank and partial drainage of the entire HPS demonstrated no significant mitigation of the selected OPPPs. This study demonstrates that these organisms can adjust to their environment during such events and may survive in biofilms and/or grow within FLA, protecting them from stressors in the supplied water.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The Biomechanics of Indirect Traumatic Optic Neuropathy Using a Computational Head Model With a Biofidelic Orbit

Indirect traumatic optic neuropathy (ITON) is an injury to the optic nerve due to head trauma and usually results in partial or complete loss of vision. In order to advance a mechanistic understanding of the injury to the optic nerve, we developed a head model with a biofidelic orbit. Head impacts were simulated under controlled conditions of impactor velocity. The locations of impact were varied to include frontal, lateral, and posterior parts of the head. Impact studies were conducted using two types of impactors that differed in their rigidity relative to the skull. The simulated results from both the impactors suggest that forehead impacts are those to which the optic nerve is most vulnerable. The mode and location of optic nerve injury is significantly different between the impacting conditions. Simulated results using a relatively rigid impactor (metal cylinder) suggest optic nerve injury initiates at the location of the intracranial end of the optic canal and spreads to the regions of the optic nerve in the vicinity of the optic canal. In this case, the deformation of the skull at the optic canal, resulting in deformation of the optic nerve, was the primary mode of injury. On the other hand, simulated results using a relatively compliant impactor (soccer ball) suggest that primary mode of injury comes from the brain tugging upon the optic nerve (from where it is affixed to the intracranial end of the optic canal) during coup countercoup motion of the brain. This study represents the first published effort to employ a biofidelic simulation of the full length of the optic nerve in which the orbit is integrated within the whole head.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Managed aquifer recharge as a strategy to redistribute excess surface flow to baseflow in snowmelt hydrologic regimes

Water management in snowmelt hydrologic regimes, characterized by large annual fluctuations in stream flow driven by seasonal snow melt, faces the challenge of highly variable supply that often does not align with timing of demand. Climate change may exacerbate management challenges by significantly reducing snowpack or shifting snow melt earlier. Here, managed aquifer recharge (MAR) is evaluated as a potential strategy to reallocate excess early-season stream flow to time periods when less surface water is available. This strategy differs from traditional MAR, where the goal is to minimize loss to surface water. We assess how to site MAR operations such that groundwater recharge flows back to the surface water system in a lagged manor to benefit water management objectives, which we term “enhanced baseflow.” We use a regional groundwater model for the Treasure Valley aquifer located in southwestern Idaho, United States to demonstrate a generalizable approach using regional groundwater models as tools to identify favorable baseflow enhancement locations. Hypothetical MAR is simulated at 197 candidate locations, which are then evaluated for how effectively they meet potential management objectives. In addition to demonstrating the modeling and evaluation approach, we discuss lessons learned from applying a pre-existing regional groundwater model to MAR for enhanced baseflow and also describe important considerations, such as the physical and institutional availability of surface flows and specific management objectives, when assessing regional and site-specific suitability of MAR for enhanced baseflow as a potential management strategy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Refractivity Observations from Radar Phase Measurements: The 22 May 2002 Dryline Case during IHOP Project

The dryline, often associated with the development of severe storms in the Southern Great Plains of the United States of America, is a boundary layer phenomenon that occurs when a warm and moist air mass from the Gulf of Mexico meets a hot and dry air mass from the southwest desert area. An accurate knowledge of the water vapor spatio-temporal variability in the lower part of the atmosphere is crucial for a better understanding of the evolution of the dryline. The tropospheric refractivity, directly related to water vapor content, is a proxy for the water vapor content of the troposphere. It has already been demonstrated that the refractivity and the refractivity vertical gradient can be jointly estimated from radar phase measurements. In fact, it has been shown that using kriging interpolation techniques, accurate refractivity maps within the coverage area of the radar can be obtained with high temporal resolution. In this paper, a detailed analysis of the time series of radar-based refractivity maps obtained during a dryline that occurred on the afternoon of 22 May 2002 during the International H 2 O Project is presented. Comparisons between the time series of radar refractivity maps, obtained with the NCAR S-Pol radar, and the refractivity measurements derived from automatic ground-based weather stations and the AERI instrument, placed at different locations within the coverage area of the NCAR S-Pol radar, demonstrate the accuracy of radar refractivity estimates even for highly variable conditions, both in time and space, in the troposphere. Correlation coefficients higher than 0.95 are obtained in all weather station locations. Regarding the RMSE, errors less than 6 N-units are obtained for all cases, being even as low as 2.92 N-units at some locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Prediction of Performance Variation Caused by Manufacturing Tolerances and Defects in Gas Diffusion Electrodes of Phosphoric Acid (PA)–Doped Polybenzimidazole (PBI)-Based High-Temperature Proton Exchange Membrane Fuel Cells

The automated process of coating catalyst layers on gas diffusion electrodes (GDEs) for high-temperature proton exchange membrane fuel cells results inherently into a number of defects. These defects consist of agglomerates in which the platinum sites cannot be accessed by phosphoric acid and which are the consequence of an inconsistent coating, uncoated regions, scratches, knots, blemishes, folds, or attached fine particles—all ranging from μm to mm size. These electrochemically inactive spots cause a reduction of the effective catalyst area per unit volume (cm2/cm3) and determine a drop in fuel cell performance. A computational fluid dynamics (CFD) model is presented that predicts performance variation caused by manufacturing tolerances and defects of the GDE and which enables the creation of a six-sigma product specification for Advent phosphoric acid (PA)-doped polybenzimidazole (PBI)-based membrane electrode assemblies (MEAs). The model was used to predict the total volume of defects that would cause a 10% drop in performance. It was found that a 10% performance drop at the nominal operating regime would be caused by uniformly distributed defects totaling 39% of the catalyst layer volume (~0.5 defects/μm2). The study provides an upper bound for the estimation of the impact of the defect location on performance drop. It was found that the impact on the local current density is higher when the defect is located closer to the interface with the membrane. The local current density decays less than 2% in the presence of an isolated defect, regardless of its location along the active area of the catalyst layer.

Gurau, Vladimir (ORCID:0000000327429061)↗
DOE OSTI2024

Simulation of the Multi-Wake Evolution of Two Sandia National Labs/National Rotor Testbed Turbines Operating in a Tandem Layout

The future of wind power systems deployment is in the form of wind farms comprised of scores of such large turbines, most likely at offshore locations. Individual turbines have grown in span from a few tens of meters to today’s large turbines with rotor diameters that dwarf even the largest commercial aircraft. These massive dynamical systems present unique challenges at scales unparalleled in prior applications of wind science research. Fundamental to this effort is the understanding of the wind turbine wake and its evolution. Furthermore, the optimization of the entire wind farm depends on the evolution of the wakes of different turbines and their interactions within the wind farm. In this article, we use the capabilities of the Common ODE Framework (CODEF) model for the analysis of the effects of wake–rotor and wake-to-wake interactions between two turbines situated in a tandem layout fully and partially aligned with the incoming wind. These experiments were conducted in the context of a research project supported by the National Rotor Testbed (NRT) program of Sandia National Labs (SNL). Results are presented for a layout which emulates the turbine interspace and relative turbine emplacement found at SNL’s Scaled Wind Technologies Facility (SWiFT), located in Lubbock, Texas. The evolution of the twin-wake interaction generates a very rich series of secondary transitions in the vortex structure of the combined wake. These ultimately affect the wake’s axial velocity patterns, altering the position, number, intensity, and shape of localized velocity-deficit zones in the wake’s cross-section. This complex distribution of axial velocity patterns has the capacity to substantially affect the power output, peak loads, fatigue damage, and aeroelastic stability of turbines located in subsequent rows downstream on the farm.

Baruah, Apurva (ORCID:0000000252354068)↗
DOE OSTI2020

Applying Two Active Acoustic Technologies to Document Presence of Large Marine Animal Targets at a Marine Renewable Energy Site

Coastal regions are highly used by humans. The growing marine renewable energy (MRE) industry will add to existing anthropogenic pressures in these regions. Regulatory bodies require animal risk assessment before new industrial activities can progress, and MRE is no exception. Preliminary data of marine mammal use of an MRE device deployment location could be informative to permitting. A combination of downlooking hydroacoustics using an echosounder and acoustic camera (imaging sonar) was used to provide a number of large targets (proxy for large fish and marine mammals) in an area of interest for MRE tidal turbine deployment in Western Passage, Maine, USA. Data were collected in May, June, August, and September of 2010 and 2011. Of the nine large targets confirmed to be animals, eight were porpoises and one was a shark. Few large targets were observed in May and June, with the majority (90%) being present in August and September of both years. The most large targets were observed when tidal current speed was less than 1 m·s−1. These data provide a preliminary assessment of large targets in a single location over sixteen 24-h surveys. The aforementioned methodology could be used for future pre- and post-installation assessments at MRE device deployment locations. Their use in concert with visual and passive acoustic monitoring can provide water depth usage by marine mammals, which is a metric that is difficult to assess with passive acoustic and visual techniques.

Staines, Garrett↗
DOE OSTI2021

Displacement Analysis of Geothermal Field Based on PSInSAR And SOM Clustering Algorithms A Case Study of Brady Field, Nevada—USA

The availability of free and high temporal resolution satellite data and advanced SAR techniques allows us to analyze ground displacement cost-effectively. Our aim was to properly define subsidence and uplift areas to delineate a geothermal field and perform time-series analysis to identify temporal trends. A Persistent Scatterer Interferometry (PSI) algorithm was used to estimate vertical displacement in the Brady geothermal field located in Nevada by analyzing 70 Sentinel-1A Synthetic-Aperture Radar (SAR) images, between January 2017 and December 2019. To classify zones affected by displacement, an unsupervised Self-Organizing Map (SOM) algorithm was applied to classify points based on their behavior in time, and those clusters were used to determine subsidence, uplift, and stable regions automatically. Finally, time-series analysis was applied to the clustered data to understand the inflection dates. The maximum subsidence is –19 mm/yr with an average value of –6 mm/yr within the geothermal field. The maximum uplift is 14 mm/yr with an average value of 4 mm/yr within the geothermal field. The uplift occurred on the NE of the field, where the injection wells are located. On the other hand, subsidence is concentrated on the SW of the field where the production wells are located. The coupling of the PSInSAR and the SOM algorithms was shown to be effective in analyzing the direction and pattern of the displacements observed in the field.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Lightning Interferometry with the Long Wavelength Array

The Long Wavelength Array is a radio telescope array located at the Sevilleta National Wildlife Refuge in La Joya, New Mexico, well suited and situated for the observation of lightning. The array consists of 256 high-sensitivity dual polarization antennas arranged in a 100 m diameter. This paper demonstrates some of the capabilities that the array brings to the study of lightning. Once 32 or more antennas are used to image lightning radio sources, virtually every integration period longer than the impulse response of the array includes at least one identifiable lightning emitter, independent of the integration period used. The use of many antennas also allows multiple simultaneous lightning radio sources to be imaged at sub-microsecond timescales; for the flash examined, 51% of the images contained more than one lightning source. Finally, by using many antennas to image lightning sources, the array is capable of locating sources fainter than the galactic background radio noise level, yielding possibly the most sensitive radio maps of lightning to date. This incredible sensitivity enables, for the first time, the emissions originating from the positive leader tips of natural in-cloud lightning to be detected and located. The tip emission is distinctly different from needle emission and is most likely due to positive breakdown.

Stock, Michael (ORCID:0000000193336790)↗
DOE OSTI2020

A Sheaf Theoretical Approach to Uncertainty Quantification of Heterogeneous Geolocation Information

Integration of multiple, heterogeneous sensors is a challenging problem across a range of applications. Prominent among these are multi-target tracking, where one must combine observations from different sensor types in a meaningful and efficient way to track multiple targets. Because different sensors have differing error models, we seek a theoretically justified quantification of the agreement among ensembles of sensors, both overall for a sensor collection, and also at a fine-grained level specifying pairwise and multi-way interactions among sensors. We demonstrate that the theory of mathematical sheaves provides a unified answer to this need, supporting both quantitative and qualitative data. Furthermore, the theory provides algorithms to globalize data across the network of deployed sensors, and to diagnose issues when the data do not globalize cleanly. We demonstrate and illustrate the utility of sheaf-based tracking models based on experimental data of a wild population of black bears in Asheville, North Carolina. A measurement model involving four sensors deployed among the bears and the team of scientists charged with tracking their location is deployed. This provides a sheaf-based integration model which is small enough to fully interpret, but of sufficient complexity to demonstrate the sheaf’s ability to recover a holistic picture of the locations and behaviors of both individual bears and the bear-human tracking system. A statistical approach was developed in parallel for comparison, a dynamic linear model which was estimated using a Kalman filter. This approach also recovered bear and human locations and sensor accuracies. When the observations are normalized into a common coordinate system, the structure of the dynamic linear observation model recapitulates the structure of the sheaf model, demonstrating the canonicity of the sheaf-based approach. However, when the observations are not so normalized, the sheaf model still remains valid.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Graphical Optimization of Spectral Shift Reconstructions for Optical Backscatter Reflectometry

Optical backscatter reflectometry (OBR) is an interferometric technique that can be used to measure local changes in temperature and mechanical strain based on spectral analyses of backscattered light from a singlemode optical fiber. The technique uses Fourier analyses to resolve spectra resulting from reflections occurring over a discrete region along the fiber. These spectra are cross-correlated with reference spectra to calculate the relative spectral shifts between measurements. The maximum of the cross-correlated spectra—termed quality—is a metric that quantifies the degree of correlation between the two measurements. Recently, this quality metric was incorporated into an adaptive algorithm to (1) selectively vary the reference measurement until the quality exceeds a predefined threshold and (2) calculate incremental spectral shifts that can be summed to determine the spectral shift relative to the initial reference. Using a graphical (network) framework, this effort demonstrated the optimal reconstruction of distributed OBR measurements for all sensing locations using a maximum spanning tree (MST). By allowing the reference to vary as a function of both time and sensing location, the MST and other adaptive algorithms could resolve spectral shifts at some locations, even if others can no longer be resolved.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Integrated Land Suitability Assessment for Depots Siting in a Sustainable Biomass Supply Chain

A sustainable biomass supply chain would require not only an effective and fluid transportation system with a reduced carbon footprint and costs, but also good soil characteristics ensuring durable biomass feedstock presence. Unlike existing approaches that fail to account for ecological factors, this work integrates ecological as well as economic factors for developing sustainable supply chain development. For feedstock to be sustainably supplied, it necessitates adequate environmental conditions, which need to be captured in supply chain analysis. Using geospatial data and heuristics, we present an integrated framework that models biomass production suitability, capturing the economic aspect via transportation network analysis and the environmental aspect via ecological indicators. Production suitability is estimated using scores, considering both ecological factors and road transportation networks. These factors include land cover/crop rotation, slope, soil properties (productivity, soil texture, and erodibility factor) and water availability. This scoring determines the spatial distribution of depots with priority to fields scoring the highest. Two methods for depot selection are presented using graph theory and a clustering algorithm to benefit from contextualized insights from both and potentially gain a more comprehensive understanding of biomass supply chain designs. Graph theory, via the clustering coefficient, helps determine dense areas in the network and indicate the most appropriate location for a depot. Clustering algorithm, via K-means, helps form clusters and determine the depot location at the center of these clusters. An application of this innovative concept is performed on a case study in the US South Atlantic, in the Piedmont region, determining distance traveled and depot locations, with implications on supply chain design. The findings from this study show that a more decentralized depot-based supply chain design with 3depots, obtained using the graph theory method, can be more economical and environmentally friendly compared to a design obtained from the clustering algorithm method with 2 depots. In the former, the distance from fields to depots totals 801,031,476 miles, while in the latter, it adds up to 1,037,606,072 miles, which represents about 30% more distance covered for feedstock transportation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation

Abstract Detecting substructure within strongly lensed images is a promising route to shed light on the nature of dark matter. However, it is a challenging task, which traditionally requires detailed lens modeling and source reconstruction, taking weeks to analyze each system. We use machine learning to circumvent the need for lens and source modeling and develop a neural network to both locate subhalos in an image as well as determine their mass using the technique of image segmentation. The network is trained on images with a single subhalo located near the Einstein ring across a wide range of apparent source magnitudes. The network is then able to resolve subhalos with masses m ≳ 10 8.5 M ⊙ . Training in this way allows the network to learn the gravitational lensing of light, and, remarkably, it is then able to detect entire populations of substructure, even for locations further away from the Einstein ring than those used in training. Over a wide range of the apparent source magnitude, the false-positive rate is around three false subhalos per 100 images, coming mostly from the lightest detectable subhalo for that signal-to-noise ratio. With good accuracy and a low false-positive rate, counting the number of pixels assigned to each subhalo class over multiple images allows for a measurement of the subhalo mass function (SMF). When measured over three mass bins from 10 9 –10 10 M ⊙ the SMF slope is recovered with an error of 36% for 50 images, and this improves to 10% for 1000 images with Hubble Space Telescope-like noise.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Low-energy Electron-track Imaging for a Liquid Argon Time-projection-chamber Telescope Concept Using Probabilistic Deep Learning

The GammaTPC is an MeV-scale single-phase liquid argon time-projection-chamber gamma-ray telescope concept with a novel dual-scale pixel-based charge-readout system. It promises to enable a significant improvement in sensitivity to MeV-scale gamma rays over previous telescopes. The novel pixel-based charge readout allows for imaging of the tracks of electrons scattered by Compton interactions of incident gamma rays. The two primary contributors to the accuracy of a Compton telescope in reconstructing an incident gamma-ray’s original direction are its energy and position resolution. In this work, we focus on using deep learning to optimize the reconstruction of the initial position and direction of electrons scattered in Compton interactions, including using probabilistic models to estimate predictive uncertainty. We show that the deep-learning models are able to predict locations of Compton scatters of MeV-scale gamma rays from simulated 500 μm pixel-based data to better than 1 mm rms error and are sensitive to the initial direction of the scattered electron. We compare and contrast different deep-learning uncertainty estimation algorithms for reconstruction applications. Additionally, we show that event-by-event estimates of the uncertainty of the locations of the Compton scatters can be used to select those events that were reconstructed most accurately, leading to improvement in locating the origin of gamma-ray sources on the sky.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

High-resolution Giant Metrewave Radio Telescope H I 21 cm Imaging of the Host Galaxy of FRB 20250316A

We report Giant Metrewave Radio Telescope (GMRT) H I 21 cm imaging of NGC 4141, the host galaxy of FRB 20250316A at z = 0.0063. Our GMRT H I 21 cm images have spatial resolutions, at z ≈ 0.0063, of ≈0.48–8.0 kpc, and provide evidence for (i) a companion galaxy, LEDA 2582852, to the southwest, (ii) a nearby (27 kpc distant) H i cloud to the southwest, (iii) disturbances in the H I distributions of both NGC 4141 and LEDA 2582852, and (iv) high H I column densities in the southwestern outskirts of NGC 4141. A Sloan Digital Sky Survey spectrum yields a low metallicity and a high star formation rate (SFR) surface density in the southwestern disk of NGC 4141, and an Hα-based SFR estimate that is significantly higher than that at the same location from the Galaxy Evolution Explorer near-ultraviolet image, indicating a recent burst of star formation. The total SFR of NGC 4141 is also found to be higher via the Hα line than from the 1.4 GHz radio continuum. The above evidence indicates that NGC 4141 has recently (within the last ≈3 Myr) acquired metal-poor gas, via either a merger or accretion, that resulted in the southwestern starburst and that may also have triggered large-scale star-formation activity in NGC 4141, resulting in the formation of the stellar progenitor of FRB 20250316A and the other transients. Our highest-resolution (480 pc) GMRT H I 21 cm image finds no H I 21 cm emission from the location of FRB 20250316A or the nearby star-forming region, suggesting that most of the H I here has been either ionized or converted into the molecular phase. Our nondetection of continuum emission at the location of FRB 20250316A yields the 3σ upper limit νL 1.38 GHz < 4.4 × 10 34 erg s −1 on the 1.38 GHz radio luminosity of a putative persistent radio source associated with FRB 20250316A, one of the strongest constraints on the radio luminosity of such an associated persistent radio source.

Kaur, Balpreet [Pune University (India)] (ORCID:00↗