Windowing artefacts likely account for recent claimed detection of oscillating cosmic scale factor
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Sasha R Brownsberger↗
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RADAI: A Large-Scale Realistic Dataset for Radiation Detection Algorithm Development
Open, realistic datasets are essential for developing and benchmarking radiation detection algorithms, yet they remain scarce. The Radiological Anomaly Detection and Identification (RADAI) project was develop to create datasets that meet the training and testing needs for sophisticated radiation detection algorithms. The RADAI dataset is a large-scale synthetic resource that integrates high-fidelity Monte Carlo simulations with realistic urban scenarios to capture both background variability and source signatures. RADAI models construction-material NORM, people and vehicles, urban clutter, and dynamic environmental effects such as cosmic-ray and rain-induced transients, and they provide list-mode detector data with motion and response modeling suitable for algorithm training and evaluation. The RADAI project resulted in three publicly-released complementary datasets together with an online scoring portal for standardized performance assessment and an open software toolkit that supports data access, augmentation, model development, and evaluation. These resources enable reproducible comparisons across methods and promote rigorous studies at the scale required by contemporary machine learning. By grounding algorithm development in realistic, well-documented conditions, RADAI supports progress toward more robust detection, identification, and localization in complex urban environments.
Ghawaly, James M. [Division of Computer Science an↗
Impulsive Magnetic Anomaly Detection At the 100-m Scale With an Array of Induction Coil Magnetometers
We demonstrate magnetic anomaly detection (MAD) using an array of 24 commercial induction coil magnetometers with stand-off distances from a pulsed 99.8(3) kA·m 2 magnetic dipole source of 260–1200 m. The sparse array is used to estimate the magnetic dipole location, magnitude, and orientation. We demonstrate how independent component analysis (ICA) improves the accuracy and precision of the magnetometer array when estimating the dipole parameters. Using sensor responses recorded from individual source pulses, we estimate the dipole location to within 29 ± 2 m, the magnitude to within 3 ± 3 kA·m 2 , and dipole orientation error to within 19 ± 0.6°.
47 OTHER INSTRUMENTATION↗
Short time-scale optical pulsations in the night sky background
A network of monitoring stations designed to detect large scale fluorescence emission in the atmosphere has been in operation for over two years. The motivation for the search arises from the prediction that an energetic photon burst would be produced in a supernova and this burst, when absorbed in the atmosphere, would produce fluorescence. This paper reports on observations up to February 1971. No supernova-like events have been found, although 4.4 were expected. One class of non-fluorescence events is described that evidence suggests is related to electrical discharge in the atmosphere. Another type of non-fluorescence pulse appears to be related to particle precipitation in the atmosphere.
Bertsch, D. L.↗
Detection of the large-scale tidal field with galaxy multiplet alignment in the DESI Y1 spectroscopic survey
We explore correlations between the orientations of small galaxy groups, or ‘multiplets’, and the large-scale gravitational tidal field. Using data from the Dark Energy Spectroscopic Instrument (DESI) Y1 survey, we detect the intrinsic alignment (IA) of multiplets to the galaxy-traced matter field out to separations of $100\,h^{-1}$ Mpc. Unlike traditional IA measurements of individual galaxies, this estimator is not limited by imaging of galaxy shapes and allows for direct IA detection beyond redshift $z=1$. Multiplet alignment is a form of higher order clustering, for which the scale-dependence traces the underlying tidal field and amplitude is a result of small-scale ($\lt 1h^{-1}$ Mpc) dynamics. Within samples of bright galaxies, luminous red galaxies (LRG) and emission-line galaxies, we find similar scale-dependence regardless of intrinsic luminosity or colour. This is promising for measuring tidal alignment in galaxy samples that typically display no IA. DESI’s LRG mock galaxy catalogues created from the A BACUS S UMMIT N -body simulations produce a similar alignment signal, though with a 33 per cent lower amplitude at all scales. An analytic model using a non-linear power spectrum (NLA) only matches the signal down to 20 $h^{-1}$ Mpc. Our detection demonstrates that galaxy clustering in the non-linear regime of structure formation preserves an interpretable memory of the large-scale tidal field. Multiplet alignment complements traditional two-point measurements by retaining directional information imprinted by tidal forces, and contains additional line-of-sight information compared to weak lensing. This is a more effective estimator than the alignment of individual galaxies in dense, blue, or faint galaxy samples.
79 ASTRONOMY AND ASTROPHYSICS↗
Pixel-Based Model For High Latitude Dust Detection
Dust has implications on the energy budget, ocean biodiversity, and economy at regional and global scales. Dust detection relies on spectral sensitivity at visible (RGB) and infrared wavelengths. Radiative properties of high latitude dust and the background surface albedo in these regions (>40°N, >40°S) complicate current dust detection methods. Leveraging supervised machine learning (ML) methods, we propose a new method accounting for regional differences of dust occurrence.
High latitude dust↗
H2 emission as a tracer of molecular hydrogen: Large-scale observations of Orion
We have detected extremely extended (greater than 1.5 deg, or 12 pc) near-infrared H2 line emission from the Orion A molecular cloud. We have mapped emission in the 1.601 micrometer(s) upsilon = 6 - 4 Q(1) and 2.121 micrometer(s) upsilon = 1 - 0 S(1) lines of H2 along a approx. 2 deg R.A. cut and from a 6' x 6' region near theta(sup 1) Ori C. The surface brightness of the extended H2 line emission is 10(exp -6) to 10(exp -5) ergs/s/sq. cm/sr. Based on the distribution and relative strengths of the H2 lines, we conclude that UV fluorescene is most likely the dominant H2 emission mechanism in the outer parts of the Orion cloud. Shock-heated gas does not make a major contribution to the H2 emission in this region. The fluorescent component of the total H2 upsilon = 1 - 0 S(1) luminosity from Orion is 30-40 solar luminosity. Molecular hydrogen excited by UV radiation from nearby OB stars contributes 98%-99% of the global H2 line emission from the Orion molecular cloud, even though this cloud has a powerful shock-excited H2 source in its core. The ability to detect large-scale H2 directly opens up new possibilities for the study of molecular clouds.
Luhman, M. L.↗
NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images
Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.
36 MATERIALS SCIENCE↗
Hydrogen Detection Strategies to Support H2@SCALE - The NREL Sensor Laboratory
Hydrogen represents a major pathway to decarbonize and stabilize the national and international energy industry and select manufacturing markets. To facilitate the development of hydrogen markets, the US Department of Energy initiated H2@Scale to bring together stakeholders to advance affordable hydrogen production, transport, storage, and utilization to increase revenue opportunities in multiple energy sectors. One major impediment to hydrogen implementation is cost. To expedite the use of hydrogen in energy and other markets, the United States announced in 2021 the Hydrogen Shot, which seeks to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). As the cost of hydrogen drops, new applications will emerge that will require unique configurations of existing equipment and infrastructure, and eventually lead to advances in the generation and utilization of hydrogen. As the hydrogen economy expands, sensors and detection methods will need to adapt to changing infrastructure demands to address the primary targets of health & safety, emissions monitoring, and process control. The NREL Sensor Laboratory is playing a pivotal role in advancing the use of hydrogen sensors and detection methodologies in each of these categories to support DOE's mission for safe and efficient utilization in emerging markets. Health & safety monitors are required to ensure that operators and facilities can react to unintended hydrogen releases, either as GH2, LH2, or as a constituent of blends (e.g., natural gas or ammonia). Current detection methodologies focus on safety applications to detect near its lower flammable limit (4 vol %), and typically include point sensors in applications such as fixed or mobile detectors (e.g., personal gas monitors). Methodologies amenable for area detection include acoustic, emerging optical imaging methods, and flame detectors. Comparable detection strategies can be utilized for emissions monitoring and quantization, however few methods can simultaneously cover both low (emissions) and high (health & safety) levels. Deployment of emission level detectors will be required to 1) reduce product loss through small but potentially significant leaks from an environmental or cost perspective, 2) reduce downtime of high demand systems by early identification of eminent system failures (leaks through pump or compressor seals indicative of impending failure), and 3) address potential emission monitoring requirements that may be set by regulating bodies. The first two points should be adopted by industry to reduce the cost-of-goods-sold. The third main category for hydrogen detection relates to process control and may be advantageous for many existing applications. Two main applications are emerging. For example, the purity requirements for hydrogen that is dispensed from refueling systems for hydrogen fuel cell electric vehicles (FCEV) is rigorously regulated by the Standard SAE J2719, which prescribes maximum allowable levels of multiple impurities in the hydrogen fuel and must be verified by a regulatory body. Hydrogen contaminant detectors (HCD) integrated to the fueling station can assure this compliance. HCDs must be able operate in 100% H2 backgrounds and be able to distinguish between multiple contaminants at low ppm to low ppb levels. Secondly, as a strategy to decarbonize the natural gas grid, there are proposals to blend hydrogen with natural gas. This blending will affect transport applications (pipeline infrastructure), stationary combustion systems (turbines), and consumer and commercial appliances. In the short-term, hydrogen levels up to 20% are proposed. Variations in the hydrogen level can have dramatic impact on the combustion process and on the potential response of safety sensors. These mixtures may be regulated so that the concentration at a delivery point must be monitored with high precision. However, routine maintenance may introduce background gases such as ambient air (with water) or maintenance gases (introduced with welding processes or adhesive outgassing.) Therefore, the detection methodology must be robust enough to recover or respond to various contaminants. Several reviews can be found in literature addressing sensing and detection technologies, including their limitations and applications. However, for most applications, limitations can be alleviated by combining various detection techniques either through system integration or implementation of machine learning methods (artificial intelligence). In this presentation, we will discuss several applications, highlight their current approach for hydrogen detection, and suggest detection strategies to supplement their limitations.
ENERGY STORAGE,HYDROGEN↗
DyG-DPCD: A Distributed Parallel Community Detection Algorithm for Large-Scale Dynamic Graphs
Dynamic (Temporal) graphs capture the valuable evolution of real-world systems, from the continuously evolving patterns of social interactions and genetic pathways to the dynamic fluctuations of economic forces. Detecting communities for such evolving networks poses unique challenges. Detecting and analyzing the evolution of communities within dynamic graphs unlocks valuable insights into the underlying structural and temporal patterns of real-world systems. However, the sheer volume of modern graph data and the inherent complexity of the temporal dimension pose significant challenges to scalable community detection algorithms. Addressing this gap, our work explores the limited landscape of scalable distributed-memory parallel methods specifically designed for dynamic network community detection. We propose a novel parallel algorithm, DyG-DPCD (Dynamic Graph Distributed Parallel Community Detection), to detect communities in dynamic networks using the Message Passing Interface (MPI) framework. We present a vertex-centric approach, allowing us to detect communities through local optimization. Furthermore, we enhance our baseline algorithm by incorporating three heuristics, which improve the algorithm’s performance significantly while maintaining the quality of the solutions. We demonstrate the efficiency of our algorithm by experimenting on several real-world large-scale networks with hundreds of millions of edges spanning diverse domains. Notably, DyG-DPCD achieves speedups between 25× and 30× for large networks that we experimented on using NERSC compute nodes. In conclusion, our algorithm outperforms the STINGER parallel re-agglomeration algorithm by 30×.
97 MATHEMATICS AND COMPUTING↗
Preliminary DMR measurements of the CMB isotropy
The COBE Differential Microwave Radiometers (DMR) instrument has produced preliminary full-sky maps at frequencies 31.5, 53, and 90 GHz. The redundant channels and matched beams at three frequencies distinguish the DMR from previous large-scale surveys. Galactic emission is seen unambiguously at all three frequencies. The only large-scale anisotropy detected in the cosmic microwave background is the dipole anisotropy. There is no clear evidence for any other large-angular-scale feature in the maps. Without correcting for any systematic effects, we are able to place limits DeltaT/T sub 0 less than 3 x 10 exp -5 for the rms quadrupole amplitude, DeltaT/T sub 0 less than 4 x 10 exp -5 for monochromatic fluctuations, and DeltaT/T sub 0 less than 4 x 10 exp -5 for Gaussian fluctuations (all limits are 95 percent C.L. with TO = 2.735 K). The data limit DeltaT/T sub 0 less than 10 exp -4 for any feature larger than 7 deg. We briefly review the DMR and discuss some implications of these results in cosmology.
Smoot, G. F.↗
A detection of the evolutionary time scale of the DA white dwarf G117 - B15A with the Whole Earth Telescope
The time rate of change for the main pulsation period of the 13,000 K DA white dwarf G117 - B15A has been detected using the Whole Earth Telescope (WET). The observed rate of period change, P(dot) = (12.0 + or - 3.5) x 10 to the -15th s/s, is somewhat larger than the published theoretical calculations of the rate of period change due to cooling, based on carbon core white dwarf models. Other effects that could contribute to the observed rate of period change are discussed.
Kepler, S. O.↗
Detectors for Tomorrow's Instruments
Cryogenically cooled superconducting detectors have become essential tools for a wide range of measurement applications, ranging from quantum limited heterodyne detection in the millimeter range to direct searches for dark matter with superconducting phonon detectors operating at 20 mK. Superconducting detectors have several fundamental and practical advantages which have resulted in their rapid adoption by experimenters. Their excellent performance arises in part from reductions in noise resulting from their low operating temperatures, but unique superconducting properties provide a wide range of mechanisms for detection. For example, the steep dependence of resistance with temperature on the superconductor/normal transition provides a sensitive thermometer for calorimetric and bolometric applications. Parametric changes in the properties of superconducting resonators provides a mechanism for high sensitivity detection of submillimeter photons. From a practical point of view, the use of superconducting detectors has grown rapidly because many of these devices couple well to SQUID amplifiers, which are easily integrated with the detectors. These SQUID-based amplifiers and multiplexers have matured with the detectors; they are convenient to use, and have excellent noise performance. The first generation of fully integrated large scale superconducting detection systems are now being deployed. I will discuss the prospects for a new generation of instruments designed to take full advantage of the revolution in detector technology.
Moseley, Harvey↗
Superconducting Detectors Come of Age, or Ready to Leave the Lab
Cryogenically cooled superconducting detectors have become essential tools for a wide range of measurement applications, ranging from quantum limited heterodyne detection in the millimeter range to direct searches for dark matter with superconducting phonon detectors operating at 20 mK. Superconducting detectors have several fundamental and practical advantages which have resulted in their rapid adoption by experimenters. Their excellent performance arises in part from reductions in noise resulting from their low operating temperatures, but unique superconducting properties provide a wide range of mechanisms for detection. For example, the steep dependence of resistance with temperature on the superconductor normal transition provides a sensitive thermometer for calorimetric and bolometric applications. Parametric changes in the properties of superconducting resonators provide a mechanism for high sensitivity detection of submillil.neter photons. From a practical point of view, the use of superconducting detectors has grown rapidly because many of these devices couple well to SQUID amplifiers, which are easily integrated with the detectors. These SQUID-based amplifiers and multiplexers have matured with the detectors; they are convenient to use, and have excellent noise performance. The first generation of fully integrated large-scale superconducting detection systems is now being deployed. Improved understanding of the operation of these detectors, combined with rapidly improving fabrication techniques, is quickly expanding the capability of these detectors. I will review the development and application of superconductor-based detectors, the ultimate limits to their performance, and consider prospects for their future applications. Continued advances promise to enable important new measurements in physics, and with appropriate advances in cryogenic infrastn~cturem, ay result in the use of these detectors in everyday monitoring applications.
Moseley, Samuel H.↗
Modeling and Performance Considerations for Automated Fault Isolation in Complex Systems
The purpose of this paper is to document the modeling considerations and performance metrics that were examined in the development of a large-scale Fault Detection, Isolation and Recovery (FDIR) system. The FDIR system is envisioned to perform health management functions for both a launch vehicle and the ground systems that support the vehicle during checkout and launch countdown by using suite of complimentary software tools that alert operators to anomalies and failures in real-time. The FDIR team members developed a set of operational requirements for the models that would be used for fault isolation and worked closely with the vendor of the software tools selected for fault isolation to ensure that the software was able to meet the requirements. Once the requirements were established, example models of sufficient complexity were used to test the performance of the software. The results of the performance testing demonstrated the need for enhancements to the software in order to meet the demands of the full-scale ground and vehicle FDIR system. The paper highlights the importance of the development of operational requirements and preliminary performance testing as a strategy for identifying deficiencies in highly scalable systems and rectifying those deficiencies before they imperil the success of the project
Ferrell, Bob↗
NASA Tech Briefs, October 2013
Topics include: A Short-Range Distance Sensor with Exceptional Linearity; Miniature Trace Gas Detector Based on Microfabricated Optical Resonators; Commercial Non-Dispersive Infrared Spectroscopy Sensors for Sub-Ambient Carbon Dioxide Detection; Fast, Large-Area, Wide-Bandgap UV Photodetector for Cherenkov Light Detection; Mission Data System Java Edition Version 7; Adaptive Distributed Environment for Procedure Training (ADEPT); LEGEND, a LEO-to-GEO Environment Debris Model; Electronics/Computers; Millimeter-Wave Localizers for Aircraft-to-Aircraft Approach Navigation; Impedance Discontinuity Reduction Between High-Speed Differential Connectors and PCB Interfaces; SpaceCube Version 1.5; High-Pressure Lightweight Thrusters; Non-Magnetic, Tough, Corrosion- and Wear-Resistant Knives From Bulk Metallic Glasses and Composites; Ambient Dried Aerogels; Applications for Gradient Metal Alloys Fabricated Using Additive Manufacturing; Passivation of Flexible YBCO Superconducting Current Lead With Amorphous SiO2 Layer; Propellant-Flow-Actuated Rocket Engine Igniter; Lightweight Liquid Helium Dewar for High-Altitude Balloon Payloads; Method to Increase Performance of Foil Bearings Through Passive Thermal Management; Unibody Composite Pressurized Structure; JWST Integrated Science Instrument Module Alignment Optimization Tool; Radar Range Sidelobe Reduction Using Adaptive Pulse Compression Technique; Digitally Calibrated TR Modules Enabling Real-Time Beamforming SweepSAR Architectures; Electro-Optic Time-to-Space Converter for Optical Detector Jitter Mitigation; Partially Transparent Petaled Mask/Occulter for Visible-Range Spectrum; Educational NASA Computational and Scientific Studies (enCOMPASS); Coarse-Grain Bandwidth Estimation Scheme for Large-Scale Network; Detection of Moving Targets Using Soliton Resonance Effect; High-Efficiency Nested Hall Thrusters for Robotic Solar System Exploration; High-Voltage Clock Driver for Photon-Counting CCD Characterization; Development of the Code RITRACKS; and Enabling Microliquid Chromatography by Microbead Packing of Microchannels.
Source record↗
Leveraging large language models to automate the identification of healthcare access barriers for veterans
Objective: To develop and evaluate an automated system for identifying healthcare barriers focusing on transportation issues in veterans’ clinical notes using large language models (LLMs) and to assess the impact of different prompting strategies on classification performance and explanation consistency. Methods: We developed a hybrid system combining pattern matching for templated notes with LLM analysis for free-text notes. Using 2000 manually annotated clinical notes, we compared four prompting strategies (dual-role short, dual-role long, analysis-first, analysis-only) across Mistral-7B and Llama-3.1 models. We evaluated classification performance using standard metrics and assessed explanation consistency through embedding similarity analysis. Results: The analysis-first strategy achieved superior performance, with Mistral-7B reaching an F1 score of 0.914, outperforming traditional machine learning approaches (GBM: 0.786, BERT: 0.811). LLMs demonstrated higher explanation consistency within models (mean cosine similarity 0.887–0.908) compared to cross-model similarities (0.767–0.872). Pattern matching successfully handled 6.7% of templated notes deterministically. Mistral-7B showed greater internal consistency but higher abstention rates compared to Llama-3.1. Conclusion: Requiring LLMs to analyze evidence before classification improves both accuracy and explanation consistency for identifying transportation barriers in clinical notes. This approach enables automated barrier detection at scale while providing clinically relevant explanations, supporting both population-level healthcare planning and individual patient care decisions.
Healthcare access barriers↗
Large-scale deep learning for metastasis detection in pathology reports
Objectives No existing algorithm can reliably identify metastasis from pathology reports across multiple cancer types and the entire US population. In this study, we develop a deep learning model that automatically detects patients with metastatic cancer by using pathology reports from many laboratories and of multiple cancer types. Materials and Methods We use 60 471 unstructured pathology reports from 4 Surveillance, Epidemiology, and End Results (SEER) registries. The reports were coded into 1 of 3 labels: metastasis negative, metastases positive, or metastasis undetermined. We utilize a task-specific deep neural network trained from scratch and compare its performance with a widely used large language model (LLM). Results Our deep learning architecture trained on task-specific data outperforms a general-purpose LLM, with a recall of 0.894 compared to 0.824. We quantified model uncertainty and used it to defer reports for human review. We found that retaining 72.9% of reports increased recall from 0.894 to 0.969. Discussion A smaller deep learning architecture trained on task-specific data outperforms a general LLM. Equally critical to model performance is the incorporation of uncertainty quantification, achieved here through an abstention mechanism. Conclusions This study’s finding demonstrate the feasibility of developing algorithms to automatically identify metastatic cancer cases from unstructured pathology reports.
machine learning↗