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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Gaussian Process Classification for Galaxy Blend Identification in LSST

Abstract A significant fraction of observed galaxies in the Rubin Observatory Legacy Survey of Space and Time (LSST) will overlap at least one other galaxy along the same line of sight, in a so-called “blend.” The current standard method of assessing blend likelihood in LSST images relies on counting up the number of intensity peaks in the smoothed image of a blend candidate, but the reliability of this procedure has not yet been comprehensively studied. Here we construct a realistic distribution of blended and unblended galaxies through high-fidelity simulations of LSST-like images, and from this we examine the blend classification accuracy of the standard peak-finding method. Furthermore, we develop a novel Gaussian process blend classifier model, and show that this classifier is competitive with both the peak finding method as well as with a convolutional neural network model. Finally, whereas the peak-finding method does not naturally assign probabilities to its classification estimates, the Gaussian process model does, and we show that the Gaussian process classification probabilities are generally reliable.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Beam Loss Assessment Through Use of Photomultiplier Tubes

The first machine in the Fermilab Accelerator chain, the Linac, delivers a 400 MeV proton beam. The first portion of the Fermilab Linac, the Drift Tube Linac, lacks the degree of instrumentation necessary for beam tuning. To compensate for this, photomultiplier tube (PMT s) based loss monitors were installed on either side of the first two drift tube tanks, but are not yet operational. One of the main goals in this is to make PMT's operational beam loss monitors for tuning. Noise reduction and peak finding on the PMT data is a requirement for this. A method for noise reduction and peak finding has been developed and implemented to produce a consistent and stable output. Future work includes integration with ACNET to automate input and output of data for analysis of beam loss.

Waggoner, Alexander↗
DOE OSTI2025

End-to-end deep learning pipeline for real-time Bragg peak segmentation: from training to large-scale deployment

X-ray crystallography reconstruction, which transforms discrete X-ray diffraction patterns into three-dimensional molecular structures, relies critically on accurate Bragg peak finding for structure determination. As X-ray free electron laser (XFEL) facilities advance toward MHz data rates (1 million images per second), traditional peak finding algorithms that require manual parameter tuning or exhaustive grid searches across multiple experiments become increasingly impractical. While deep learning approaches offer promising solutions, their deployment in high-throughput environments presents significant challenges in automated dataset labeling, model scalability, edge deployment efficiency, and distributed inference capabilities. We present an end-to-end deep learning pipeline with three key components: (1) a data engine that combines traditional algorithms with our peak matching algorithm to generate high-quality training data at scale, (2) a modular architecture that scales from a few million to hundreds of million parameters, enabling us to train large expert-level models offline while deploying smaller, distilled models at the edge, and (3) a decoupled producer-consumer architecture that separates specialized data source layer from model inference, enabling flexible deployment across diverse computing environments. Using this integrated approach, our pipeline achieves accuracy comparable to traditional methods tuned by human experts while eliminating the need for experiment-specific parameter tuning. Although current throughput requires optimization for MHz facilities, our system's scalable architecture and demonstrated model compression capabilities provide a foundation for future high-throughput XFEL deployments.

Wang, Cong↗
NASA NTRS2006

SABER (TIMED) and MLS (UARS) Temperature Observations of Mesospheric and Stratospheric QBO and Related Tidal Variations

More than three years of temperature observations from the SABER (TIMED) and MLS WARS) instruments are analyzed to study the annual and inter-annual variations extending from the stratosphere into the upper mesosphere. The SABER measurements provide data from a wide altitude range (15 to 95 km) for the years 2002 to 2004, while the MLS data were taken in the 16 to 55 km altitude range a decade earlier. Because of the sampling properties of SABER and MLS, the variations with local solar time must be accounted for when estimating the zonal mean variations. An algorithm is thus applied that delineates with Fourier analysis the year-long variations of the migrating tides and zonal mean component. The amplitude of the diurnal tide near the equator shows a strong semiannual periodicity with maxima near equinox, which vary from year to year to indicate the influence from the Quasi-biennial Oscillation (QBO) in the zonal circulation. The zonal mean QBO temperature variations are analyzed over a range of latitudes and altitudes, and the results are presented for latitudes from 48"s to 48"N. New results are obtained for the QBO, especially in the upper stratosphere and mesosphere, and at mid-latitudes. At Equatorial latitudes, the QBO amplitudes show local peaks, albeit small, that occur at different altitudes. From about 20 to 40 km, and within about 15" of the Equator, the amplitudes can approach 3S K for the stratospheric QBO or SQBO. For the mesospheric QBO or MQBO, we find peaks near 70 km, with temperature amplitudes reaching 3.5"K, and near 85 km, the amplitudes approach 2.5OK. Morphologically, the amplitude and phase variations derived from the SABER and MLS measurements are in qualitative agreement. The QBO amplitudes tend to peak at the Equator but then increase again pole-ward of about 15" to 20'. The phase progression with altitude varies more gradually at the Equator than at mid-latitudes. A comparison of the observations with results from the Numerical Spectral Model (NSM) reveals that there is qualitative agreement. The NSM generates the QBO extending from the stratosphere into the upper mesosphere, with temperature variations extending to mid latitudes, but the predicted amplitudes are smaller than those observed.

Huang, Frank T.↗
DOE OSTI2023

Detecting Dwarf Galaxies Around NGC 55

In order to improve our understanding of the satellite populations of low-mass galaxies, the DECam Local Volume Exploration (DELVE) – DEEP Survey is performing a search for satellites around the isolated, low-mass galaxy, NGC 55. As part of this search, we characterize our dwarf galaxy detection sensitivity by injecting artificial dwarf galaxies into our data set and measuring the fraction that we can recover. Additionally, we inject and recover artificial dwarfs to test how the blending of resolved stars affects the detection of dwarf galaxies. We inject 650 artificial dwarfs across 26 DELVE-DEEP coadded images of the NGC 55 halo and use the software tool, Source Extractor, to perform source recovery. A dwarf-search pipeline is then run on our dwarf-injected catalogs, utilizing the photutils function, find peaks, and detections are recorded as a function of dwarf size and luminosity. Our results demonstrate that we are most sensitive to large, bright dwarf galaxies, and our sensitivity begins to decrease as we approach DELVE-DEEP’s detection limit or blending causes significant reductions in recovered magnitude. We also find that our results are hindered by the limitations of star-galaxy separation applied to distant, faint sources, which is possibly a consequence of the blending of small star clusters within dwarf galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2012

Vehicle Detection for RCTA/ANS (Autonomous Navigation System)

Using a stereo camera pair, imagery is acquired and processed through the JPLV stereo processing pipeline. From this stereo data, large 3D blobs are found. These blobs are then described and classified by their shape to determine which are vehicles and which are not. Prior vehicle detection algorithms are either targeted to specific domains, such as following lead cars, or are intensity- based methods that involve learning typical vehicle appearances from a large corpus of training data. In order to detect vehicles, the JPL Vehicle Detection (JVD) algorithm goes through the following steps: 1. Take as input a left disparity image and left rectified image from JPLV stereo. 2. Project the disparity data onto a two-dimensional Cartesian map. 3. Perform some post-processing of the map built in the previous step in order to clean it up. 4. Take the processed map and find peaks. For each peak, grow it out into a map blob. These map blobs represent large, roughly vehicle-sized objects in the scene. 5. Take these map blobs and reject those that do not meet certain criteria. Build descriptors for the ones that remain. Pass these descriptors onto a classifier, which determines if the blob is a vehicle or not. The probability of detection is the probability that if a vehicle is present in the image, is visible, and un-occluded, then it will be detected by the JVD algorithm. In order to estimate this probability, eight sequences were ground-truthed from the RCTA (Robotics Collaborative Technology Alliances) program, totaling over 4,000 frames with 15 unique vehicles. Since these vehicles were observed at varying ranges, one is able to find the probability of detection as a function of range. At the time of this reporting, the JVD algorithm was tuned to perform best at cars seen from the front, rear, or either side, and perform poorly on vehicles seen from oblique angles.

Brennan, Shane↗
DOE OSTI2023

mzapy : An Open-Source Python Library Enabling Efficient Extraction and Processing of Ion Mobility Spectrometry-Mass Spectrometry Data in the MZA File Format

We have recently reported MZA, a new and simple mass spectrometry data structure based on the broadly supported HDF5 format and created to facilitate software development. While this format is inherently supportive of application development, the availability of a core library with standard mass spectrometry utilities greatly facilitates fast software development. Here, we present a Python library, mzapy, for efficient extraction and processing of mass spectrometry data in the MZA format. In addition to raw data extraction, mzapy contains supporting utilities enabling tasks including calibration, signal processing, peak finding, and generating plots. Being implemented in pure Python with minimal and largely standardized dependencies makes mzapy uniquely suited to application development in the multi-omics domain. The free and open source mzapy is built with extensibility in mind, and future development will support cloud computing and artificial intelligence/machine learning applications. The software source code is freely available at https://github.com/PNNL-m-q/mzapy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Prototype acoustic positioning system for the Pacific Ocean Neutrino Experiment

We present the design and initial performance characterization of the prototype acoustic positioning system intended for the Pacific Ocean Neutrino Experiment. It comprises novel piezo-acoustic receivers with dedicated filtering- and amplification electronics installed in P-ONE instruments and is complemented by a commercial system comprised of cabled and autonomous acoustic pingers for sub-sea installation manufactured by Sonardyne Ltd. We performed an in-depth characterization of the acoustic receiver electronics and their acoustic sensitivity when integrated into P-ONE pressure housings. These show absolute sensitivities of up to -125 dB re V2/μPa2 in a frequency range of 10–40 kHz. We furthermore conducted a positioning measurement campaign in the ocean by deploying three autonomous acoustic pingers on the seafloor, as well as a cabled acoustic interrogator and a P-ONE prototype module deployed from a ship. Using a simple peak-finding detection algorithm, we observe high accuracy in the tracking of relative ranging times at approximately 230–280 μs at distances of up to 1600 m, which is sufficient for positioning detectors in a cubic-kilometer detector and which can be further improved with more involved detection algorithms. The tracking accuracy is further confirmed by independent ranging of the Sonardyne system and closely follows the ship's drift in the wind measured by GPS. The absolute positioning shows the same tracking accuracy with its absolute precision only limited by the large uncertainties of the deployed pinger positions on the seafloor.

Data analysis↗
DOE OSTI2023

Pure spin current injection of single-layer monochalcogenides

We compute the spectrum of pure spin current injection in ferroelectric single-layer SnS, SnSe, GeS, and GeSe. The formalism takes into account the coherent spin dynamics of optically excited conduction states split in energy by spin–orbit coupling. The velocity of the electron's spins is calculated as a function of incoming photon energy and angle of linearly polarized light within a full electronic band structure scheme using density functional theory. We find peak speeds of 520, 360, 270 and 370 Km s -1 for SnS, SnSe, GeS and GeSe, respectively which are an order of magnitude larger than those found in bulk semiconductors, e.g., GaAs and CdSe. Interestingly, the spin velocity is almost independent of the direction of polarization of light in a range of photon energies. Our results demonstrate that single-layer SnS, SnSe, GeS and GeSe are candidates to produce on demand spin-current in spintronics applications.

2D-monochalcogenides↗
DOE OSTI2024

Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors

Abstract Accurate observation of two or more particles within a very narrow time window has always been a challenge in modern physics. It creates the possibility of correlation experiments, such as the ground-breaking Hanbury Brown–Twiss experiment, leading to new physical insights. For low-energy electrons, one possibility is to use a Microchannel plate with subsequent delay lines for the readout of the incident particle hits, a setup called a Delay Line Detector. The spatial and temporal coordinates of more than one particle can be fully reconstructed outside a region called the dead radius. For interesting events, where two electrons are close in space and time, the determination of the individual positions of the electrons requires elaborate peak finding algorithms. While classical methods work well with single particle hits, they fail to identify and reconstruct events caused by multiple nearby particles. To address this challenge, we present a new spatiotemporal machine learning model to identify and reconstruct the position and time of such multi-hit particle signals. This model achieves a much better resolution for nearby particle hits compared to the classical approach, removing some of the artifacts and reducing the dead radius a factor of eight. We show that machine learning models can be effective in improving the spatiotemporal performance of delay line detectors.

Computer Science↗
DOE OSTI2025

Neural architecture codesign for fast physics applications

We develop a pipeline to streamline neural architecture codesign for physics applications to reduce the need for ML expertise when designing models for novel tasks. Our method employs neural architecture search and network compression in a two-stage approach to discover hardware efficient models. This approach consists of a global search stage that explores a wide range of architectures while considering hardware constraints, followed by a local search stage that fine-tunes and compresses the most promising candidates. We exceed performance on various tasks and show further speedup through model compression techniques such as quantization-aware-training and neural network pruning. We synthesize the optimal models to high level synthesis code for FPGA deployment with the hls4ml library. Additionally, our hierarchical search space provides greater flexibility in optimization, which can easily extend to other tasks and domains. We demonstrate this with two case studies: Bragg peak finding in materials science and jet classification in high energy physics, achieving models with improved accuracy, smaller latencies, or reduced resource utilization relative to the baseline models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Preliminary Thermoluminescent Dosimeter Glow Curve Analysis with Automated Glow Peak Identification for LiF Mg,Ti

When appropriately analyzed, thermoluminescent dosimeter glow curve analysis allows for improved quantification of thermoluminescent material behavior while flagging abnormalities. The mathematical separation of a glow curve into contributions from energetically unique trap states, or glow curve analysis, may be used to remove undesired effects of signal fading for complex materials. A generalized glow curve analysis software for the separation of glow curves is presented in this paper. Written in C ++ , the software uses the first-order kinetics model with automatic peak identification. The automatic identification of peaks is achieved through a unique peak-finding algorithm. Here, the program was performance tested using experimental glow curve data from LiF:Mg,Ti, and comparative results are presented.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Inferring the shape of data: a probabilistic framework for analysing experiments in the natural sciences

A critical step in data analysis for many different types of experiments is the identification of features with theoretically defined shapes in N -dimensional datasets; examples of this process include finding peaks in multi-dimensional molecular spectra or emitters in fluorescence microscopy images. Identifying such features involves determining if the overall shape of the data is consistent with an expected shape; however, it is generally unclear how to quantitatively make this determination. In practice, many analysis methods employ subjective, heuristic approaches, which complicates the validation of any ensuing results—especially as the amount and dimensionality of the data increase. Here, we present a probabilistic solution to this problem by using Bayes’ rule to calculate the probability that the data have any one of several potential shapes. This probabilistic approach may be used to objectively compare how well different theories describe a dataset, identify changes between datasets and detect features within data using a corollary method called Bayesian Inference-based Template Search; several proof-of-principle examples are provided. Altogether, this mathematical framework serves as an automated ‘engine’ capable of computationally executing analysis decisions currently made by visual inspection across the sciences.

Science & Technology - Other Topics↗
DOE OSTI2023

Mu2e - Extinction Monitor Research & Development

Current efforts are being conducted at Fermi National Laboratory to study potential violations in accepted theory that would otherwise suggest a restructuring of our fundamental understanding of the universe. Mu2e is one of these frontier projects that studies charged lepton flavor violation (CLFV) which if observed, would suggest physics beyond the Standard Model. Therefore, this note encompasses several projects that contribute to the fruition of Mu2e investigations. Due to the broad range of disciplinary inconsistencies that each project requires, all the work is being presented as a means of justifying contribution to Mu2e. The projects are comprised of a G4Beamline simulation analyzing 8GeV proton beam interaction with a titanium window of Recycler ring extinction rates using three Cherenkov radiation-based detectors and supplemental work for the implementation of a micro–Telecommunications Computing Architecture (MicroTCA) crate to establish a peak finding algorithm to ensure that the out-of-time beam is less than 10^-10 fractional level along with inefficiency analysis on scintillation counters for the Cosmic-Ray Veto (CRV) analysis. Preliminary results have been achieved for extinction rate simulation by achieving coincidence rates for 2/3-fold and 3/3-fold on the detectors in the order of 10^-9 and 10^-10, respectively. Only preliminary results of a triangular counter and four rectangular di-counters for the CRV have been realized but other non-experimental contributions were made to the development of the MicroTCA crate.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1986

Picture-Element Comparator

Proposed circuit, called "neighborhood comparison operator," compares data from neighboring picture elements (pixels) to find peaks, ridges, and valleys in picture data. Circuit also able to expand or shrink pixel regions. Circuit concept developed for image-processing computers. Circuit handles data stream of 12-bit pixels rather than conventional 8 or 16 bits. Consist entirely of standard logic chips.

Gennery, D. B.↗
NASA NTRS1988

The search for solar gravity modes

A solar oscillations observing program of more than 13 yr is reviewed. The observations are most sensitive to low degree solar modes and were used for the study of long period p-mode and g-mode oscillations. At the start of the 1987 observing season (summer) long-standing problems with the instrument were corrected which (along with good weather) allowed the cleanest set of data to date. The search for evidence of g-modes in this data is described. Analysis of this data shows good evidence for g-modes. Various methods were used for mode identification with a statistical search for a simple pattern of even spacing in period selected as the most robust. Using this method, a possible g-mode identification was made with an asymptotic period separation T0 = 37.1 min. This identification is consistent with a rotation splitting of 1.6 MicroHz. Tests with randomly generated spectral peaks find as significant a possible set of modes in only 2 out of 100 cases.

Henning, Harald M.↗
NASA NTRS1995

The solar/interplanetary event of 14 April 1994 observed by Yohkoh/SXT

The polar crown event of April 14 1994 is one of the largest scale eruptive events observed by the Yohkoh/SXT. Associated with the formation of an arcade of soft X-ray loops at the Sun was the detection of an interplanetary forward/reverse shock event by the Ulysses spacecraft some 4-7 days later. The relationship between the coronal and interplanetary signatures of these events is important if we are to address fully the initialization and consequent acceleration of interplanetary phenomena, such as CMEs and counter-streaming electrons, originating at the Sun. From detailed analysis of the energetics of the arcade formed during the eruption of April 14 1994, we find peak temperatures and emission measures of approximately 5MK and approximately 10(exp 48)cm(exp -3) respectively. The total thermal content of the arcade loop structure observed in soft X-rays is calculated to be some 5 x 10(exp 29) ergs. The development of these parameters as the event proceeds and their relationship to the dynamics of the eruption are investigated. Although spanning a longitudinal range of some 150 degrees the April 14 event displayed the typical helmet streamer structure normally associated with coronal mass ejections These helmet streamers are thought to be related to the global solar magnetic field through the heliospheric current sheet (HCS). The arcade formation, together with the eruption of material into interplanetary space, signifies a large-scale reconfiguration of the coronal magnetic field. We examine the effects of the formation of such a coronal arcade structure on the HCS and discuss the dynamics involved with the passage of a large scale disturbance through the interplanetary magnetic field.

Alexander, D.↗
NASA NTRS2003

Global and Local Helioseismic Studies of Solar Convection Zone Dynamics Using SOI-MDI on SOHO

Our joint collaborative analyses of global mode data to characterize the solar differential rotation (e.g. Thompson et al. 1996, Schou et al. 1998), and most recently to detect and analyze temporal variations in angular velocity Omega profiles both within the convection zone and in the deeper radiative interior (e.g. Howe et al 2000a,b; Toomre et al. 2000), have led to a series of fascinating discoveries. These should be pursued further as the solar cycle continues. The physical deductions being made from these studies have been greatly strengthened by utilizing both SOI-MDI and GONG data in order to have two independent observational realizations of Doppler images spanning a five-year interval, using two separate procedures to determine global mode splittings, and then analyzing those splitting data sets using both RLS and SOLA inversion procedures. There are considerable subtleties in the effects of instrumental response functions and calibrations, sensitivity of peak finding algorithms and their mode leakage estimates, and stochastic variations in mode amplitudes that can all contribute to apparent changes in the Omega profiles being inferred from sequences of helioseismic data. We have come to understand the implications of many of these calibration and analysis steps, greatly aided by frequent multi-week collaborative working sessions in our Helioseismic Analysis Facility (HAF) at JILA involving many members of the SO1 dynamics and inversion team, including most of our Co-Is during the summer months when we hold intensive working sessions. Considerable further focused attention is required in a collaborative setting on such global mode issues as we continue studying the changing sun.

Toomre, Juri↗