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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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1,613 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Deep Learning with Quantized Neural Networks for Gravitational-wave Forecasting of Eccentric Compact Binary Coalescence

We present the first application of deep learning forecasting for binary neutron stars, neutron star–black hole systems, and binary black hole mergers that span an eccentricity range e ≤ 0.9. In this work, we train neural networks that describe these astrophysical populations, and then test their performance by injecting simulated eccentric signals in advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) noise available at the Gravitational Wave Open Science Center to (1) quantify how fast neural networks identify these signals before the binary components merge; (2) quantify how accurately neural networks estimate the time to merger once gravitational waves are identified; and (3) estimate the time-dependent sky localization of these events from early detection to merger. Our findings show that deep learning can identify eccentric signals from a few seconds (for binary black holes) up to tens of seconds (for binary neutron stars) prior to merger. A quantized version of our neural networks achieves 4× reduction in model size, and up to 2.5× inference speedup. These novel algorithms may be used to facilitate time-sensitive multimessenger astrophysics observations of compact binaries in dense stellar environments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Mining for Strong Gravitational Lenses with Self-supervised Learning

We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys’ Data Release 9. Targeting the identification of new strong gravitational lens candidates, we first create a rapid similarity search tool to discover new strong lenses given only a single labeled example. We then show how training a simple linear classifier on the self-supervised representations, requiring only a few minutes on a CPU, can automatically classify strong lenses with great efficiency. We present 1192 new strong lens candidates that we identified through a brief visual identification campaign and release an interactive web-based similarity search tool and the top network predictions to facilitate crowd-sourcing rapid discovery of additional strong gravitational lenses and other rare objects: github.com/georgestein/ssl-legacysurvey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Low-energy Electron-track Imaging for a Liquid Argon Time-projection-chamber Telescope Concept Using Probabilistic Deep Learning

The GammaTPC is an MeV-scale single-phase liquid argon time-projection-chamber gamma-ray telescope concept with a novel dual-scale pixel-based charge-readout system. It promises to enable a significant improvement in sensitivity to MeV-scale gamma rays over previous telescopes. The novel pixel-based charge readout allows for imaging of the tracks of electrons scattered by Compton interactions of incident gamma rays. The two primary contributors to the accuracy of a Compton telescope in reconstructing an incident gamma-ray’s original direction are its energy and position resolution. In this work, we focus on using deep learning to optimize the reconstruction of the initial position and direction of electrons scattered in Compton interactions, including using probabilistic models to estimate predictive uncertainty. We show that the deep-learning models are able to predict locations of Compton scatters of MeV-scale gamma rays from simulated 500 μm pixel-based data to better than 1 mm rms error and are sensitive to the initial direction of the scattered electron. We compare and contrast different deep-learning uncertainty estimation algorithms for reconstruction applications. Additionally, we show that event-by-event estimates of the uncertainty of the locations of the Compton scatters can be used to select those events that were reconstructed most accurately, leading to improvement in locating the origin of gamma-ray sources on the sky.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Galaxy Spin Classification. I. Z-wise versus S-wise Spirals with the Chirality Equivariant Residual Network

The angular momentum of galaxies (galaxy spin) contains rich information about the initial condition of the universe, yet it is challenging to efficiently measure the spin direction for the tremendous amount of galaxies that are being mapped by ongoing and forthcoming cosmological surveys. We present a machine-learning-based classifier for the Z-wise versus S-wise spirals, which can help to break the degeneracy in the galaxy spin direction measurement. The proposed chirality equivariant residual network (CE-ResNet) is manifestly equivariant under a reflection of the input image, which guarantees that there is no inherent asymmetry between the Z-wise and S-wise probability estimators. We train the model with Sloan Digital Sky Survey images, with the training labels given by the Galaxy Zoo 1 project. A combination of data augmentation techniques is used during the training, making the model more robust to be applied to other surveys. We find an ~30% increase in both types of spirals when Dark Energy Spectroscopic Instrument (DESI) images are used for classification, due to the better imaging quality of DESI. We verify that the ~7σ difference between the numbers of Z-wise and S-wise spirals is due to human bias, since the discrepancy drops to <1.8σ with our CE-ResNet classification results. We discuss the potential systematics relevant to future cosmological applications.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Reconstructing Ly$α$ Fields from Low-resolution Hydrodynamical Simulations with Deep Learning

Hydrodynamical cosmological simulations are a powerful tool for accurately predicting the properties of the intergalactic medium (IGM) and for producing mock skies that can be compared against observational data. However, the need to resolve density fluctuation in the IGM puts a stringent requirement on the resolution of such simulations, which in turn limits the volumes that can be modeled, even on the most powerful supercomputers. In this work, we present a novel modeling method that combines physics-driven simulations with data-driven generative neural networks to produce outputs that are qualitatively and statistically close to the outputs of hydrodynamical simulations employing eight times higher resolution. We show that the Ly$α$ flux field, as well as the underlying hydrodynamic fields, have greatly improved statistical fidelity over a low-resolution simulation. Importantly, the design of our neural network allows for sampling multiple realizations from a given input, enabling us to quantify the model uncertainty. Using test data, we demonstrate that this model uncertainty correlates well with the true error of the Ly$α$ flux prediction. Ultimately, our approach allows for training on small simulation volumes and applying it to much larger ones, opening the door to producing accurate Ly$α$ mock skies in volumes of Hubble size, as will be probed with DESI and future spectroscopic sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A Gigaparsec-scale Hydrodynamic Volume Reconstructed with Deep Learning

The next generation of spectroscopic surveys will map the large-scale structure of the Universe at high redshifts (2 ≤ z ≤ 5) using millions of quasar spectra, enabling major advances in constraining both the standard cosmological model and its extensions. Robust cosmological analyses of these data sets require numerical simulations that both cover gigaparsec volumes and resolve features on ∼10 kpc scales and smaller. However, running such large-volume, high-resolution hydrodynamic simulations is computationally prohibitive. We present a generative deep learning model that enhances a low-resolution, gigaparsec-scale (960 h −1 Mpc) hydrodynamic simulation using a smaller (80 h −1 Mpc) high-resolution input hydrodynamic simulation as training data. The resulting enhanced simulation reproduces the line-of-sight power spectrum to within ∼10% and the three-dimensional power spectrum at the ∼20% level at intermediate to small scales (k ≲ 2 h Mpc −1 ). Our method shows strong promise for producing realistic simulations for cosmological analyses with current surveys such as the Dark Energy Spectroscopic Instrument and upcoming next-generation experiments, but further improvements are needed to accurately recover the large-scale modes. We publicly release the enhanced hydrodynamic simulation, along with a halo catalog from a companion N-body dark matter simulation to support the calibration of data analysis pipelines for these large-scale surveys.

Convolutional neural networks↗
DOE OSTI2024

New Strong Gravitational Lenses from the DESI Legacy Imaging Surveys Data Release 9

We have conducted a search for strong gravitational lensing systems in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys Data Release 9. This is the third paper in a series. These surveys together cover ~19,000 deg 2 visible from the Northern Hemisphere, reaching a z-band AB magnitude of ~22.5. We use a deep residual neural network, trained on a compilation of known lensing systems and high-grade candidates as well as nonlenses in the same footprint. After applying our trained neural network to the survey data, we visually inspect and rank images with probabilities above a threshold which has been chosen to balance precision and recall. We have found 1895 lens candidates, of which 1512 are identified for the first time. Combining the discoveries from this work with those from Papers I (335) and II (1210), we have discovered a total of 3057 new candidates in the Legacy Surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Applications of Machine Learning to Predicting Core-collapse Supernova Explosion Outcomes

Most existing criteria derived from progenitor properties of core-collapse supernovae are not very accurate in predicting explosion outcomes. We present a novel look at identifying the explosion outcome of core-collapse supernovae using a machine-learning approach. Informed by a sample of 100 2D axisymmetric supernova simulations evolved with F ornax , we train and evaluate a random forest classifier as an explosion predictor. Furthermore, we examine physics-based feature sets including the compactness parameter, the Ertl condition, and a newly developed set that characterizes the silicon/oxygen interface. With over 1500 supernovae progenitors from 9-27 M ⊙ , we additionally train an autoencoder to extract physics-agnostic features directly from the progenitor density profiles. We find that the density profiles alone contain meaningful information regarding their explodability. Both the silicon/oxygen and autoencoder features predict the explosion outcome with ≈90% accuracy. In anticipation of much larger multidimensional simulation sets, we identify future directions in which machine-learning applications will be useful beyond the explosion outcome prediction.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

ANN↗
DOE OSTI2025

Filter-Banks for Ultra-Wideband Communications, Sensing, and Localization

Recently, filterbank multi-carrier spread spectrum (FBMC-SS) has been proposed as a candidate waveform for ultrawideband (UWB) communications. It has been noted that FBMCSS is a perfect match to this application, leading to a trivial method of matching to the required spectral mask at different regions of the world. FBMC-SS also allows easy rejection of high-power interfering signals that may appear over different parts of the UWB spectral band. In this paper, we concentrate on the use of staggered multitone spread spectrum (SMT-SS) for UWB communications. SMT makes use of offset QAM modulation to transmit data symbols over narrowband, overlapping subcarrier bands. This form of FBMC-SS is well-suited to UWB communications because it has good spectral efficiency and a flat power spectral density (PSD), resulting in good utilization of the UWB spectral mask. Additionally, we explore new methods for multi-coding that result in higher bit rates than previous FBMCSS systems. Moreover, we study methods for equalizing the UWB multipath channel and cancelling narrowband interference. Excellent performance of the proposed methods are substantiated by presenting simulation results.

99 - GENERAL AND MISCELLANEOUS↗