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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Smart Planning for Radioactive Source Transport Advanced Tools for Increased Safety and Efficiency

End-of-life (EOL) management of high-activity radioactive sources is made uniquely challenging by the inherent risks associated with storage and transportation of these sources, the complex logistics involved, and the strict requirements for regulatory compliance. Traditional methods lack comprehensive tools for accurate site assessments and precision planning for the transportation of radioactive sources. They also frequently fail to provide the adaptability required to consider diverse operational environments, resulting in inefficiencies and potential safety concerns. This paper introduces a novel software solution developed to address these issues by integrating advanced technologies such as light detection and ranging (LiDAR)-based 3D environment modeling, smart dynamic route planning, and customizable measurement functionalities. This software enables detailed terrain visualizations, facilitating thorough environmental assessments and enabling users to virtually navigate, analyze, and plan site-specific operations. Among the key features are a user-centric interface for virtual navigation, precise site measurement tools for site evaluations, interactive visualizations that highlight potential operational hazards, dynamic route planning capabilities, and real-time collision detection to promote safe workflows. By demonstrating the effectiveness of this tool through real-world application, the present work underscores the tool’s potential to revolutionize radioactive source EOL management by improving operational efficiencies, minimizing risk, and advancing the state of practice to achieve suitable and secure radioactive material handling.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Preparing research samples for safe arrival at centers and facilities: recipes for successful experiments

Preparation of biomacromolecules for structural biology studies is a complex and time-consuming process. The goal is to produce a highly concentrated, highly pure product that is often shipped to large facilities with tools to prepare the samples for crystallization trials or for measurements at synchrotrons and cryoEM centers. The aim of this article is to provide guidance and to discuss general considerations for shipping biomacromolecular samples. Details are also provided about shipping samples for specific experiment types, including solution- and cryogenic-based techniques. These guidelines are provided with the hope that the time and energy invested in sample preparation is not lost due to shipping logistics.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A Physics-Aligned Multi-Domain Machine Learning Framework for Time-Localised Diagnosis of Power Electronics Faults

This paper presents a physics-aligned framework for fault diagnosis in multi-phase power-electronic systems using cycle-synchronous windowing and multi-domain features derived from Fourier, wavelet, and Hilbert–Huang representations. While both logistic regression and multilayer perceptron (MLP) models achieve perfect performance under standard evaluation, blind unseen testing reveals a critical failure in a baseline MLP. This is shown to arise from model selection based on validation accuracy. Using validation-loss-based selection restores correct unseen performance and improves confidence. Feature ablation shows that Fourier and wavelet features dominate, while computational analysis indicates that feature extraction, particularly HHT, governs runtime.

Kumar, Praveen [ORNL] (ORCID:0000000291877857)↗
DOE OSTI2024

Architecture Design for Remote Operation of Microreactors: Poster

The nuclear sector is pursuing a number of advanced reactor concepts, one of which is the microreactor, an advanced reactor characterized by a power output of less than 20 MWth. These reactors are intended for use in applications where traditional power solutions are economically or logistically impractical. One key feature required for the successful deployment of microreactors is a remote operation capability, which can dramatically cut staffing expenses by eliminating the need for licensed operators to be present at the site of each reactor and instead concentrate in a centrally located operations center. In moving to a remote operations framework for nuclear reactors, the number of potential attack surfaces for a cyber adversary looking to cause harm or disruption increases. Therefore, a robust cybersecurity architecture is required to mitigate these potential vulnerabilities. This paper explores the functional infrastructure, security, and communication requirements to adapt remote operations to nuclear applications. It then presents a reference architecture for remote operations of microreactors that applies best practices in cybersecurity and remote communications in the context of a digital twin remote operation system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Enhancing Automotive Intrusion Detection Through Multi-Modal Fusion: A CAN FD-LiDAR Approach

As vehicles become smarter and more autonomous, they increasingly depend on advanced sensors and communication technologies to operate securely. However, such growing dependence on technology—whether it’s CAN (Controller Area Network) for internal communication or LiDAR (Light Detection and Ranging) for sensing the world around them—also expands the attack surface for the types of cyber attacks. Traditional intrusion detection systems (IDS) typically monitor these systems in isolation, limiting their ability to detect sophisticated, crosssystem attacks. To address this, we propose a multi-modal fusion approach that combines real-world CAN FD signals (from the HCRL dataset) with LiDAR features (from the nuScenes dataset) to enhance attack detection. Our method employs a twostage ensemble approach. Calibrated XGBoost and LightGBM models initially process CAN FD (Fuzzing Data) and LiDAR data independently, detecting timing anomalies and space abnormalities. They are subsequently logarithmically combined with a logistic regression meta-model along with 17 engineered features capturing cross-modal behavior, prediction conflicts, and nonlinear interactions. This approach achieves an AUC of 0.87 and an F1-score of 0.82, surpassing single-modality baselines and early fusion methods, at merely 2 ms inference latency. Compared with deep learning competitors, it is 3 times more efficient, providing a lightweight, interpretable, and real time solution to automotive cybersecurity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

DEVELOPMENT AND DEMONSTRATION TESTBED FOR THE REMOTE OPERATIONS AND MONITORING OF MICROREACTORS

The nuclear industry is rapidly developing many advanced-reactor concepts for near-term deployment in both traditional and non-traditional nuclear-powered applications. One such category of advanced reactor is the microreactor, a class of reactor with less than 20MWth power output, intended for applications where the economics or logistics of traditional power sources are difficult. This includes applications such as remote communities, mining sites, defense installations, or humanitarian and disaster-relief missions. One key enabling feature for the successful deployment of microreactors is a remote operations capability. Remote operations provide monitoring and control capabilities which can significantly reduce staffing costs by eliminating the need for licensed operators at each reactor facility and improve the economic viability for microreactor deployment. A remote concept of operations is not currently an established capability in the nuclear industry. In addition, no demonstration microreactor is expected to complete construction or go critical until at least 2026. This leaves two major capability gaps: the successful demonstration of a remote concept of operations for microreactors and a test bed suitable for said demonstration. Both gaps must be addressed in order to advance the remote concepts of nuclear operation and, more broadly, microreactors themselves from paper to reality. This paper aims to fill these gaps and describes a test bed that would support development and deployment of a remote concept of nuclear operations, initial experimental results from that test bed, and the application of the test bed and experimental results for a digital-twin-based remote concept of operations underdevelopment at Idaho National Laboratory (INL). The platform chosen as a remote concept of nuclear operations test bed is the Single Primary Heat Extraction and Removal Emulator, known as SPHERE, located at INL. SPHERE is a small-scale non-nuclear test bed that emulates thermal behavior of a microreactor. The small-scale and non-nuclear nature of SHPERE limit safety concerns associated with remote operations while still providing the physical response representative of a microreactor. A network connection was added to SPHERE that enables remote-monitoring capability. This allows for real-time data streaming to networked workstations, data historians, and human-machine interfaces (HMIs). These are all critical components in a remote concept of operations, thus providing a robust development and demonstration platform. An initial experiment was performed using the SPHERE remote operations testbed. This included running a comprehensively instrumented SPHERE through a series of steady-state and transient operating scenarios in both normal and abnormal operating conditions, all while streaming live test data to a remote HMI and data warehouse. This initial experiment served three purposes: (1) characterizing the response of SPHERE, (2) demonstrating the remote connection to SPHERE, and (3) providing a baseline data set for development of a digital-twin-based remote concept of operations that is under development at INL.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Predictive analytics to direct clinical attention to complex patients with elevated suicide risk: enhancement of the Veterans Health Administration REACH VET model

Suicide is a major public health concern, particularly among Veterans. The U.S. Department of Veterans Affairs Veterans Health Administration (VHA) employs the Recovery Engagement and Coordination for Health–Veterans Enhanced Treatment (REACH VET) model to prioritise high-risk patients for targeted clinical attention. REACH VET 1.0 (RV 1.0) was developed on 2008–2011 data. To reflect changes in clinical practice and populations, VHA updated it to REACH VET 2.0 (RV 2.0). This study describes its development and validation. RV 2.0 used longitudinal data from 7,248,170 VHA patients (4,967 suicide deaths) in 2018–2019, with 650 time-varying demographic, clinical and area-level predictors derived from a 2-year lookback (2016–2019). An ensemble of Elastic-Net logistic regression models was trained on 2018 data and evaluated monthly at the population level in 2019, focusing on the top 0.1% intervention risk tier. Analyses assessed model discrimination, suicide detection, risk concentration, subgroup consistency (sex, age and race/ethnicity) and performance relative to RV 1.0 using the same percentile-based risk strata. RV 2.0 outperformed RV 1.0 across all risk strata, with better discrimination (C-statistic 0.76 vs 0.69) and consistent performance across demographic subgroups. Within the top 0.1% of predicted risk, RV 2.0 identified more deaths, higher suicide rates and greater mortality risk concentration both when averaged across the 12 monthly 2019 test sets (5.6 vs 3.6; 83.6 vs 53.7 per 100,000 person-years; 21.0 vs 14.1) and when annualised for 2019 (67 vs 43; 2.7% vs 1.7%; 1,003 vs 644 per 100,000 person-years; 26.7 vs 17.1). RV 2.0 improves suicide risk stratification among Veterans, demonstrating better performance and consistent prediction across subgroups and highlighting the need for regular model updates and evaluation.

Peluso, Alina [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2024

FacultyHack Events: Faculty-Focused Hackathons for High-Performance Computing Curriculum Development

Broadening participation initiatives are important for engaging underrepresented groups in science, technology, engineering, and math (STEM). Such initiatives help foster supportive and inclusive work environments that promote creativity and productivity. While there are initiatives that aim to engage students and faculty, opportunities remain to improve faculty support. Hackathons have proved to be a useful approach for student engagement. There are, however, limited insights into whether and how such events would also work for faculty aiming to develop curricula. This paper discusses the design of a faculty-focused hackathon event, FacultyHack, for curriculum development. We outline the logistics and structure for two past FacultyHack events, detail changes between events, and describe potential improvements and lessons learned.

Holmen, John [ORNL] (ORCID:0000000259342641)↗
DOE OSTI2025

PDPTW-DB: MILP-Based Offline Route Planning for PDPTW with Driver Breaks

The Pickup and Delivery Problem with Time Windows (PDPTW) involves optimizing routes for vehicles to meet pickup and delivery requests within specific time constraints, a challenge commonly faced in logistics and transportation. Microtransit, a flexible and demand-responsive service using smaller vehicles within defined zones, can be effectively modeled as a PDPTW. Yet, the need for driver breaks—a key human constraint—is frequently overlooked in PDPTW solutions, despite being necessary for regulatory compliance. This study presents a novel mixed-integer linear programming formulation for the Pickup and Delivery Problem with Time Windows and Driver Breaks (PDPTW-DB). To the best of our knowledge this formulation is the first to consider mandatory periodic driver breaks within optimized Microtransit routes. The proposed model incorporates regulatory compliant break scheduling directly within the vehicle routing optimization framework. By considering driver break requirements as an integral component of the optimization process, rather than as a post-processing step, the model enables the generation of routes that respect hours of service regulations while minimizing operational costs. This integrated approach facilitates the generation of schedules that are operationally efficient and prioritize driver welfare through driver breaks. We work with a public transit agency from the southern USA, and highlight the specific nuances of driver break optimization, and present a Pickup and Delivery Problem with Time Windows formulation for optimizing Microtransit operations and scheduling driver breaks. We validate our approach using real-world data from the transit agency. Our results validate our formulation in producing cost-effective, and regulation-compliant solutions.

Applied Computing, Transportation↗
DOE OSTI2024

Sentinel

Network intrusion detection systems (NIDS) are commonplace in network security but they frequently employ algorithms that are computational demanding requiring hardware and software with significant power requirements. Two examples of such resource-intensive algorithms used for network security are regular expression matching and broader signature pattern matching which are commonly used in deep packet inspection (DPI). Network security algorithms that have large power requirements may be a challenge for low-power internet-of-things (IoT) environments, which generally lack the power resources to implement complex security measures like computationally expensive DPI at the edge. Furthermore, IoT environments incorporating 5G standalone networks have network latency constraints beyond just power that make DPI at the edge even more difficult. Programmable logic is ideally suited for machine learning inference for DPI because of its deep instruction level parallelism and single-cycle memory access. Machine learning approaches for DPI have been explored before using the programmable logic of field programmable gate arrays (FPGA) as a potential solution for NIDS approaches that would be power-suitable for IoT. However, those previous programmable logic NIDS approaches utilize either a supervised or unsupervised learning model. Sentinel utilizes the ensemble of these two machine learning approaches known as a semi-supervised approach which has shown promise in NIDS implementations. Sentinel provides a programmable logic implementation of a semi-supervised approach for DPI which operates at much lower power and latency than a GPU implementation with negligible loss of accuracy due to quantization through a logistic regressor.

Anderson, MatthewW [Idaho National Laboratory (INL↗
DOE OSTI2025

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds—five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.

Arasteh, Kourosh [Lawrence Livermore National Labo↗
DOE OSTI2025

The Effect of Updraft Entrainment on Convective Cell Deepening in Realistic Large-Eddy Simulations

Entrainment of surrounding cooler and drier air into convective updrafts is one of the key processes that influence deep convection initiation and growth. Numerous studies have investigated the effect of entrainment on isolated convective cloud growth in idealized simulations, but the importance of this effect in realistic conditions with many interacting convective clouds remains uncertain. We examine the impact of entrainment on the depth reached by convective clouds in realistic large-eddy simulations (LES) over central Argentina during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign. Cloudy updrafts and their associated properties are assigned to convective cells tracked with radar reflectivity signatures. Several thousand convective cells are tracked over two high convective available potential energy (CAPE) and two low CAPE cases that support cells of varying depths and intensities. Entrainment is calculated explicitly as the fluxes of air into the outer surface of each cloudy updraft. Single-predictor logistic regression models are used to determine the relative importance of updraft, near-updraft, and preconvective initiation atmospheric conditions in predicting whether convective cells become deep. We then build a multiple-predictor regression model pairing important updraft and meteorological metrics with fractional entrainment rate. The probability of cells transitioning to deep convection is most sensitive to ambient 600-hPa relative humidity (42% of total metric contribution to cloud depth predictability), followed by low-level CAPE (28%), cloud-base updraft width (19%), and fractional entrainment (11%). Thus, the initial width of the updraft along with potential buoyancy and its dilution through the midtroposphere collectively determine whether deep convection will result from shallower clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Investigating Resiliency of Transportation Network under Targeted and Potential Climate Change Disruptions

Ensuring robustness and resilience in intermodal transportation systems is essential for the continuity and reliability of global logistics. These systems are vulnerable to various disruptions, including natural disasters and technical failures. Despite significant research on freight transportation resilience, investigating the robustness of the system after targeted and climate-change-driven disruption remains a crucial challenge. Drawing on network science methodologies, this study models the interdependencies within the rail and water transport networks and simulates different disruption scenarios to evaluate system responses. Here, we use the data from the U.S. Department of Energy Volpe Center for network topology and tonnage projections. The proposed framework quantifies deliberate, stochastic, and climate-driven infrastructure failure, using higher resolution downscaled multiple Earth System Models’ simulations from Coupled Model Intercomparison Project Phase version 6. We show that the disruptions of a few nodes could have a larger impact on the total tonnage of freight transport than on network topology. For example, the removal of targeted 20 nodes can bring the total tonnage carrying capacity to 30% with about 75% of the rail freight network intact. This research advances the theoretical understanding of transportation resilience and provides practical applications for infrastructure managers and policymakers. By implementing these strategies, stakeholders and policymakers can better prepare for and respond to unexpected disruptions, ensuring sustained operational efficiency in transportation networks.

Climate Change Disruptions↗
DOE OSTI2024

Carbon-Storing Structural Material Based on Wastewater-Cultivated Chlorella Sorokiniana

Microalgae cultivation is a promising technology for carbon sequestration and wastewater remediation. It is economically and logistically critical to find viable applications for the large quantities of produced biomass. In current research, a structural material is developed by using nearly 100% Chlorella sorokiniana. The otherwise non-cohesive microalgae cells are first surface-activated and then compacted under a relatively high pressure. The resulting material is a dense solid, stronger than typical steel-reinforced concrete in flexural tests. The processing procedure is simple and fast, and the setup is scalable. This technique may be useful for next-generation green construction and provide a use-case for sustainable microalgae biomass.

Yi, Haozhe↗
DOE OSTI2025

The impact of kidney function on Alzheimer’s disease blood biomarkers: implications for predicting amyloid-β positivity

Impaired kidney function has a potential confounding effect on blood biomarker levels, including biomarkers for Alzheimer’s disease (AD). Given the imminent use of certain blood biomarkers in the routine diagnostic work-up of patients with suspected AD, knowledge on the potential impact of comorbidities on the utility of blood biomarkers is important. We aimed to evaluate the association between kidney function, assessed through estimated glomerular filtration rate (eGFR) calculated from plasma creatinine and AD blood biomarkers, as well as their influence over predicting Aβ-positivity. We included 242 participants from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort, comprising cognitively unimpaired individuals (CU; n = 124), mild cognitive impairment (MCI; n = 58), AD dementia (n = 34), and non-AD dementia (n = 26) patients all characterized by [ 18 F] AZD-4694. Plasma samples were analyzed for Aβ42, Aβ40, glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), tau phosphorylated at threonine 181 (p-tau181), 217 (p-tau217), 231 (p-tau231) and N-terminal containing tau fragments (NTA-tau) using Simoa technology. Kidney function was assessed by eGFR in mL/min/1.73 m 2 , based on plasma creatinine levels, age, and sex. Participants were also stratified according to their eGFR-indexed stages of chronic kidney disease (CKD). We evaluated the association between eGFR and blood biomarker levels with linear models and assessed whether eGFR provided added predictive value to determine Aβ-positivity with logistic regression models. Biomarker concentrations were highest in individuals with CKD stage 3, followed by stages 2 and 1, but differences were only significant for NfL, Aβ42, and Aβ40 (not Aβ42/Aβ40). All investigated biomarkers showed significant associations with eGFR except plasma NTA-tau, with stronger relationships observed for Aβ40 and NfL. However, after adjusting for either age, sex or Aβ-PET SUVr, the association with eGFR was no longer significant for all biomarkers except Aβ40, Aβ42, NfL, and GFAP. When evaluating whether accounting for kidney function could lead to improved prediction of Aβ-positivity, we observed no improvements in model fit (Akaike Information Criterion, AIC) or in discriminative performance (AUC) by adding eGFR to a base model including each plasma biomarker, age, and sex. While covariates like age and sex improved model fit, eGFR contributed minimally, and there were no significant differences in clinical discrimination based on AUC values. We found that kidney function seems to be associated with AD blood biomarker concentrations. However, these associations did not remain significant after adjusting for age and sex, except for Aβ40, Aβ42, NfL, and GFAP. While covariates such as age and sex improved prediction of Aβ-positivity, including eGFR in the models did not lead to improved prediction for any biomarker. Our findings indicate that renal function, within the normal to mild impairment range, does not seem to have a clinically relevant impact when using highly accurate blood biomarkers, such as p-tau217, in a biomarker-supported diagnosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Learning epistatic polygenic phenotypes with Boolean interactions

Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1 , a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Misclassification of causes of death among a small all-autopsied group of former nuclear workers: Death certificates vs. autopsy reports

The U.S. Transuranium and Uranium Registries performs autopsies on each of its deceased Registrants as a part of its mission to follow up occupationally-exposed individuals. This provides a unique opportunity to explore death certificate misclassification errors, and the factors that influence them, among this small population of former nuclear workers. Underlying causes of death from death certificates and autopsy reports were coded using the 10 th revision of the International Classification of Diseases (ICD-10). These codes were then used to quantify misclassification rates among 268 individuals for whom both full autopsy reports and death certificates with legible underlying causes of death were available. When underlying causes of death were compared between death certificates and autopsy reports, death certificates correctly identified the underlying cause of death’s ICD-10 disease chapter in 74.6% of cases. The remaining 25.4% of misclassified cases resulted in over-classification rates that ranged from 1.2% for external causes of mortality to 12.2% for circulatory disease, and under-classification rates that ranged from 7.7% for external causes of mortality to 47.4% for respiratory disease. Neoplasms had generally lower misclassification rates with 4.3% over-classification and 13.3% under-classification. A logistic regression revealed that the odds of a match were 2.8 times higher when clinical history was mentioned on the autopsy report than when it was not. Similarly, the odds of a match were 3.4 times higher when death certificates were completed using autopsy findings than when autopsy findings were not used. This analysis excluded cases where it could not be determined if autopsy findings were used to complete death certificates. The findings of this study are useful to investigate the impact of death certificate misclassification errors on radiation risk estimates and, therefore, improve the reliability of epidemiological studies.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2026

Variability in Performance of a Machine Learning Seismicity Catalog: Central Italy, 2016–2017

Machine learning (ML) catalogs contain many more earthquakes than routine catalogs, but their performance in phase picking and earthquake detection has not been fully evaluated. We develop station‐level detection probabilities using logistic regression and combine them across a seismic network to compute spatial magnitude‐of‐completeness fields. We apply this approach to two catalogs from the 2016–2017 Central Italy sequence that were constructed from the same seismic network, one routine and one ML‐based. At the station level, the ML picker increases detection sensitivity by identifying smaller magnitude events and detecting earthquakes at greater distances. Spatially, the magnitude of completeness decreases substantially, with median values shifting from 1.6 to 0.5 for P waves and from 1.7 to 0.5 for S waves. However, the ML catalog also shows greater variability in station‐level performance than the routine catalog. These results demonstrate that ML‐based improvements in detectability are widespread but spatially nonuniform, highlighting their benefits, their limitations, and the potential for further improvements.

15 GEOTHERMAL ENERGY↗