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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Holes in Optical Lightning Flashes: Identifying Poorly Transmissive Clouds in Lightning Imager Data

Space-based optical lightning sensors including the lightning imaging sensor (LIS) and geostationary lightning mapper (GLM) are pixelated imagers that detect lightning as transient increases in cloud top illumination. Detection requires optical emissions to escape the cloud top to space with sufficient energy to trigger a pixel on the imaging array. Through scattering and absorption, certain clouds are able to block most light from reaching the instrument, causing a reduction in detection efficiency (DE) and possibly location accuracy (LA). Radiant lightning emissions that illuminate large cloud top areas are used to examine scenarios where clouds block light from reaching orbit. In some cases, these anomalies in the spatial radiance distribution from the lightning pulse lead to “holes” in the optical lightning flash where certain pixels fail to trigger. Such holes are identified algorithmically in the Tropical Rainfall Measuring Mission satellite LIS record and the microphysical properties of the coincident storm region are queried. We find that holes primarily occur in tall (IR T b < 235 K) convection (87%) and overhanging anvil clouds (10%). The remaining 3% of holes occur in moderate-to-weak convection or in clear air breaks between stormclouds. We further demonstrate how an algorithm that assesses the spatial radiance patterns from energetic lightning pulses might be used to construct an optical transmission gridded stoplight product for GLM that could help operators identify clouds with a potentially reduced DE and LA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Nanosecond Gated CMOS Camera (NSGCC) ICD (Rev. 2.1)

The Ultra-Fast X-ray Imager (UXI) program is an ongoing effort at Sandia National Laboratories to create high speed, multi-frame, time-gated Read Out Integrated Circuits (ROICs), and a corresponding suite of photodetectors to image a wide variety of High Energy Density (HED) physics experiments on both Sandia’s Z-Machine and LLNL’s National Ignition Facility (NIF). Several cameras have been designed over the length of the program; one of the most recent is the Icarus, which is an improvement on past imagers (Furi and Hippogriff). A second sensor that can be connected is the Daedalus sensor. The Icarus is a 1024 × 512-pixel array with either 25 μm or 8 µm spatial resolution containing four frames of storage per pixel and has improved timing generation and distribution components while achieved 2 ns time gating. The Daedalus sensor is also a 1024 x 512-pixel array with 25 µm special resolution containing three frames of storage per pixel and has an increased set of features for a wider variety of applications from interlacing of rows in each frame to configurability of all shutters. See Section 14 for details regarding the Icarus implementation of the firmware and Section 15 for details regarding the Daedalus implementation. Due to the unique test environments UXI sensors are targeted for, full custom hardware was required to physically mount an Icarus or Daedalus sensor, manage its various functions, and read out pixel data for transfer to a host computer. Beyond experimental functionality, the hardware also needed to accommodate sensor characterization requirements. Lawrence Livermore National Laboratory’s ‘Version 4.0 Board’ was the result of these efforts. It mounts all the components required to fully utilize the Icarus and Daedalus sensors including analog to digital converters to convert pixel data and various system voltages to digital form for readout and analysis, DAC channels for remote configuration of critical bias voltages, static random-access memories to buffer pixel data, RS422 and Gigabit Ethernet communications for remote access, and an FPGA to tie these components together. This document describes the FPGA electrical interfaces in detail to allow the reader a greater understanding of the device, and to facilitate implementation of custom software to control and manage it. The Version 4.0 Board is a continuation of the Nano-second Gated CMOS hardware design that retains much of the functionality of the Version 1.0 Board while adding features including a DAC instead of digital potentiometers, as well as sensors for pressure and radiation. The Version 4.0 board is intended for applications requiring tight form-factor enclosures. It is composed of two stacking boards; one holds the FPGA and regulators to power the various components of the board while the other contains the mating connector to the sensor, image-readoff ADCs, the DAC, and other components.

42 ENGINEERING↗
DOE OSTI2026

CHESS 2025: Spectrometer orthorectified at-sensor radiance from NEON AOP imaging spectroscopy surveys

This dataset provides Level 1 (L1) orthorectified at-sensor radiance derived from measurements collected by the Imaging Spectrometer-1 (NIS-1) onboard the NEON (National Ecological Observatory Network) Airborne Observation Platform (AOP) for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). NIS-1 captures light reflected from the Earth’s surface in 426 discrete wavelength bands as raw digital numbers (DNs; Level 0). These data are then calibrated to physical units (uW/cm²·sr·nm) following the processing steps described in the NEON Imaging Spectrometer Level 1B Calibrated Radiance Algorithm Theoretical Basis Document (ATBD; Gallery 2022). The data delivered here are the primary inputs for the surface reflectance product in “Custom surface reflectance, shade masks, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study” (Carroll et al. 2026). For intertemporal comparison, the radiance data here are most directly relatable to the v2 radiance data in “NEON AOP Imaging Spectroscopy Survey of Upper East River Colorado Watersheds: Raw-Space Radiance and Observational Variable Dataset” (Goulden et al. 2018), to which the same processing methodology was applied. Together, the radiance and reflectance data enable users to exploit the unique reflection signatures of different surface objects for land cover classification, foliar trait mapping, plant vigor assessment, water content estimation, trace-element identification, and other scientific applications. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. Within each domain, data are delivered by flightline as orthorectified and calibrated hyperspectral rasters in Hierarchical Data Format version 5 (HDF5) format, with radiance values provided in uW/cm²·sr·nm on a fixed, uniform Universal Transverse Mercator (UTM) grid at 1 meter spatial resolution. The radiance rasters include all 426 NIS-1 spectral bands, along with associated quality-assurance (QA) and diagnostic and ancillary layers needed for atmospheric correction workflows. Orthorectified radiance is produced from pushbroom spectrometer observations by applying NEON’s radiometric calibration (including bad pixel masking, dark subtract, dark pedestal shift correction, electronic panel ghost correction, grating ghost correction, deblur correction and flat-fielding) and spectral calibration (using spectral response function band centers and full-width at half-maximum intensity), followed by geolocation and regridding to the fixed grid. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2021

System and method for compact, adaptive optical sensor for obtaining areal images

The present disclosure relates to an imaging system which may make use of an electronic controller, a digital micromirror array, an aperture control system and a detector to image a desired scene. The digital micromirror array has a plurality of micromirror elements responsive to control signals generated by the electronic controller for electronically aiming the micromirror elements in a desired direction to image the desired area, and for receiving and reflecting light emanating from the desired area. The aperture control system receives light reflected from the digital micromirror array and passes a predetermined subportion of the received light therethrough. The detector is responsive to the predetermined subportion of light.

Panas, Robert Matthew↗
DOE OSTI2022

Cell dark current–voltage from non-calibrated module electroluminescence image analysis

Here, we present a fast, accurate, and reliable method of obtaining cell dark current–voltage (I–V) curves from module electroluminescence (EL) images without requiring calibration or correction. For a pristine module, EL-derived dark I–V are compared to directly probed data for a variety of changing imaging parameters: camera sensor, lens, filter, aperture width, exposure time (level of sensor saturation), number of images used, and various combinations of these. Pristine modules and those experiencing different modes and degrees of degradation are examined. A recent study of modules using five different cell technologies demonstrates the practicality of our “EL sweep” technique for performance and degradation studies.

14 SOLAR ENERGY↗
DOE OSTI2022

Ultrasonic through-wall sensors

Aspects of ultrasonic through-wall data communication are described. In some aspects, a sensor device is located in an isolated environment behind a wall. A modulator generates a modulated sensor signal based on the binary sensor signal and a carrier signal. A coaxially aligned pair of electroacoustic transducers that transmits the modulated sensor signal through the wall of the isolated environment. A demodulator demodulates the modulated sensor signal to generate a demodulated binary sensor signal. A sensor data recovery component converts the demodulated binary sensor signal into an image or another sensor parameter.

Zhang, Haifeng↗
DOE OSTI2022

The Use of Thermal Cameras for Pedestrian Detection

Visible-range camera sensors have been widely used for pedestrian detection. However, most of the methods, which employ visible-range color cameras, do not perform well under low-light and no-light conditions, e.g. during night time. Since the working principle of thermal camera sensors is mainly based on temperature and not light, they have been employed for person detection to overcome the drawbacks of visible-range sensors under these conditions. Every object gives off thermal energy, which is captured by a thermal camera sensor. When an object becomes hotter, it emits more thermal energy, and is therefore captured as much brighter or vice versa. Yet, compared to visible-range cameras, there are many additional challenges that need to be addressed when detecting pedestrians from thermal camera images. These challenges include bright hot objects close to humans, similar pixel values in an image due to weather conditions, or objects that block thermal cameras such as concrete or glass. Glass acts like a mirror for infrared radiation and reflects whatever is in front of the camera. Thus, novel methods are still required to accomplish pedestrian detection task from thermal camera images. To contribute to these efforts, we propose a new method and a modified object detection network incorporating saliency maps of thermal camera images. The features obtained from thermal images and their corresponding saliency maps are combined to obtain richer representations of pedestrian regions, and better detection performance. We perform extensive evaluations on five different datasets to compare the performance of the proposed approach with two baselines. Moreover, we evaluate and compare the transferability of these approaches by doing leave-one-out cross validation across different datasets. Furthermore, the results show that the proposed approach outperforms the baselines, and has better transferability properties across different thermal image datasets.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Low Size, Weight, and Power Space-Based Imaging System

The proposed low-SWAP solution is a FPA focus mechanism which is composed of an actuator motor, attached to a flexure that holds the camera sensor while imaging. The mechanism, while moderately complex, allows for large changes in the focal plane while also maintaining the ability to make small adjustments to produce the best possible data. This solution does the best job at accounting for thermal gradients within the optical components that tend to degrade the image quality.

42 ENGINEERING↗
DOE OSTI2020

Understanding ICF hohlraums using NIF gated laser-entrance-hole images

The newly available ns-gated laser-entrance-hole (LEH) imager on the National Ignition Facility provides routine, non-perturbative measurements of the x-ray emission from laser-heated plasmas inside the hohlraum as viewed at 19° to the hohlraum axis through one of its LEHs. Multiple images are acquired for a series of times and filter-selected x-ray energy bands within a single shot. The images provide time dependent data on phenomena including the effective radius of the LEH, the length of the gold-plasma “bubble” evolving off the interior wall surface heated by the outer beams, the evolving radius of the x-ray heated hohlraum wall, and the radius of the ablation front of the fusion capsule. Here, these measurements are explained and illustrated with sample data. These techniques are then applied to understand hohlraum behavior as a function of gas fill. For hohlraums with helium gas fill densities of 0.15 to 0.30 mg/cm 3 , synthetic images computed from simulations agree well with experimental gated LEH images when an inhibited heat transport model [Jones et al., Phys. Plasmas 24, 056312 (2017)] is used. This model can be adjusted to reproduce the expansion rate of the laser-heated plasma bubble in such a way as to improve agreement with the images. At the higher 0.6 mg/cc gas fill, the experimental images show more pronounced 3D features, resulting in slightly less good agreement with the 2D simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Engineering diamond interfaces free of dark spins

Nitrogen-vacancy (NV) centers in diamond are utilized extensively as quantum sensors for imaging fields at the nanoscale. The ultrahigh sensitivity of NV magnetometers has enabled the detection and spectroscopy of individual electron spins, with potentially far-reaching applications in condensed matter physics, spintronics, and molecular biology. However, the surfaces of these diamond sensors naturally contain electron spins, which create a background signal that can be hard to differentiate from the signal of the target spins. In this study, we develop a surface modification approach that eliminates the unwanted signal of these so-called dark electron spins. Our surface passivation technique, based on coating diamond surfaces with a thin titanium oxide (Ti⁢O 2 ) layer, reduces the dark spin density. The observed reduction in dark spin density aligns with our findings on the electronic structure of the diamond-Ti⁢O 2 interface. The reduction, from a typical value of 2000 μm −2 to a value below that set by the detection limit of our NV sensors (200 μm −2 ), results in a twofold increase in Hahn-echo coherence time of near surface NV centers. Furthermore, we derive a comprehensive spin model that connects dark spin relaxation with NV coherence, providing additional insights into the mechanisms behind the observed spin dynamics. Our findings are directly transferable to other quantum platforms, including nanoscale solid-state qubits and superconducting qubits.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Radiation, optical, power flow, and electrical diagnostics at the Z facility: Layout and techniques utilized to operate in the harsh environment

The Z machine is a current driver producing up to 30 MA in 100 ns that utilizes a wide range of diagnostics to assess accelerator performance and target behavior conduct experiments that use the Z target as a source of radiation or high pressures. Here, we review the existing suite of diagnostic systems, including their locations and primary configurations. The diagnostics are grouped in the following categories: pulsed power diagnostics, x-ray power and energy, x-ray spectroscopy, x-ray imaging (including backlighting, power flow, and velocimetry), and nuclear detectors (including neutron activation). We will also briefly summarize the primary imaging detectors we use at Z: image plates, x-ray and visible film, microchannel plates, and the ultrafast x-ray imager. The Z shot produces a harsh environment that interferes with diagnostic operation and data retrieval. We term these detrimental processes “threats” of which only partial quantifications and precise sources are known. Finally, we summarize the threats and describe techniques utilized in many of the systems to reduce noise and backgrounds.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing

Finding actionable trends in laser-based metal additive manufacturing process monitoring data is challenging owing to the diversity and complexity of the underlying physical interactions. A single monitoring solution that captures a particular process phenomenon, such as a photodiode that tracks melt pool intensity, is not alone capable of evaluating process stability or detecting flaw formation with sufficient precision for routine application in industry. In this work, to improve flaw detection performance, we adopted a data fusion approach that captures multiple process phenomena. To demonstrate this, we acquired data from laser powder bed fusion (LPBF) builds of cylindrical specimens produced with different laser spot sizes, emulating defocusing due to process faults such as thermal lensing. The resulting specimens had porosity of varying types and severity, quantified by post-build non-destructive X-ray computed tomography, Archimedes density measurements, and destructive metallographic characterization. During the build, the melt pool state was monitored with two coaxial high-speed video cameras and a temperature field imaging system. Physically intuitive low-level melt pool signatures, such as melt pool temperature, shape and size, and spatter intensity were extracted from this high-dimensional, image-based sensor data. These process signatures were subsequently used as input features in relatively simple machine learning models, such as a support vector machine, which were trained to detect laser defocusing, and in addition, predict porosity type and severity. The results show that the data fusion approach significantly enhanced system performance by reducing the overall false positive rate from ~ 0.1 to ~ 0.001 without sacrificing the true positive rate (~0.90). These results were at par with a black-box, deep machine learning approach (convolutional neural network).

36 MATERIALS SCIENCE↗
DOE OSTI2024

Using AI for Wave-front Estimation with the Rubin Observatory Active Optics System

Abstract The Vera C. Rubin Observatory will, over a period of 10 yr, repeatedly survey the southern sky. To ensure that images generated by Rubin meet the quality requirements for precision science, the observatory will use an active-optics system (AOS) to correct for alignment and mirror surface perturbations introduced by gravity and temperature gradients in the optical system. To accomplish this, Rubin will use out-of-focus images from sensors located at the edge of the focal plane to learn and correct for perturbations to the wave front. We have designed and integrated a deep-learning (DL) model for wave-front estimation into the AOS pipeline. In this paper, we compare the performance of this DL approach to Rubin’s baseline algorithm when applied to images from two different simulations of the Rubin optical system. We show the DL approach is faster and more accurate, achieving the atmospheric error floor both for high-quality images and low-quality images with heavy blending and vignetting. Compared to the baseline algorithm, the DL model is 40× faster, the median error 2× better under ideal conditions, 5× better in the presence of vignetting by the Rubin camera, and 14× better in the presence of blending in crowded fields. In addition, the DL model surpasses the required optical quality in simulations of the AOS closed loop. This system promises to increase the survey area useful for precision science by up to 8%. We discuss how this system might be deployed when commissioning and operating Rubin.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Flow visualization in high-energy-density laboratory plasmas

Flow visualization is often essential for evaluating high-energy-density (HED) laboratory plasmas, examining their dynamics, and assessing design strategies. However, imaging at fine phenomenological flow scales is a major challenge. Here, a zone-plate–based radiography system tailored for applications in HED physics is demonstrated. The burst-mode radiography system combines a dual-flash laser-generated x-ray source, a high-magnification zone plate optic, and a multiple frame hybrid complementary metal-oxide-semiconductor (hCMOS) sensor for image detection. Operating at a magnification of 42 x⁠, a pair of radiographs is measured in rapid succession, each time gated by a 4.75 keV flash of x rays lasting 100 ps. The system achieves a spatial resolution below 3 μm. The potential of this approach for applications in HED physics is exemplified by observing small-scale spike morphologies at a pusher-foam interface impacted by a strong, laser-driven blast wave.

Flow instabilities↗
DOE OSTI2020

The LSST DESC data challenge 1: generation and analysis of synthetic images for next-generation surveys

Data Challenge 1 (DC1) is the first synthetic data set produced by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). DC1 is designed to develop and validate data reduction and analysis and to study the impact of systematic effects that will affect the LSST data set. DC1 is comprised of r -band observations of 40 deg 2 to 10 yr LSST depth. In this paper, we present each stage of the simulation and analysis process: (a) generation, by synthesizing sources from cosmological N -body simulations in individual sensor-visit images with different observing conditions; (b) reduction using a development version of the LSST Science Pipelines; and (c) matching to the input cosmological catalogue for validation and testing. We verify that testable LSST requirements pass within the fidelity of DC1. We establish a selection procedure that produces a sufficiently clean extragalactic sample for clustering analyses and we discuss residual sample contamination, including contributions from inefficiency in star–galaxy separation and imperfect deblending. We compute the galaxy power spectrum on the simulated field and conclude that: (i) survey properties have an impact of 50 per cent of the statistical uncertainty for the scales and models used in DC1; (ii) a selection to eliminate artefacts in the catalogues is necessary to avoid biases in the measured clustering; and (iii) the presence of bright objects has a significant impact (2σ–6σ) in the estimated power spectra at small scales (ℓ > 1200), highlighting the impact of blending in studies at small angular scales in LSST.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

A minimum assumption approach to MEG sensor array design

Objective. Our objective is to formulate the problem of the magnetoencephalographic (MEG) sensor array design as a well-posed engineering problem of accurately measuring the neuronal magnetic fields. This is in contrast to the traditional approach that formulates the sensor array design problem in terms of neurobiological interpretability the sensor array measurements. Approach. We use the vector spherical harmonics (VSH) formalism to define a figure-of-merit for an MEG sensor array. We start with an observation that, under certain reasonable assumptions, any array of m perfectly noiseless sensors will attain exactly the same performance, regardless of the sensors' locations and orientations (with the exception of a negligible set of singularly bad sensor configurations). We proceed to the conclusion that under the aforementioned assumptions, the only difference between different array configurations is the effect of (sensor) noise on their performance. We then propose a figure-of-merit that quantifies, with a single number, how much the sensor array in question amplifies the sensor noise. Main results. We derive a formula for intuitively meaningful, yet mathematically rigorous figure-of-merit that summarizes how desirable a particular sensor array design is. We demonstrate that this figure-of-merit is well-behaved enough to be used as a cost function for a general-purpose nonlinear optimization methods such as simulated annealing. We also show that sensor array configurations obtained by such optimizations exhibit properties that are typically expected of 'high-quality' MEG sensor arrays, e.g. high channel information capacity. Significance. Our work paves the way toward designing better MEG sensor arrays by isolating the engineering problem of measuring the neuromagnetic fields out of the bigger problem of studying brain function through neuromagnetic measurements.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Laser beam pointing stabilization using analog position-sensitive diodes

Laser wire scanners have been used for ion beam profile and emittance measurements at the Spallation Neutron Source linear accelerator. Due to propagation distances exceeding 100 m, reliable measurements require laser beam stabilization, previously accomplished with a feedback control loop using a digital camera as a position sensor [Hardin et al., Opt. Express 19, 2874 (2011)]. Here, this paper presents an upgraded pointing stabilization system utilizing analog position-sensitive diodes (PSDs) as the position detector, optimized with an optical diffuser. The new system significantly surpasses the previous system by offering an order-of-magnitude improvement in radiation tolerance and a bandwidth limited only by the burst repetition rate of the laser beam. Operating with a 60-Hz burst mode laser, the new system effectively suppresses laser beam drifts up to 30 Hz, compared to the previous system’s 4 Hz limit. In addition, the amplitude of spectral components below 0.1 Hz was reduced by a factor of more than 100, which is over seven times greater than was achieved by the previous system.

Data acquisition↗
DOE OSTI2026

Generalist multimodal AI: A review of architectures, challenges and opportunities

Multimodal models are expected to be a critical component to future advances in artificial intelligence. Here, this field is starting to grow rapidly with a surge of new design elements motivated by the success of foundation models in natural language processing (NLP) and vision. It is widely hoped that further extending the foundation models to multiple modalities (e.g., text, image, video, sensor, time series, graph, etc.) will ultimately lead to generalist multimodal models, i.e. one model across different data modalities and tasks. However, there is little research that systematically analyzes recent multimodal models (particularly the ones that work beyond text and vision) with respect to the underling architecture proposed. Therefore, this work provides a fresh perspective on generalist multimodal models (GMMs) via a novel architecture and training configuration specific taxonomy. This includes factors such as Unifiability, Modularity, and Adaptability that are pertinent and essential to the wide adoption and application of GMMs. The review further highlights key challenges and prospects for the field and guide the researchers into the new advancements.

Artificial intelligence (AI)↗