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DOE OSTI2021

Linking resource availability to pantropical forest canopy resistance and resilience to cyclone disturbance

Statement of purpose: Tropical cyclones are intensifying and occurring at higher latitudes in recent decades, but the mechanisms underpinning the resistance (ability to withstand disturbance-induced change) and resilience (pace of return to pre-disturbance reference values) of tropical forests to cyclones remains largely unexplored at the pantropical scale. We conducted a meta-analysis to investigate the role of soil resource availability (i.e., total soil phosphorus concentration) in mediating site-level forest canopy resistance and resilience to cyclones pan-tropically. We evaluated cyclone-induced and post-cyclone litterfall mass (g/m2/day), phosphorus (P) and nitrogen (N) fluxes (mg/m2/day), as well as concentrations (mg/g) across 73 case studies in Australia, Guadeloupe, Hawaii, Mexico, Puerto Rico, and Taiwan. The dataset zip file includes three data and two metadata files: - The compiled Litterfall Mass Flux data from tropical forests across the globe prior to and after varying tropical cyclone disturbances are provided in Litterfall_Mass.csv. This data file also includes site location, geographical characteristics, elevation, soil phosphorus concentration, geology, and several variables related to each tropical cyclone disturbance. - The compiled Litterfall Nitrogen and Phosphorus Flux data from tropical forests across the globe prior to and after varying tropical cyclone disturbances are provided in Litterfall_Nutrients.csv. This data file also includes site location, geographical characteristics, elevation, soil phosphorus concentration, geology, and several variables related to each tropical cyclone disturbance. - Tropical cyclone track data compiled from HURDAT2 and IBTrACS databases and used as input in the HURRECON model (https://github.com/hurrecon-model/HurreconR) to generate wind data is provided in hurdat2-1851-2019-052520.txt. - The metadata file (Metadata_Meta-analysis_Litterfall-Mass.pdf) has the complete information on each variable included in the Litterfall_Mass.csv dataset, the data sources, and data processing information. - The metadata file (Metadata_Meta-analysis_Litterfall-Nutrients.pdf) has the complete information on each variable included in the Litterfall_Nutrients.csv dataset, the data sources, and data processing information.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Single-Axis Tracker Control Optimization Potential for the Contiguous United States

Conventional tracker control algorithms maximize collection of direct irradiance with no regard for collection of diffuse irradiance. Therefore, a tracker control algorithm that optimizes for maximal total irradiance, not just direct, might realize improved insolation collection. Using weather data gridded at 0.25° by 0.25° latitude/longitude spacing covering the contiguous United States, we evaluate the insolation gain of two alternative control algorithms optimized for improved total irradiance collection in monofacial arrays and present annual and monthly geographic heatmaps showing the gains across the contiguous United States. Certain locations show potential annual insolation gains approaching 1.0%, but most locations with recently-built tracker systems show annual gains between 0.1% and 0.4%. We also demonstrate a relationship between a climate's annualized diffuse insolation fraction and its potential tracker optimization insolation gain.

diffuse↗

DOE OSTI2022

Single-Axis Tracker Control Optimization Potential for the Contiguous United States: Preprint

diffuse↗

DOE OSTI2024

Review of In Situ Sensing for Directed Energy Deposition for Industrial Part Quality Assessment

As the use additive manufacturing (AM) processes continues to grow in critical industries, improved quality assurance methods are becoming increasingly sought after for qualification and certification of AM components. Traditional nondestructive evaluation of printed components is often unable to supply the required confidence in print quality to justify qualification and certification, but the layer-by-layer nature of AM provides unprecedented opportunities for in situ quality inspection. This document summarizes recent developments in process monitoring research specifically related to Directed Energy Deposition (DED). Particular attention is given to three aspects of the highlighted manuscripts: (1) the type of sensors used, (2) features extracted from each sensor modality, and (3) analysis of extracted features for AM quality assessment. Based on the review of the state-of-the-art, several observations have been made. First, none of the reviewed works have applied their trained models to real part geometries, with many of the works relying on single track experiments, thin-walled structures, and cubes. Similarly, there have not been any works demonstrating model generalizability, i.e., a model trained on data from one build allows for fruitful analysis of data from another build. Many works used machine learning techniques to distinguish different process regimes (i.e., normal, keyholing, lack-of-fusion), but very few papers have investigated stochastic variation in an already “optimized” process. Sensor fusion approaches are also limited in the DED sensing literature, but the few works that have employed such techniques have demonstrated the benefits. Finally, registration of in situ data to the build coordinate system is of paramount importance to producing industrially relevant in situ monitoring systems. Data registration allows direct correlations between process anomalies detected in the process monitoring data to localized departures in part quality, but such techniques are generally lacking in the current literature.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Measuring and Modeling Bifacial Technologies

Measuring and modeling bifacial technologies is of interest for new system design, capacity testing, and performance evaluation. The bifacial PV field at NREL has been gathering data for different bifacial technologies and sensors for the last 2.5 years, making it possible to compare bifacial performance with monofacial counterparts, as well as experimental demonstrations of best locations to place sensors to match modeled and perceived irradiance non-uniformity on the rear of the bifacial modules. This presentation summarizes performance, degradation, sensor position for module edge effects and the impact of row edge effects, and the roadmap for NREL bifacial modeling tools.

bifacial PV↗

DOE OSTI2022

Light-Level Geolocation of the LANL Population of Western Bluebirds

Options for tracking and reconstructing animal movement are increasingly accessible due to rapidly decreasing costs, smaller sizes, and a proliferation of analytical inference techniques (Rutz and Hays, 2009; Wikelski et al., 2007). A wide variety of options exists for determining movement patterns of animals—from high-resolution pinpoint ARGOS satellite tags to time-consuming and logistically challenging radio telemetry. One option, light-level geolocation, offers small, affordable devices with long battery lives known as global location sensors (GLS), making them ideal for gathering preliminary migratory data. Geolocation works by inferring patterns of animal movement from light-level transitions between day and night (Hill and Braun, 2001). The recent advances in tracking technologies allow biologists to zoom in on migratory behavior, delineating heretofore-unidentified intraspecific migratory behavior (Delmore et al., 2012). The ubiquity of migratory divides (populations within a species that exhibit different migratory patterns) remains largely unknown, but their existence can lead to favorable demographic metrics (e.g., genetic diversity) in conservation contexts (Møller et al., 2011) and necessitate the development of population-specific conservation plans (Delmore et al., 2012). Delimiting the populations within a species that migrate and the extent of their migrations relative to individuals that remain resident across a species’ range has important evolutionary, ecological, and conservation implications. Within this context, Los Alamos National Laboratory (LANL) biologists have leveraged the local Avian Nestbox Network (ANN; Fair and Myers, 2002) to evaluate the migratory behaviors of a common local bird species, the Western Bluebird (Sialia mexicana).

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Contributions From Cloud Morphological Changes to the Interannual Shortwave Cloud Feedback Based on MODIS and ISCCP Satellite Observations

The surface temperature-mediated change in cloud properties, referred to as the cloud feedback, continues to dominate the uncertainty in climate projections. A larger number of contemporary global climate models (GCMs) project a higher degree of warming than the previous generation of GCMs. This greater projected warming has been attributed to a less negative cloud feedback in the Southern Ocean. Here, we apply a novel “double decomposition method” that employs the “cloud radiative kernel” and “cloud regime” concepts, to two data sets of satellite observations to decompose the interannual cloud feedback into contributions arising from changes within and shifts between cloud morphologies. Our results show that contributions from the latter to the cloud feedback are large for certain regimes. We then focus on interpreting how both changes within and between cloud morphologies impact the shortwave cloud optical depth feedback over the Southern Ocean in light of additional observations. Results from the former cloud morphological changes reveal the importance of the wind response to warming increases low- and mid-level cloud optical thickness in the same region. Results from the latter cloud morphological changes reveal that a general shift from thick storm-track clouds to thinner oceanic low-level clouds contributes to a positive feedback over the Southern Ocean that is offset by shifts from thinner broken clouds to thicker mid- and low-level clouds. Our novel analysis can be applied to evaluate GCMs and potentially diagnose shortcomings pertaining to their physical parameterizations of particular cloud morphologies.

58 GEOSCIENCES↗

DOE OSTI2024

Considerations for Introducing Artificial Intelligence into Nuclear Power Plants

Advanced computational tools and techniques such as artificial intelligence and machine learning (AI/ML) can transform the nuclear power industry. This is necessary given that the economic viability of the existing fleet is in jeopardy and its labor-centric approach to operations and maintenance. Currently, AI/ML research is being undertaken for reactor system design and analysis including fault and accident prognosis, nuclear risk analysis such as plant safety and security evaluation, and plant operations and maintenance including predictive maintenance. Applications include both existing and advanced reactor technologies with the aim of improving operational and business efficiencies. Most every aspect of the organization can benefit, from instrumentation and control, to work planning, to human-machine interactions and business management. AI/ML in nuclear can simplify complex problems and produce more effective decision-making. Nonetheless, careful consideration must be given to the implementation of an AI/ML initiative. The aims of this research are to 1) review barriers to AI/ML adoption within the nuclear power industry, and 2) suggest potential solutions. These barriers are organized along five distinct categories (Figure 1) that are interconnected. The first are historical barriers that track the industry’s development over the decades including worldwide nuclear events that shaped public perceptions. The resulting federal scrutiny and intense safety culture that emerged are discussed. Technical barriers to AI/ML adoption are considerable, and include data privacy concerns, data governance, and the current lack of AI/ML expert knowledge at the plants. The main business case barrier remains cost, but an absence of an industry-wide vision and wide-scale adoption also produces reluctance. Stakeholder readiness is reviewed with special attention given to regulatory readiness. The 5-year strategic plan for AI readiness recently published by the U.S. Nuclear Regulatory Commission is highlighted. Last, adoption barriers at the user level are addressed including the importance of user experience and explainable AI. The AI adoption barriers described here are inter-related and ideally should be addressed in a holistic fashion.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Evaluation of PBR Spent Fuel Criticality and Dose Rate Compliance for Storage and Transportation

Spent tri-structural isotropic (TRISO)–based fuels have a strong track record in storage and transportation without documented incidents. This work seeks to reduce uncertainty to aid in more informed spent fuel management of TRISO-based fuels by modeling both fresh and spent pebble bed reactor (PBR) fuel and comparing the results to the regulatory standards from 10 CFR 71. SCALE was used for all modeling due to it having fast and accurate methods for handling PBR fuel modeling, as well as having an efficient method for shielding calculations in monaco with automated variance reduction using importance calculations (MAVRIC), which utilizes the consistent adjoint-driven importance sampling (CADIS) and the forward-weighted consistent adjoint-driven importance sampling (FW-CADIS) methods. KENO-VI was used for all criticality calculations, TSUNAMI was used for uncertainty quantification on k-effective, TRITON and the Oak Ridge isotope generation code (ORIGEN) were both used for depletion of the fuel, and MAVRIC was used for shielding calculations. For criticality assessments, this study focused on the requirement that the value of the neutron multiplication factor, k-effective (k-eff), would not exceed a peak value of 0.95, including uncertainty, with 95% confidence. Criticality was initially examined by modeling fresh fuel from three different designs—HTR-10 fuel, PBMR-400 fuel, and demonstration fuel representative of a TRISO-fueled modern high-temperature gas reactor (HTGR) design, henceforth referred to as Demo HTGR—and placing them into various sized containers with conditions described in 10 CFR 71 to quantify the peak k-eff state. When the peak value of 0.95 k-eff was exceeded, mitigation methods were examined in those scenarios. Burnup credit, pebble displacement in areas of strong neutron multiplication, and random pebble replacement using pebbles of various compositions and replacement fractions were examined. In summary, the criticality of PBR fuels can be well accounted for by restricting container size, taking credit for burnup, or by displacing/replacing pebbles. Uncertainty of the k-eff due to nuclear data uncertainties was recorded at ~0.6644%Δk/k, or roughly 664% mil (pcm). The nuclear data–induced uncertainty was relatively small and should not require significant modification in the design to be accounted for. Revisions to the evaluated nuclear data file values have been shown to have a larger impact than nuclear data–induced uncertainty. For dose rate aspects, U.S. Nuclear Regulatory Commission regulations require a maximum dose rate of 10 millirem per hour (mrem/h) at 2 meters. In examining the dose rate behavior of spent PBR fuel, the representative Demo HTGR fuel was modeled exclusively due to it possessing the highest target burnup of the examined fuels. Equilibrium cycle modeling methods were used to produce a higher-fidelity discharge isotopic composition than simple assumptions, such as reflected pebbles. The discharge composition was used as a source term in the fixed-source transport shielding calculations, and dose rates were calculated at 2 m for the shortest possible cooling time. The low concentration of fuel material led to dose rates that were in line with regulatory limits, despite the high burnup when compared to traditional light water reactor fuels. In conclusion, the methods employed in this study would require more work to further verify and validate and are limited to the criticality and dose rate analyses performed.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Deep learning-based spatio-temporal estimate of greenhouse gas emissions using satellite data

Accurate estimation of greenhouse gases (GHGs) emissions is very important for developing mitigation strategies to climate change by controlling and reducing GHG emissions. This project aims to develop multiple deep learning approaches to estimate anthropogenic greenhouse gas emissions using multiple types of satellite data. NO2 concentration is chosen as an example of GHGs to evaluate the proposed approach. Two sentinel satellites (sentinel-2 and sentinel-5P) provide multiscale observations of GHGs from 10-60m resolution (sentinel-2) to ~kilometer scale resolution (sentinel-5P). Among multiple deep learning (DL) architectures evaluated, two best DL models demonstrate that key features of spatio-temporal satellite data and additional information (e.g., observation times and/or coordinates of ground stations) can be extracted using convolutional neural networks and feed forward neural networks, respectively. In particular, irregular time series data from different NO 2 observation stations limit the flexibility of long short-term memory architecture, requiring zero-padding to fill in missing data. However, deep neural operator (DNO) architecture can stack time-series data as input, providing the flexibility of input structure without zero-padding. As a result, the DNO outperformed other deep learning architectures to account for time-varying features. Overall, temporal patterns with smooth seasonal variations were predicted very well, while frequent fluctuation patterns were not predicted well. In addition, uncertainty quantification using conformal inference method is performed to account for prediction ranges. Overall, this research will lead to a new groundwork for estimating greenhouse gas concentrations using multiple satellite data to enhance our capability of tracking the cause of climate change and developing mitigation strategies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Investigation and Diagnosis of Faulty Data Channels in CMS Outer Tracker Module Testing

The High-Luminosity Large Hadron Collider (HL-LHC) is currently undergoing upgrades to improve its luminosity. In parallel, this requires an upgrade to the Compact Muon Solenoid (CMS)’s Outer Tracker, consisting of Pixel-Strip (PS) and Strip-Strip (2S) modules that can accurately track the path of charged particles originating from the collisions. It follows that such complex modules call for extensive testing, requiring a sophisticated Data Acquisition (DAQ) system that can perform specific tests to assess their performance. In addition, errors caused by the hardware of a given testing station, and its associated data channel, need to be accurately identified to guarantee proper testing of modules. We have developed a software extension to the Phase-II Outer Tracker Analyzer of Test Outputs (POTATO), which is a specialized software designed to analyze and grade all of the module tests through a centralized database. This extension categorizes and analyzes module test results by its station and data channel. Its analysis can be used to identify trends in grading that indicate issues in these channels’ grading process rather than in the individual modules. This poster shows our methodology and results for identifying faulty data channels. Using this extension, we can quickly diagnose and address problems in our DAQ system, ensuring proper evaluation corrections for each module.

Chen, Angus [Fermilab]↗

DOE OSTI2026

Investigation and Diagnosis of Faulty Data Channels in CMS Outer Tracker Module Testing

Chen, Angus [Fermilab]↗

DOE OSTI2025

Evaluation of Properties for Microsample Identification

A study was conducted to determine if individual particle characteristics could be used to identify particles of interest, sub-samples, from bulk post-detonation debris. Three archived post-detonation debris samples were used for this effort. Particles from these samples were identified as active (produced fission tracks), and inactive (did not produce fission tracks), as the first defining characteristic. Morphology was the secondary characteristic to select particles for further study, i.e. spherical/non-spherical. Once particles were identified and isolated, they were characterized by optical microscopy for size in µm, number of fission tracks, morphology, transmitted light color, and reflected light color. Particles were then analyzed by scanning electron microscopy for morphology, elemental content, and compound identification. Raman spectroscopy was attempted on five particles with indeterminate results due to environmental mixing (heterogeneity) during the events of particle formation. Once all non-destructive analyses were completed all particles were analyzed by thermal ionization mass spectrometry to determine isotopic atom percents of plutonium and uranium, and an estimate of atoms of plutonium and uranium in each particle. An estimate of the ratio of uranium to plutonium was also obtained (U/Pu). Data analytics of the data from the particles showed that combining characteristics of the particles have a high probability of identifying particles of interest from bulk post-detonation debris samples. Please note that this version of the report is an abridged version of the full report (Wagnon et al. 2025) that has been edited to be appropriate for public release.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2023

Quantifying airborne fraction trends and the destination of anthropogenic CO 2 by tracking carbon flows in a simple climate model

Abstract Atmospheric carbon dioxide (CO 2 ) concentrations have increased as a direct result of human activity and are at their highest level over the last 2 million years, with profound impacts on the Earth system. However, the magnitude and future dynamics of land and ocean carbon sinks are not well understood; therefore, the amount of anthropogenic fossil fuel emissions that remain in the atmosphere (the airborne fraction) is poorly constrained. This work aims to quantify the sources and controls of atmospheric CO 2 , the fate of anthropogenic CO 2 over time, and the likelihood of a trend in the airborne fraction. We use Hector v3.0, a coupled simple climate and carbon cycle model with the novel ability to explicitly track carbon as it flows through the Earth system. We use key model parameters in a Monte Carlo analysis of 15 000 model runs from 1750 to 2300. Results are filtered for physical realism against historical observations and CMIP6 projection data, and we calculate the relative importance of parameters controlling how much anthropogenic carbon ends up in the atmosphere. Modeled airborne fraction was roughly 52%, consistent with observational studies. The overwhelming majority of model runs exhibited a negative trend in the airborne fraction from 1960–2020, implying that current-day land and ocean sinks are proportionally taking up more carbon than the atmosphere. However, the percentage of atmospheric CO 2 derived from anthropogenic origins can be much higher because of Earth system feedbacks. We find it peaks at over 90% between 2010–2050. Moreover, when looking at the destination of anthropogenic fossil fuel emissions, only a quarter ends up in the atmosphere while more than half of emissions are taken up by the land sink on centennial timescales. This study evaluates the likelihood of airborne fraction trends and provides insights into the dynamics of anthropogenic CO 2 in the Earth system.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Evaluation of the Four-Dimensional Ensemble-Variational Hybrid Data Assimilation with Self-Consistent Regional Background Error Covariance for Improved Hurricane Intensity Forecasts

The feasibility of a hurricane initialization framework based on the Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational (GSI-4DEnVar) hybrid data assimilation system for the Hurricane Weather Research and Forecasting model (HWRF) model is evaluated in this study. The system considers the temporal evolution of error covariances via the use of four-dimensional ensemble perturbations that are provided by high-resolution, self-consistent HWRF ensemble forecasts. It is different from the configuration of the GSI-based three-dimensional ensemble-variational (GSI-3DEnVar) hybrid data assimilation system, similar to that used in the operational HWRF, which employs background error covariances provided by coarser-resolution global ensembles from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) ensemble Kalman filtering data assimilation system. In addition, our proposed initialization framework discards the empirical intensity correction in the vortex initialization package that is employed by the GSI-3DEnVar initialization framework in operational HWRF. Data assimilation and numerical simulation experiments for Hurricanes Joaquin (2015), Patricia (2015), and Matthew (2016) are conducted during their intensity changes. The impacts of two initialization frameworks on the HWRF analyses and forecasts are compared. It is found that GSI-4DEnVar leads to a reduction in track, minimum sea level pressure (MSLP), and maximum surface wind (MSW) forecast errors in all of the HWRF simulations, compared with the GSI-3DEnVar initialization framework. With assimilating high-resolution observations within the hurricane inner-core region, GSI-4DEnVar can produce the initial hurricane intensity reasonably well without the empirical vortex intensity correction. Further diagnoses with Hurricane Joaquin indicate that GSI-4DEnVar can significantly alleviate the imbalances in the initial conditions and enhance the performance of the data assimilation and subsequent hurricane intensity and precipitation forecasts

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Reducing Cost of Chlorinated Volatile Organic Compound Remediation by Transitioning from Active to Passive Soil Vapor Extraction - 20157

Areas of high chlorinated volatile organic compound (cVOC) contamination at the Savannah River Site (SRS) have been undergoing remediation via soil vapor extraction, sometimes coupled with thermal treatments to enhance extraction rates. These active systems are effective in removing large amounts of contaminant mass from the subsurface and mitigating the impacts to groundwater. However, as extraction rates decline, costs must be evaluated with respect to the benefit of continued active operation. A decision framework for identifying conditions when a transition to a more passive remediation is appropriate has been developed with state and federal regulatory agencies. Two remediation areas have recently been transitioned from active soil vapor extraction (ASVE) to passive soil vapor extraction (PSVE) at the SRS. Performance evaluation goals including plume stabilization, overall mass removal trends, environmental sustainability and costs were considered in transitioning from active remediation to passive technologies at both sites. At the Dynamic Underground Stripping (DUS) project at the M-Area Settling Basin, ASVE was combined with steam injection to extract cVOCs during active operations. Steam injection occurred from September 2005 to September 2009. DUS utilized 63 steam injection wells, 34 active vapor extraction wells, and 3 active soil vapor extraction units (SVEUs). Two active SVEUs had 60 horsepower blowers; the third had a 25-horsepower blower. Mass removal was closely tracked during DUS operations; over 181,437 kilograms (400,000 pounds) of cVOCs were removed while active steam injection occurred. After steaming was stopped, ASVE continued. The 34 active wells were evaluated in 2012. The ASVE wells were grouped into categories of high, medium, and low extraction rates. High producing wells remained connected to a single active SVEU. Low producing wells were abandoned, and the medium producing wells were transitioned to PSVE (Microblowers{sup TM}). Microblowers{sup TM} utilize a dedicated blower per well and are solar powered. This passive technology provides energy, maintenance, and operation costs savings while still providing an efficient reduction in cVOC migration to groundwater. In 2018, the remaining ASVE wells were evaluated again. The purpose of this testing was to identify which wells removed the most mass. An optimal well configuration was determined. The criteria to discontinue ASVE was removal of less than 18 kilograms (40 pounds) per week of cVOCs. After 3 months of shutdown (rebound conditions), 2.7 kilograms (5.9 pounds) of cVOCs per week were being removed. Data from the rebound test justified ending ASVE and transitioning wells with higher extraction rates to PSVE. Wells that had depleted the cVOC mass within their zone of influence were abandoned. Performance data from existing PSVE wells justified ending PSVE at wells with depleted extraction rates. Currently the system has 16 PSVE wells operating. Another ASVE system was being used to treat cVOC contaminated soil at the A-Area Miscellaneous Rubble Pile (AMRP) at SRS. System operation began in 2004, with 7 ASVE wells connected to a 60- horsepower blower. Mass removal rates and contaminant concentrations remained consistently low over the ASVE lifespan at AMRP. This indicated that mass removal was diffusion limited. With this data, the 7 ASVE wells were transitioned to PSVE in 2017. Twelve pressure monitoring points were also transitioned to PSVE wells. AMRP currently has 19 PSVE wells operating. Both transitions from active to passive remediation had concurrence from the United States Environmental Protection Agency and the South Carolina Department of Health and Environmental Control. These transitions ensure that only the necessary amount of energy is being exerted to remediate the environment. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Fermi -LAT follow-up observations in seven years of real-time high-energy neutrino alerts

The realtime program for high-energy neutrino track events detected by the IceCube South Pole Neutrino Observatory releases alerts to the astronomical community with the goal of identifying electromagnetic counterparts to astrophysical neutrinos. Gamma-ray observations from theFermi-Large Area Telescope (LAT) enabled the identification of the flaring gamma-ray blazar TXS 0506+056 as a likely counterpart to the neutrino event IC-170922A. By continuously monitoring the gamma-ray sky,Fermi-LAT plays a key role in the identification of candidate counterparts to realtime neutrino alerts. In this paper, we present theFermi-LAT strategy for following up high-energy neutrino alerts applied to seven years of IceCube data. Right after receiving an alert, a search is performed in order to identify gamma-ray activity from known and newly detected sources that are positionally consistent with the neutrino localization. In this work, we study the population of blazars found in coincidence with high-energy neutrinos and compare them to the full population of gamma-ray blazars detected byFermi-LAT. We also evaluate the relationship between the neutrino and gamma-ray luminosities, finding different trends between the two blazar classes BL Lacs and flat-spectrum radio quasars.

Astronomy & Astrophysics↗

DOE OSTI2023

Evaluating Ion Accumulation and Storage in Traveling Wave Based Structures for Lossless Ion Manipulations

Structures for lossless ion manipulations (SLIM) technology has demonstrated high resolving power ion mobility separation and flexibility to integrate complex ion manipulations into a single experimental platform. To enable IMS separations, trapping/accumulating ions inside SLIM (or in-SLIM) prior to injection of a packet for separations provides ease of operation and reduces the need for dedicated ion traps external to SLIM. To fully characterize the ion accumulation process, we have evaluated the effect of TW amplitudes, ion collection times, and storage times on the “in-SLIM” accumulation process. The study utilized a SLIM module comprising 5 distinct tracks, each with a specific ion accumulation configuration. The effect of the TW conditions on the accumulation process was investigated for a 3-peptide mixture: kemptide, angiotensin II, and neurotensin at a TW speed of 106 m/s. The effect of ion accumulation time/collection time and storage time was investigated, in addition to TW amplitude. Overall, the signal of the analyte ions increased when the ion collection time increased from 49 to 163 ms but decreased when the ion collection time increased further to 652 ms due to the space charge effects. Ion losses were observed at high TW amplitudes (e.g., 15 V p–p and 20 V p–p ). In addition, under space charge conditions (e.g., collection times of 163 and 652 ms), the signal of the analyte ions decreased with an increase in storage times for all TW amplitudes applied to the trapping region. For ion accumulation, the data indicate that gentler TW conditions must be utilized to minimize ion losses and fragments to benefit from the “in-SLIM” accumulation process. Finally, wider SLIM tracks provided better performance than those with narrower tracks.

59 BASIC BIOLOGICAL SCIENCES↗