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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

An iterative CMB lensing estimator minimizing instrumental noise bias

Noise maps from cosmic microwave background (CMB) experiments are generally statistically anisotropic, due to scanning strategies, atmospheric conditions, or instrumental effects. Any mismodeling of this complex noise can bias the reconstruction of the lensing potential and the measurement of the lensing power spectrum from the observed CMB maps. We introduce a new CMB lensing estimator based on the maximum (MAP) reconstruction that is minimally sensitive to these instrumental noise biases. By modifying the likelihood to rely exclusively on correlations between CMB map splits with independent noise realizations, we minimize autocorrelations that contribute to biases. In the regime of many independent splits, this maximum closely approximates the optimal MAP reconstruction of the lensing potential. In simulations, we demonstrate that this method is able to determine lensing observables that are immune to any noise mismodeling with a negligible cost in signal-to-noise ratio. Our estimator enables unbiased and nearly optimal lensing reconstruction for next-generation CMB surveys.

Legrand, Louis [University of Cambridge (United Ki↗
DOE OSTI2026

Upgrade and characterization of the fast-channels of Imaging Neutral Particle Analyzers in the DIII-D tokamak

This publication presents the characterization of the recently upgraded fast (MHz) channels of the DIII-D Imaging Neutral Particle Analyzers. The new system allows for a phase space resolution of < 10 keV and < 10 cm. The main source of noise is due to the background radiation (neutrons and gammas) present in the diagnostic lab during experiments, which can hit the photomultipliers and generate large spikes in signal. Good linear correlations (r 2 > 0.9) are found between the average rate of noise spikes in the fast channels and the average neutron rate. Additional noise is caused by the scintillation of the optical fibers used to guide the signal to the acquisition system, which is induced by neutron and gamma impacts. A correlation factor r 2 > 0.6 between the baseline noise caused by this scintillation and the signals in the neutron diagnostic was found. Possible mitigation strategies, such as the use of shielding and filters, are discussed. These allow to reduce the noise spike rate by an order of magnitude and the noise baseline by a factor 4. Despite the noise, it is shown that the upgraded fast channels provide enough signal-to-noise ratio to observe coherent fluctuations of the energetic particle population.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Gravitational Waves as a Source of Large-Scale White Noise: New Constraints

A stochastic gravitational wave (GW) background sources a shear in the flow of cosmic fluid which, through non-linear mode coupling, generates large-scale white noise (LSWN) in the kurvature density field. Building on the LSWN framework of our previous work, we derive the amplitude of this GW-induced LSWN and translate the observational non-detection of LSWN into bounds on the production redshift and density of gravity waves. In particular, a minimal constraint on gravity waves with $z=0$ density parameter $Ω_\mathrm{GW0}^*$ in frequency band $f_*$ generated at redshift $z_*$ must satisfy ${z_*}^2\,Ω_\mathrm{GW0}^*<5\times10^7\,(f_*/\mathrm{nHz})^{3/2}$. This, for example, precludes the gravity waves recently detected by pulsar timing arrays \cite{NANOGrav:2023hvm} from being present before $z_*\sim10^8$, long after the quark hadron phase transition. While orders of magnitude stronger than other constraints on gravity waves, this is a minimal LSWN constraint as realistic modeling of early universe gravity wave production, including the granularity of the gravity-wave sources and the LSWN produced by the associated acoustic waves would probably tighten this constraint by orders of magnitude.

Barenboim, Gabriela [Valencia U., IFIC; Valencia U↗
DOE OSTI2025

Sparge Sampling of Molten Salts for Online Monitoring via Laser-Induced Breakdown Spectroscopy

A method was developed to sample molten salts by sparging to generate and transport aerosols to an isolated instrument for compositional analysis by laser-induced breakdown spectroscopy (LIBS). Real-time monitoring of molten salt composition is critical to developing molten salt nuclear reactors, which offer enhanced safety and efficiency. In this article, the sparge sampling method is described and compared with sampling using a Collison nebulizer. The size distribution and transport of aerosols produced from molten eutectic NaNO 3 –KNO 3 salt were compared for multiple gas flow rates (75–1200 mL min –1 ) and transport distances (0.68–2.61 m). Both methods produced aerosols ranging from 0.5 to 5.0 μm determined using a cascade impactor. Aerosols were effectively transported without pre- or trace-heating of gas lines, but transport efficiency was reduced by the formation of agglomerates. Sparge sampling was found to use less sample and less gas than a Collison nebulizer while producing a more concentrated aerosol stream (up to 5 μg L –1 ). The effects of laser energy and delay time on the signal quality of LIBS measurements of these aerosols were also studied. High energy and short delay times were found to enhance signal and repeatability, whereas signal-to-background and signal-to-noise ratios were highest at low energy and longer delay times. The capabilities of this system for online monitoring of molten salts were demonstrated with calibrations for Sr and Li with relative standard deviations of 2.6% and 1.5% and limits of detection of 380 and 180 μg g –1 , respectively.

Aerosols↗
DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments [https://arxiv.org/abs/2403.11872]. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Online thermal profile prediction for large format additive manufacturing: A hybrid CNN-LSTM based approach

Large format additive manufacturing (LFAM) is an advanced 3D printing technique that efficiently fabricates large-scale components through a layer-by-layer extrusion and deposition process. Accurate surface layer temperature monitoring is essential to prevent manufacturing failures and ensure final product quality. Traditional physics-based offline approaches for simulating thermal behavior are often inefficient and complex, posing challenges on real-time, in-situ monitoring. Here, to address this, we propose a data-driven hybrid CNN-LSTM model to predict sequential thermal images of arbitrary length using real-time infrared thermal imaging. In this approach, a Convolutional Neural Networks (CNN) is trained offline to capture spatial features, reduce dimensional complexity, and enhance time efficiency, while a stacked Long Short-Term Memory (LSTM) is applied online to capture temporal information for improved prediction of future thermal behavior in subsequent printing layers. Model performance is evaluated using MSE, SSIM, and PSNR metrics and is benchmarked against stacked LSTM and convolutional LSTM models, demonstrating superior accuracy and applicability. Additionally, to mitigate noise from moving extruders and gantry backgrounds in thermal images, a fine-tuned semantic segmentation model is implemented offline to extract printing geometry, enabling precise temperature tracking along the tool path for further thermal analysis. The frameworks developed in this study significantly advance temperature monitoring, thermal analysis, and in-situ manufacturing control for LFAM, bridging the gap between theoretical modeling and practical application.

Geometry extraction↗
DOE OSTI2025

Quartz fluorescence backgrounds in xenon particle detectors

It has been known for almost a decade that delayed photon noise with a power law time profile follows scintillation pulses in liquid xenon particle detectors. The origin of the noise has remained unknown, and in the past two years, has become an overwhelming background for low-threshold dark matter searches aimed at 𝒪⁡(10) GeV dark matter particle masses, as well as measurements of coherent neutrino-nucleus scattering of 8 B solar neutrinos. We have performed a comprehensive series of tests in a small liquid xenon cell at LBL, from which we conclude that the dominant component of this delayed photon noise is due to UV-induced fluorescence of quartz photosensor windows.

crystal optics↗
DOE OSTI2024

Understanding the Phase of Responsivity and Noise Sources in Frequency-Domain Multiplexed Readout of Transition Edge Sensor Bolometers

Abstract Cosmic microwave background (CMB) experiments have deployed focal planes with $$\mathcal {O}(10^{4})$$ O ( 10 4 ) transition edge sensor (TES) bolometers cooled to sub-Kelvin temperatures by multiplexing the readout of many TES channels onto a single pair of wires. Digital Frequency-domain Multiplexing (DfMux) is a multiplexing technique used in many CMB polarization experiments, such as the Simons Array, SPT-3 G, and EBEX. The DfMux system studied here uses LC filters with resonant frequencies ranging from 1.5 to 4.5 MHz connected to an array of TESs. Each detector has an amplitude-modulated carrier tone at the resonant frequency of its accompanying LC resonator. The signal is recovered via quadrature demodulation where the in-phase (I) component of the demodulated current is in phase with the complex admittance of the circuit and the quadrature (Q) component is orthogonal to I. Observed excess current noise in the Q component is consistent with fluctuations in the resonant frequency. This noise has been shown to be non-orthogonal to the phase of the detector’s responsivity. We present a detailed analysis of the phase of responsivity of the TES and noise sources in our DfMux readout system. Further, we investigate how modifications to the TES operating resistance and bias frequency can affect the phase of noise relative to the phase of the detector responsivity, using data from Simons Array to evaluate our predictions. We find that both the phase of responsivity and phase of noise are functions of the two tuning parameters, which can be purposefully selected to maximize signal-to-noise (SNR) ratio.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Demonstration of a 1820 channel multiplexer for transition-edge sensor bolometers

The scalability of most transition-edge sensor arrays is limited by the multiplexing technology, which combines their signals over a reduced number of wires and amplifiers. Here, in this Letter, we present and demonstrate a multiplexer design optimized for transition-edge sensor bolometers with 1820 sensors per readout unit, a factor of two more than the previous state-of-the-art. The design is optimized for cosmic microwave background imaging applications, and it builds on previous microwave superconducting quantum interference device multiplexers by doubling the available readout bandwidth to the full 4–8 GHz octave. Evaluating the key performance metrics of yield, sensitivity, and crosstalk through laboratory testing, we find an end-to-end operable detector yield of 78%, a typical nearest-neighbor crosstalk amplitude of ∼0.4%, and a median white noise level of 83 pA/$\sqrt{\textrm{Hz}}$ due to the multiplexer, corresponding to an estimated contribution of 4% to the total system noise for a ground-based cosmic microwave background telescope. Additionally, we identify a possible path toward reducing resonator loss for future designs with reduced noise. We expect these developments to alleviate the system complexity, cryogenic requirements, and cost of future large arrays of low temperature detectors.

cosmic microwave background↗
DOE OSTI2024

Informing solar blind radioluminescence imaging through a calibrated spectrum

While direct radiation detection methods offer great insight into the origin of ionizing particles and photons, their use to locate contaminated areas or concealed radioactive sources can lead to undue exposure of personnel and equipment to ionizing radiation or the potential for contamination. These same sources induce ultraviolet (UV) optical photon fluorescence in air – a process referred to as radioluminescence – that may be imaged from low dose regions over larger attenuation lengths than ionizing radiation. However, most optical detection methods are limited to low lighting conditions to image the more abundant ultraviolet-A (UV-A) photons. To extend this capability to room light or daytime conditions, the solar blind region (Ultraviolet-C (UV-C), <280 nm) can be tapped. Though the emission yield of UV-C photons is roughly two orders of magnitude lower than that in the UV-A regime, the UV-C offers dramatic improvements in signal-to-noise ratios under bright lighting conditions due to decreased background interferences. The yield of specific UV-C lines, if present in the literature at all, varies widely, which has a large impact in modeling and analyzing standoff UV-C measurement scenarios. Thus, we have captured improved radioluminescence spectra over 250–400 nm and identified observed emission peaks in ambient air. Many of the UV-C photons produced by ionizing radiation excitation result from high-energy, molecular nitrogen Gaydon-Herman transitions which have had limited study to date for this application. Relating these findings to published UV-A yields, we estimate emissions between 0.15–0.19 photons/MeV over 250–280 nm. Additionally, we also use a commercial corona-discharge imaging camera to demonstrate outdoor UV-C radiation mapping of alpha and gamma emitters from 50 and 75 m standoffs, respectively. The imaged “counts” are compared to optically modelled values and show the same trend over distance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Testing the ΛCDM Cosmological Model with Forthcoming Measurements of the Cosmic Microwave Background with SPT-3G

We forecast constraints on cosmological parameters enabled by three surveys conducted with SPT-3G, the third-generation camera on the South Pole Telescope. The surveys cover separate regions of 1500, 2650, and 6000 deg$^{2}$ to different depths, in total observing 25% of the sky. These regions will be measured to white noise levels of roughly 2.5, 9, and 12μK-armin, respectively, in cosmic microwave background (CMB) temperature units at 150 GHz by the end of 2024. The survey also includes measurements at 95 and 220 GHz, which have noise levels a factor of ∼1.2 and 3.5 times higher than 150 GHz, respectively, with each band having a polarization noise level ∼2times higher than the temperature noise. We use a novel approach to obtain the covariance matrices for jointly and optimally estimated gravitational lensing potential band powers and unlensed CMB temperature and polarization band powers. We demonstrate the ability to test the ΛCDM model via the consistency of cosmological parameters constrained independently from SPT-3G and Planck data, and consider the improvement in constraints on ΛCDM extension parameters from a joint analysis of SPT-3G and Planck data. The ΛCDM cosmological parameters are typically constrained with uncertainties up to ∼2 times smaller with SPT-3G data, compared to Planck, with the two data sets measuring significantly different angular scales and polarization levels, providing additional tests of the standard cosmological model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Broadband coherent anti-Stokes Raman scattering (BCARS) microscopy for rapid, label-free biological imaging

Broadband coherent anti-Stokes Raman scattering (BCARS) microscopy is a label-free imaging approach that provides detailed chemical information at high spatial resolution in a sample through nonlinear, coherent excitation of molecular vibrations and detection of Raman spectra. While its utility for biological imaging has been demonstrated, many aspects of this technique must mature before it can be widely adopted. One of the areas of required improvement is imaging speed—most BCARS implementations involve sample rastering, which limits imaging speed. Beam scanning can provide faster BCARS imaging but presents some unique challenges. Here, we describe a beam-scanning BCARS microscopy system that improves spatial resolution twofold and imaging speed by fivefold over a previous beam-scanning implementation. These enhancements were enabled by an improvement in supercontinuum power and the use of a sCMOS camera for its high data transfer rate and low read noise. Implementation of the sCMOS camera required correction for the significant pixel-to-pixel background and photon response nonuniformity. Here, we report on the method that we implemented for calibrating and correcting the pixel-to-pixel differences in sCMOS camera noise.

Dixon, Jessica Z. [Georgia Institute of Technology↗
DOE OSTI2024

OR22-Neuromorphic Rad Detector-PD3Ra (Final Report)

In unattended monitoring scenarios, automated radiation detection algorithms must be able to detect low signal-to-noise ratio (SNR) anomalies in a potentially dynamic and noisy background and report these anomalies in a timely fashion. Dynamic and noisy backgrounds complicate the use of simple gross-counting algorithms because they can lead to either high false positive rates or low sensitivity. Algorithms that use the entire spectrum have been the most successful in this area; notable examples are the NSCRAD algorithm developed at Pacific Northwest National Laboratory and recently the nonnegative matrix factorization approach developed at Lawrence Berkeley National Laboratory (LBNL). These approaches use either spectral regions of interest or spectral decomposition to detect threat isotopes in the background.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Deep Spectroscopy with DESI for Photometric Redshift Training and Calibration

Deep spectroscopic samples can improve photometric redshift (photo-z) estimates and reduce uncertainties on redshift distributions. Such improvements can increase the cosmological constraining power of large imaging-based experiments such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and mitigate what may be a limiting systematic effect. We present results from the “DESI-Deep pilot” program, which was designed to assess the capability of the Dark Energy Spectroscopic Instrument (DESI) on the 4m Mayall telescope to measure redshifts of galaxies as faint as expected lensing samples for early LSST data (m i ≤ 24.5). We find that DESI is remarkably efficient at this task, with redshift success rates comparable to the results of observations from 10 m class telescopes with only ∼2 × longer integration time (rather than ∼8 × longer as would be expected from aperture-area scaling), while simultaneously achieving ∼30 times larger multiplexing. We also find that the signal-to-noise ratio of the spectra scales as expected for background-limited observations even for the longest exposure times (∼7 hr) and faintest targets in the program. These results demonstrate that DESI could provide the definitive redshift sample for the early years of LSST with a modest investment of observing time. Based upon the results of this program, we provide updated predictions for the time required to collect benchmark samples for photo-z training and calibration using a variety of spectroscopic facilities. Finally, we describe a potential “DESI-Deep” survey designed to train and calibrate photo-z’s for imaging experiments, and provide forecasts of its impact on cosmological inference.

Dey, Biprateep [University of Toronto; University ↗
DOE OSTI2026

Cosmic axions revealed via amplified modulation of the ellipticity of a laser

We propose a new axion dark matter detection strategy that employs optical readout of laser beam ellipticity modulations caused by axion-induced electric fields in a microwave cavity, using electro-optic (EO) crystals, enhanced by externally injected radio-frequency (rf) power. Building upon the variance-based probing method [Phys. Rev. D 107, 103005 (2023).], we extend this concept to the optical domain: A weak probe laser interacts with an EO crystal coupled to the resonant microwave cavity field at cryogenic temperatures, and the axion-induced electric field is revealed through induced ellipticity. The injected rf signal coherently interferes with that of the axion field, amplifying the optical response and significantly improving sensitivity. While our EO-based method employs a Fabry-Pérot resonator, we do not require Michelson interferometers. Our method, hence, enables compact, high-frequency axion searches, across the 0.5–50 GHz range. Operating at cryogenic temperatures not only suppresses thermal backgrounds, but, critically, allows the probing method to mitigate the quantum noise. This approach offers a scalable path forward for axion detection over the ∼(few−200) μ⁢eV mass range—covering the preferred parameter space for the postinflationary Peccei-Quinn axion dark matter—using compact, tunable systems.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗