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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

A comparative study of computational methods in cosmic gas dynamics

Many theoretical investigations of fluid flows in astrophysics require extensive numerical calculations. The selection of an appropriate computational method is, therefore, important for the astronomer who has to solve an astrophysical flow problem. The present investigation has the objective to provide an informational basis for such a selection by comparing a variety of numerical methods with the aid of a test problem. The test problem involves a simple, one-dimensional model of the gas flow in a spiral galaxy. The numerical methods considered include the beam scheme, Godunov's method (G), the second-order flux-splitting method (FS2), MacCormack's method, and the flux corrected transport methods of Boris and Book (1973). It is found that the best second-order method (FS2) outperforms the best first-order method (G) by a huge margin.

Van Albada, G. D.↗
NASA NTRS1986

Thermal infrared opacity of the Mars atmosphere

An analytical technique is presented for the deriving Mars atmosphere opacity from data furnished by the IR Thermal Mapper (IRTM) instruments on the Viking landers. Each IRTM features four telescopes for viewing Mars in six spectral bands and seven spatial fields of view. The present study focuses on 20 microns measurements of the thermal inertia, 0.3-3.0 microns data for the surface albedo, and, finally, the 7, 9, and 15 microns data for the actual opacity derivations. The model takes into account the atmospheric temperature profile, scattering by dust, the surface emissivity and the variation of the surface/atmosphere temperature discontinuity. Analyses are carried out of the possible error factors and the model is used to generate opacity histories for the two Viking Lander sites. The results, when compared with other astronomical data, indicate that the method is sufficiently accurate to use opacity data to analyze the processes of storm genesis and to study local storms.

Martin, T. Z.↗
NASA NTRS1988

Overview of lunar-based astronomy

The opportunities along with the advantages and disadvantages of the Moon for astronomical observatories are carefully and methodically considered. Taking a relatively unbiased approach, it was concluded that lunar observatories will clearly be a major factor in the future of astronomy in the next century. He concludes that ground based work will continue because of its accessibility and that Earth orbital work will remain useful, primarily for convenience of access in constructing and operating very large space systems. Deep space studies will feature not only probes but extensive systems for extremely long baseline studies at wavelengths from gamma rays through visible and IR out to radio is also a conclusion drawn, along with the consideration that lunar astronomy will have found important permanent applications along lines such as are discussed at the present symposium and others quite unsuspected today.

Smith, Harlan J.↗
NASA NTRS2015

Aligning, Bonding, and Testing Mirrors for Lightweight X-ray Telescopes

High-resolution, high throughput optics for x-ray astronomy entails fabrication of well-formed mirror segments and their integration with arc-second precision. In this paper, we address issues of aligning and bonding thin glass mirrors with negligible additional distortion. Stability of the bonded mirrors and the curing of epoxy used in bonding them were tested extensively. We present results from tests of bonding mirrors onto experimental modules, and on the stability of the bonded mirrors tested in x-ray. These results demonstrate the fundamental validity of the methods used in integrating mirrors into telescope module, and reveal the areas for further investigation. The alignment and integration methods are applicable to the astronomical mission concept such as STAR-X, the Survey and Time-domain Astronomical Research Explorer.

mirror alignment↗
DOE OSTI2024

Assessing and benchmarking the fidelity of posterior inference methods for astrophysics data analysis

In this era of large and complex astronomical survey data, interpreting, validating, and comparing inference techniques becomes increasingly difficult. This is particularly critical for emerging inference methods like Simulation-Based Inference (SBI), which offer significant speedup potential and posterior modeling flexibility, especially when deep learning is incorporated. We present a study to assess and compare the performance and uncertainty prediction capability of Bayesian inference algorithms – from traditional MCMC sampling of analytic functions to deep learning-enabled SBI. We focus on testing the capacity of hierarchical inference modeling in those scenarios. Before we extend this study to cosmology, we first use astrophysical simulation data to ensure interpretability. We demonstrate a probabilistic programming implementation of hierarchical and non-hierarchical Bayesian inference using simulations derived from the DeepBench software library, a benchmarking tool developed by our group that generates simple and controllable astrophysical objects from first principles. This study will enable astronomers and physicists to harness the inference potential of these methods with confidence.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Detecting Anomalous Images in Astronomical Datasets

Abstract Environmental and instrumental conditions can cause anomalies in astronomical images, which can potentially bias all kinds of measurements if not excluded. Detection of the anomalous images is usually done by human eyes, which is slow and sometimes not accurate. This is an important issue in weak lensing studies, particularly in the era of large-scale galaxy surveys, in which image qualities are crucial for the success of galaxy shape measurements. In this work we present two automatic methods for detecting anomalous images in astronomical data sets. The anomalous features can be divided into two types: one is associated with the source images, and the other appears on the background. Our first method, called the entropy method, utilizes the randomness of the orientation distribution of the source shapes and the background gradients to quantify the likelihood of an exposure being anomalous. Our second method involves training a neural network (autoencoder) to detect anomalies. We evaluate the effectiveness of the entropy method on the Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS) and Dark Energy Camera Legacy Survey (DECaLS DR3) data. In CFHTLenS, with 1171 exposures, the entropy method outperforms human inspection by detecting 12 of the 13 anomalous exposures found during human inspection and uncovering 10 new ones. In DECaLS DR3, with 17112 exposures, the entropy method detects a significant number of anomalous exposures while keeping a low false-positive rate. We find that although the neural network performs relatively well in detecting source anomalies, its current performance is not as good as the entropy method.

Astronomy & Astrophysics↗
NASA NTRS2010

A New Compression Method for FITS Tables

As the size and number of FITS binary tables generated by astronomical observatories increases, so does the need for a more efficient compression method to reduce the amount disk space and network bandwidth required to archive and down1oad the data tables. We have developed a new compression method for FITS binary tables that is modeled after the FITS tiled-image compression compression convention that has been in use for the past decade. Tests of this new method on a sample of FITS binary tables from a variety of current missions show that on average this new compression technique saves about 50% more disk space than when simply compressing the whole FITS file with gzip. Other advantages of this method are (1) the compressed FITS table is itself a valid FITS table, (2) the FITS headers remain uncompressed, thus allowing rapid read and write access to the keyword values, and (3) in the common case where the FITS file contains multiple tables, each table is compressed separately and may be accessed without having to uncompress the whole file.

Pence, William↗
DOE OSTI2022

J-PLUS: Support vector regression to measure stellar parameters

Stellar parameters are among the most important characteristics in studies of stars which, in traditional methods, are based on atmosphere models. However, time, cost, and brightness limits restrain the efficiency of spectral observations. The Javalambre Photometric Local Universe Survey (J-PLUS) is an observational campaign that aims to obtain photometry in 12 bands. Owing to its characteristics, J-PLUS data have become a valuable resource for studies of stars. Machine learning provides powerful tools for efficiently analyzing large data sets, such as the one from J-PLUS, and enables us to expand the research domain to stellar parameters. The main goal of this study is to construct a support vector regression (SVR) algorithm to estimate stellar parameters of the stars in the first data release of the J-PLUS observational campaign. The training data for the parameter's regressions are featured with 12-waveband photometry from J-PLUS and are crossidentified with spectrum-based catalogs. These catalogs are from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, the Apache Point Observatory Galactic Evolution Experiment, and the Sloan Extension for Galactic Understanding and Exploration. We then label them with the stellar effective temperature, the surface gravity, and the metallicity. Ten percent of the sample is held out to apply a blind test. We develop a new method, a multi-model approach, in order to fully take into account, the uncertainties of both the magnitudes and the stellar parameters. The method utilizes more than 200 models to apply the uncertainty analysis. We present a catalog of 2 493 424 stars with the root mean square error of 160 K in the effective temperature regression, 0.35 in the surface gravity regression, and 0.25 in the metallicity regression. We also discuss the advantages of this multi-model approach and compare it to other machine-learning methods.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1974

On the determination of certain astronomical, selenodesic, and gravitational parameters of the moon

A method was examined for joint construction of a selenocentric fundamental system which can be realized by a coordinate catalog of reference contour points uniformly positioned over the entire lunar surface, and determination of the parameters characterizing the gravitational field, rotation, and orbital motion of the moon. Characteristic of the problem formulation is the introduction of a new complex of inconometric measurements which can be made using pictures obtained from an artificial lunar satellite. The proposed method can be used to solve similar problems on any other planet for which surface images can be obtained from a spacecraft. Characteristic of the proposed technique for solving the problem is the joint statistical analysis of all forms of measurements: orbital iconometric, earth-based trajectory, and also a priori information on the parameters in question which is known from earth-based astronomical studies.

Aleksashin, Y. P.↗
DOE OSTI2022

DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification

With increased adoption of supervised deep learning methods for work with cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly important. In the context of morphological classification of galaxies, we study the effects of perturbations in imaging data. In particular, we examine the consequences of using neural networks when training on baseline data and testing on perturbed data. We consider perturbations associated with two primary sources: (a) increased observational noise as represented by higher levels of Poisson noise and (b) data processing noise incurred by steps such as image compression or telescope errors as represented by one-pixel adversarial attacks. We also test the efficacy of domain adaptation techniques in mitigating the perturbation-driven errors. We use classification accuracy, latent space visualizations, and latent space distance to assess model robustness in the face of these perturbations. For deep learning models without domain adaptation, we find that processing pixel-level errors easily flip the classification into an incorrect class and that higher observational noise makes the model trained on low-noise data unable to classify galaxy morphologies. On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy up to 23% on data with higher observational noise. Domain adaptation also increases up to a factor of ${\approx}2.3$ the latent space distance between the baseline and the incorrectly classified one-pixel perturbed image, making the model more robust to inadvertent perturbations. Successful development and implementation of methods that increase model robustness in astronomical survey pipelines will help pave the way for many more uses of deep learning for astronomy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Automated Stellar Spectra Classification with Ensemble Convolutional Neural Network

Large sky survey telescopes have produced a tremendous amount of astronomical data, including spectra. Machine learning methods must be employed to automatically process the spectral data obtained by these telescopes. Classification of stellar spectra by applying deep learning is an important research direction for the automatic classification of high-dimensional celestial spectra. In this paper, a robust ensemble convolutional neural network (ECNN) was designed and applied to improve the classification accuracy of massive stellar spectra from the Sloan digital sky survey. We designed six classifiers which consist six different convolutional neural networks (CNN), respectively, to recognize the spectra in DR16. Then, according the cross-entropy testing error of the spectra at different signal-to-noise ratios, we integrate the results of different classifiers in an ensemble learning way to improve the effect of classification. The experimental result proved that our one-dimensional ECNN strategy could achieve 95.0% accuracy in the classification task of the stellar spectra, a level of accuracy that exceeds that of the classical principal component analysis and support vector machine model.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1972

A foldable 4.27 meter (14 foot) spacecraft antenna

The problems and solutions associated with the design, fabrication, and testing of a large, lightweight, radial-rib, folding, spacecraft antenna reflector are discussed. The antenna reflector was designed as a highly efficient communications system for outer-planet missions extending as far as approximately 59.839 x 10 to the 11th power meters (40 astronomical units) from the sun. The methods used to obtain a lightweight precision rib surface, the evaluation and fabrication of the metallic reflector mesh surface, and the surface-evaluation techniques used on the assembled antenna reflector are included.

Starkey, D. J.↗
NASA NTRS2002

An Introduction to High-Energy Astrophysics: Detectors, Techniques and Missions

A series of lectures will be given to acquaint the amateur astronomer with historical, present and future methods of observation in high-energy astrophysics. These will begin with the basic principles of how photons of high energy are detected and their energy measured. The detectors and data distribution methods will be described. Finally, a comprehensive description of the experiments and missions in high-energy astrophysics will be reviewed.

Fishman, Gerald J.↗
DOE OSTI2025

Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions

Neural posterior estimation (NPE), a type of amortized variational inference, is a computationally efficient means of constructing probabilistic catalogs of light sources from astronomical images. To date, NPE has not been used to perform inference in models with spatially varying covariates. However, ground-based astronomical images exhibit spatially varying sky backgrounds and point spread functions (PSFs), and accounting for this variation is essential for constructing accurate catalogs of imaged light sources. In this work, we introduce a novel NPE-based cataloging method that trains an inference network with semisynthetic astronomical images generated using PSFs and backgrounds sampled from the Sloan Digital Sky Survey. In experiments with semisynthetic images, we evaluate the method on key cataloging tasks: light source detection, star/galaxy separation, and flux measurement. A “generalist” inference network—trained with diverse PSFs and backgrounds—performs as well as a “specialist” network even when both are evaluated on the specialist’s particular PSF/background combination. This result suggests that a single NPE network can generalize across spatial variations, eliminating the need for retraining on each observational condition.

astronomy image processing↗
DOE OSTI2024

helioLuna: Prospect for cislunar spacecraft and near-earth asteroid detection using heliostat fields at night

We experimentally and computationally investigate a proposed frequency-domain method for detecting and tracking cislunar spacecraft and near-earth asteroids using heliostat fields at night. Unlike imaging, which detects spacecraft and asteroids by their streak in sidereally-fixed long-exposure photographs, our proposed detection method oscillates the orientation of heliostats concentrating light from the stellar field and measures the light’s photocurrent power spectrum at sub-milliHertz resolution. If heliostat oscillation traces out an ellipse fixed in the galactic coordinate system, spacecraft or asteroids produce a peak in photocurrent power spectrum at a frequency slightly shifted from the starlight peak. The frequency shift is on the scale of milliHertz and proportional to apparent angular rate relative to sidereal. Relative phase corresponds to relative angular position, enabling tracking. A potential advantage of this frequency-domain method over imaging is that detectivity improves with apparent angular rate and number of heliostats. Since heliostats are inexpensive compared to an astronomical observatory and otherwise unused at night, the proposed method may cost-effectively augment observatory systems such as NASA’s Asteroid Terrestrial-impact Last Alert System (ATLAS).

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2019

Spectrally Resolved Synthetic Aperture Imaging Interferometer: Final Report of NASA Innovative Advanced Concepts - Phase 1 Study

The Spectrally Resolved Synthetic-Aperture Imaging Interferometer (SRSAII) is a system proposed to provide high-resolution and high-sensitivity measurements of astronomical objects. SRSAII uses long baseline interferometric methods to achieve the resolution and low-noise, high time-precision detection to achieve the sensitivity. The primary goal of the SRSAII study was to lay out a framework for using new optical physics technologies to directly resolve, both spatially and spectrally, the disk of an exoplanet. In addition to the ambitious goal of directly resolving an exoplanet, the SRSAII team also sought to identify science opportunities achievable with intermediate system configurations which may offer resolution significantly higher than the current state of the art, but insufficient for direct resolution of an exoplanetary disk. An operational SRSAII system can function with essentially arbitrarily large baselines, achieving correspondingly high angular resolution. The primary limitation occurs in the system sensitivity, which became the major technical focus for study. In this report, we compare the predicted performance (sensitivity in SNR (Signal-to-Noise Ratio) along with angular resolution) of three interferometric techniques: direct detection (also known as homodyne interferometry), multi-channel intensity interferometry (using the Hanbury Brown and Twiss effect), and multi-channel heterodyne interferometry (using an optical frequency comb as a local oscillator). Additionally, quantum-assisted interferometry is also explored as a prospective enhancement of established methods. This report presents a survey of the technologies that enable the SRSAII techniques - optical frequency combs, single photon detectors, and photonic integrated circuits. These technologies are the basis of methods critical to SRSAII's success: precision timing, length and frequency metrology, sensitive photodetection, fine-scale wavelength filtering, and dense multi-channel operation. Lastly, we give some notional performance metrics and propose some possible experimental observations.

High Resolultion↗
DOE OSTI2021

Self-supervised Representation Learning for Astronomical Images

Sky surveys are the largest data generators in astronomy, making automated tools for extracting meaningful scientific information an absolute necessity. We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks. These representations can be directly used as features, or fine-tuned, to outperform supervised methods trained only on labeled data. We apply a contrastive learning framework on multiband galaxy photometry from the Sloan Digital Sky Survey (SDSS), to learn image representations. We then use them for galaxy morphology classification and fine-tune them for photometric redshift estimation, using labels from the Galaxy Zoo 2 data set and SDSS spectroscopy. In both downstream tasks, using the same learned representations, we outperform the supervised state-of-the-art results, and we show that our approach can achieve the accuracy of supervised models while using 2-4 times fewer labels for training. The codes, trained models, and data can be found at https://portal.nersc.gov/project/dasrepo/self-supervised-learning-sdss.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2007

Photometric Calibration of Consumer Video Cameras

Equipment and techniques have been developed to implement a method of photometric calibration of consumer video cameras for imaging of objects that are sufficiently narrow or sufficiently distant to be optically equivalent to point or line sources. Heretofore, it has been difficult to calibrate consumer video cameras, especially in cases of image saturation, because they exhibit nonlinear responses with dynamic ranges much smaller than those of scientific-grade video cameras. The present method not only takes this difficulty in stride but also makes it possible to extend effective dynamic ranges to several powers of ten beyond saturation levels. The method will likely be primarily useful in astronomical photometry. There are also potential commercial applications in medical and industrial imaging of point or line sources in the presence of saturation.This development was prompted by the need to measure brightnesses of debris in amateur video images of the breakup of the Space Shuttle Columbia. The purpose of these measurements is to use the brightness values to estimate relative masses of debris objects. In most of the images, the brightness of the main body of Columbia was found to exceed the dynamic ranges of the cameras. A similar problem arose a few years ago in the analysis of video images of Leonid meteors. The present method is a refined version of the calibration method developed to solve the Leonid calibration problem. In this method, one performs an endto- end calibration of the entire imaging system, including not only the imaging optics and imaging photodetector array but also analog tape recording and playback equipment (if used) and any frame grabber or other analog-to-digital converter (if used). To automatically incorporate the effects of nonlinearity and any other distortions into the calibration, the calibration images are processed in precisely the same manner as are the images of meteors, space-shuttle debris, or other objects that one seeks to analyze. The light source used to generate the calibration images is an artificial variable star comprising a Newtonian collimator illuminated by a light source modulated by a rotating variable neutral- density filter. This source acts as a point source, the brightness of which varies at a known rate. A video camera to be calibrated is aimed at this source. Fixed neutral-density filters are inserted in or removed from the light path as needed to make the video image of the source appear to fluctuate between dark and saturated bright. The resulting video-image data are analyzed by use of custom software that determines the integrated signal in each video frame and determines the system response curve (measured output signal versus input brightness). These determinations constitute the calibration, which is thereafter used in automatic, frame-by-frame processing of the data from the video images to be analyzed.

Suggs, Robert↗