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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Global ocean tides through assimilation of oceanographic and altimeter satellite data in a hydrodynamic model

Ocean tides must be considered in many scientific disciplines: astronomy, oceanography, geodesy, geophysics, meteorology, and space technologies. Progress in each of these disciplines leads to the need for greater knowledge and more precise predictions of the ocean tide contribution. This is particularly true of satellite altimetry. On one side, the present and future satellite altimetry missions provide and will supply new data that will contribute to the improvement of the present ocean tide solutions. On the other side, tidal corrections included in the Geophysical Data Records must be determined with the maximum possible accuracy. The valuable results obtained with satellite altimeter data thus far have not been penalized by the insufficiencies of the present ocean tide predictions included in the geophysical data records (GDR's) because the oceanic processes investigated have shorter wavelengths than the error field of the tidal predictions, so that the residual errors of the tidal corrections are absorbed in the empirical tilt and bias corrections of the satellite orbit. For future applications to large-scale oceanic phenomena, however, it will no longer be possible to ignore these insufficiencies.

Leprovost, Christian↗
DOE OSTI2021

DeepMerge – II. Building robust deep learning algorithms for merging galaxy identification across domains

In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target dataset. Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary. Here we employ domain adaptation techniques— Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)— and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies. Additionally, we explore the use of Fisher loss and entropy minimization to enforce better in-domain class discriminability. We show that the addition of each domain adaptation technique improves the performance of a classifier when compared to conventional deep learning algorithms. We demonstrate this on two examples: between two Illustris-1 simulated datasets of distant merging galaxies, and between Illustris-1 simulated data of nearby merging galaxies and observed data from the Sloan Digital Sky Survey. The use of domain adaptation techniques in our experiments leads to an increase of target domain classification accuracy of up to ~20%. With further development, these techniques will allow astronomers to successfully implement neural network models trained on simulation data to efficiently detect and study astrophysical objects in current and future large-scale astronomical surveys.

galaxies: interactions↗
NASA NTRS2011

Update on Parametric Cost Models for Space Telescopes

Since the June 2010 Astronomy Conference, an independent review of our cost data base discovered some inaccuracies and inconsistencies which can modify our previously reported results. This paper will review changes to the data base, our confidence in those changes and their effect on various parametric cost models

Stahl. H. Philip↗
DOE OSTI2022

Tilted spatially nonflat inflation

Throughout this work, we construct nonlinear inflaton potential-energy densities that describe not necessarily very slowly rolling closed and open inflation models, and compute tilted primordial spatial inhomogeneity power spectra that follow from quantum-mechanical fluctuations during inflation in these models. Earlier nonflat inflation model power spectra computations assumed an inflaton potential-energy density with a linear slope that resulted in very slow roll during inflation and untilted power spectra. These new tilted power spectra differ from those that have previously been used to study cosmological data in nonflat cosmological models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Modeling Stochastic Variability in Multiband Time-series Data

In preparation for the era of time-domain astronomy with upcoming large-scale surveys, we propose a state-space representation of a multivariate damped random walk process as a tool to analyze irregularly-spaced multifilter light curves with heteroscedastic measurement errors. We adopt a computationally efficient and scalable Kalman filtering approach to evaluate the likelihood function, leading to maximum O(k 3 n) complexity, where k is the number of available bands and n is the number of unique observation times across the k bands. This is a significant computational advantage over a commonly used univariate Gaussian process that can stack up all multiband light curves in one vector with maximum O(k 3 n 3 ) complexity. Using such efficient likelihood computation, we provide both maximum likelihood estimates and Bayesian posterior samples of the model parameters. Three numerical illustrations are presented: (i) analyzing simulated five-band light curves for a comparison with independent single-band fits; (ii) analyzing five-band light curves of a quasar obtained from the Sloan Digital Sky Survey Stripe 82 to estimate short-term variability and timescale; (iii) analyzing gravitationally lensed g- and r-band light curves of Q0957+561 to infer the time delay. Two R packages, Rdrw and timedelay, are publicly available to fit the proposed models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

High-count rate effects in event processing for XRISM/ Resolve X-ray microcalorimeter: I. Ground test

The spectroscopic performance of an X-ray microcalorimeter is compromised at high count rates. We utilize the Resolve X-ray microcalorimeter onboard the XRISM satellite to examine the effects observed during high-count rate measurements and propose modeling approaches to mitigate them. We specifically address the following instrumental effects that impact performance: CPU limit, pile-up, and untriggered electrical cross-talk. Experimental data at high count rates were acquired during ground testing using the flight model instrument and a calibration X-ray source. In the experiment, data processing not limited by the performance of the onboard CPU was run in parallel, which cannot be done in orbit. This makes it possible to access the data degradation caused by limited CPU performance. We use these data to develop models that allow for a more accurate estimation of the aforementioned effects. To illustrate the application of these models in observation planning, we present a simulated observation of GX 13+1. Understanding and addressing these issues is crucial to enhancing the reliability and precision of X-ray spectroscopy in situations characterized by elevated count rates.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Practical galaxy morphology tools from deep supervised representation learning

Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful semantic representations of galaxies that are useful for new tasks on which the models were never trained. We exploit these representations to outperform several recent approaches at practical tasks crucial for investigating large galaxy samples. The first task is identifying galaxies of similar morphology to a query galaxy. Given a single galaxy assigned a free text tag by humans (e.g. ‘#diffuse’), we can find galaxies matching that tag for most tags. The second task is identifying the most interesting anomalies to a particular researcher. Our approach is 100 per cent accurate at identifying the most interesting 100 anomalies (as judged by Galaxy Zoo 2 volunteers). The third task is adapting a model to solve a new task using only a small number of newly labelled galaxies. Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch. We solve each task with very few new labels; either one (for the similarity search) or several hundred (for anomaly detection or fine-tuning). This challenges the longstanding view that deep supervised methods require new large labelled data sets for practical use in astronomy. To help the community benefit from our pretrained models, we release our fine-tuning code zoobot. Zoobot is accessible to researchers with no prior experience in deep learning.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Finding my drumbeat: applying lessons learned from Remo Ruffini to understanding astrophysical transients [Slides]

Lessons Learned: Bandwagon science can miss key physics; Bandwagon scientists cling to their bandwagon, oftentimes denying fundamental physics; and, Better approach: Be aware of the physical limitations of a paradigm. What assumptions have been made? How can this be different in Nature?; Be cognizant of alternative models; Encourage the development of those models; and, Although validation is important, fundamental physics forms the basis for Occam’s razor, not whether a toy model fits the data well.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2002

Cosmic-Ray Background Flux Model Baed on a Gamma-Ray Large Area Space Telescope Baloon Flight Engineering

Cosmic-ray background fluxes were modeled based on existing measurements and theories and are presented here. The model, originally developed for the Gamma-ray Large Area Space Telescope (GLAST) Balloon Experiment, covers the entire solid angle (4(pi) sr), the sensitive energy range of the instrument ((approx) 10 MeV to 100 GeV) and abundant components (proton, alpha, e(sup -), e(sup +), (mu)(sup -), (mu)(sup +) and gamma). It is expressed in analytic functions in which modulations due to the solar activity and the Earth geomagnetism are parameterized. Although the model is intended to be used primarily for the GLAST Balloon Experiment, model functions in low-Earth orbit are also presented and can be used for other high energy astrophysical missions. The model has been validated via comparison with the data of the GLAST Balloon Experiment.

COSMIC RADIATION↗
NASA NTRS1992

DSS-13 26-meter antenna upgraded radiometer system

The Deep Space Station (DSS)-13 26-m antenna radiometer system was upgraded with an IBM-compatible computer-controlled configuration with improved supporting hardware and software. Software was generated to analyze results and correct for antenna mispointing, tropospheric loss, and other observing errors. This total power radiometer configuration provides a prototype for the new DSS-13 34-m antenna. The radiometer system is described in terms of the theory, instrumentation hardware, computer configuration, and operational features and performance. The system is used to obtain antenna efficiency and pointing model data and is useful for radio source calibrations required for radio astronomy. Some recent results are given.

Stelzried, C. T.↗
DOE OSTI2023

Second Report of the Nuclear Data Subcommittee of the Nuclear Science Advisory Committee

The central importance of the nuclear data curated by the US Nuclear Data Program (USNDP) for clean energy generation, national security, nonproliferation, medical applications, and space exploration as well as basic science was described in a prior report issued by the DOE/NSF Nuclear Science Advisory Committee subcommittee on Nuclear Data (NSAC-ND) in September 2022. In this report, we present a set of fourteen (14) recommendations that will enhance and advance DOE-NP's stewardship of nuclear data. The first three recommendations focus on the existing core USNDP capabilities, namely: 1) Support the nuclear structure evaluation workforce to improve the currency, consistency, and accessibility of the Evaluated Nuclear Structure Data File (ENSDF); 2) Enhance nuclear reaction evaluation within the USNDP in support of the Evaluated Nuclear Data File (ENDF) through expansion of the workforce and integration of high-performance computing, automation, and machine learning and; 3) Continue atomic mass evaluation in support AME and NUBASE databases. This is followed by eight (8) recommendations representing new cross-cutting initiatives involving both measurement and evaluation to address outstanding nuclear data needs. These new initiatives require a highly trained, diverse workforce that includes personnel with expertise from both inside and outside the nuclear physics community from which evaluators have traditionally been recruited. As such, many of these initiatives are accomplished via a Topical Nuclear Data Collaborations (TNDC). A TNDC is made up of domestic and international stakeholders, subject matter and nuclear data experts, and nuclear data evaluators and features a workforce development plan to ensure that nuclear data evaluators maintain currency in the relevant applications and are seen as equity partners in the endeavor. These include: 1) Establish a coordinated effort to improve evaluation and modeling in nuclear astrophysics for stellar dynamics, multi-messenger astronomy and nucleosynthesis; 2) Initiate a TNDC to develop and maintain nuclear structure evaluation beyond discrete states, including nuclear level densities, photon strength functions and photonuclear data for improved reaction modeling, and exploring nuclear structure at finite temperature; 3) Create a TNDC to perform correlated fission data evaluation, including cross sections, fragment yields, v(A), v(E n ) for nuclear energy, national security, nonproliferation and basic science; 4) From a panel of subject matter experts to establish and annually update a roster of key decay data to nurture its accelerated dissemination including both measurement and evaluation for targeted high-value nuclides for national security, nonproliferation and medical applications; 5) Comprehensive, consistent neutron-induced structure and reaction data for nuclear energy, national security, nonproliferation and planetary nuclear spectroscopy; 6) Charged-particle stopping powers for detector design, space effects and ion beam therapy; 7) High-energy reactions for space exploration and medical nuclide production, and; 8) The creation of an infrastructure for open data and data preservation for use by the entire nuclear physics community. All told, these initiatives require approximately $6.5M increase in NP support of the USNDP in fiscal year 2023 dollars and would require at least 3-5 years to carry out due to the length of time needed to recruit and train new nuclear data researchers. This relatively modest investment would help ensure that the fruits of the nuclear data research carried out by DOE-NP and its collaborators would be brought to bear to address some of the most important needs of our nation and the world. To ensure effective execution of this plan, we present an overview of recruitment, training, and retention goals for the USNDP, the centerpiece of which is a mutually agreed upon code of conduct. Finally, we identify the facility and instrumentation needed to perform the recommended experimental activities. This includes a short review of target fabrication capabilities, reactors, neutron beam, light- and heavy-stable ion, gamma-ray, high-energy and radioactive ion beam facilities. Lastly, a more complete appendix of experimental facilities previously compiled is included with new input provided for 6 facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1983

Magnetohydrodynamic simulation of the coronal transient associated with the solar limb flare of 1980, June 29, 18:21 UT

The spatial and temporal behavior of excess and depleted density regions of the coronal transient accompanying the west limb solar flare of 18:21 UT on June 29, 1980, is modeled mathematically on the basis of SMM-X-ray-polychrometer data, and the results are compared to observations made with the radio spectrograph at Harvard Radio Astronomy Station at Fort Davis and with the NCAR/HAO Mark III K-coronameter at Mauna Loa. Input data for the model include T(max) = about 20 x 10 to the 6th K, n(max) = about 4 x 10 to the 11th/cu cm, and an assumed ejection velocity of 200 km/sec. Computations using an improved 2D nonplane MHD model are carried out for locally open and closed magnetic topologies. The spatially wide, large-amplitude, temporarily steepened MHD wave predicted by the model for both magnetic topologies is shown to agree well with the observations, except that the predicted density enhancement exceeded observed values by at least 50 percent for the closed field and by a factor of 3 for the open field. This discrepancy is seen as an indication that the mass emitting soft X-rays was confined in closed-field regions near the sun during the obervation period.

Wu, S. T.↗
NASA NTRS2000

Atomic Data Needs for X-ray Astronomy

This publication contains written versions of most of the invited talks presented at the workshop on "Atomic Data Needs for X-ray Astronomy," which was held at NASA's Goddard Space Flight Center on December 16-17, 1999. The workshop was divided into five major areas: Observational Spectroscopy, Theoretical Calculations of Atomic Data, Laboratory Measurements of Atomic Parameters, Spectra Modeling, and Atomic Databases. These proceedings are expected to be of interest to producers and users of atomic data. Moreover, the contributions presented here have been written in a way that can be used by a general audience of scientists and graduate students in X-ray astronomy, modelling, and in computational and experimental atomic physics.

Bautista, Manuel A.↗
DOE OSTI2024

Maven: a multimodal foundation model for supernova science

Abstract A common setting in astronomy is the availability of a small number of high-quality observations, and larger amounts of either lower-quality observations or synthetic data from simplified models. Time-domain astrophysics is a canonical example of this imbalance, with the number of supernovae observed photometrically outpacing the number observed spectroscopically by multiple orders of magnitude. At the same time, no data-driven models exist to understand these photometric and spectroscopic observables in a common context. Contrastive learning objectives, which have grown in popularity for aligning distinct data modalities in a shared embedding space, provide a potential solution to extract information from these modalities. We present Maven, the first foundation model for supernova science. To construct Maven, we first pre-train our model to align photometry and spectroscopy from 0.5 M synthetic supernovae using a contrastive objective. We then fine-tune the model on 4702 observed supernovae from the Zwicky transient facility. Maven reaches state-of-the-art performance on both classification and redshift estimation, despite the embeddings not being explicitly optimized for these tasks. Through ablation studies, we show that pre-training with synthetic data improves overall performance. In the upcoming era of the Vera C. Rubin observatory, Maven will serve as a valuable tool for leveraging large, unlabeled and multimodal time-domain datasets.

Zhang, Gemma (ORCID:0000000280198082)↗
NASA NTRS1973

LF radio noise from the earth's magnetosphere

Gyro-synchrotron radio noise emitted by electrons trapped in the earth's magnetosphere has been a subject of extensive research. Previous efforts, which considered frequencies greater than 1 MHz, have shown that this noise should not be detectable in the MF to HF range because its intensity is below the cosmic background noise level. The author has investigated the LF range and has found that appreciable noise is generated at these frequencies. In fact, the theoretical results for this LF noise agree very well with experimental data obtained by a radio astronomy experiment aboard the IMP 6 spacecraft. A comparison showed that the model predicted both variation in the observed noise intensity with Kp and the noise spectral characteristics. Consequently, it is concluded that detectable LF radio noise is emitted, by means of the cyclotron-synchrotron mechanism, by electrons trapped in the earth's magnetosphere, and that this noise is observable only for frequencies below about 300 kHz. For higher frequencies, the theoretical model and the experimental data reconfirm that this noise is below that of cosmic origin.

Frankel, M. S.↗
DOE OSTI2022

Automatic Detection and Classification of Radio Galaxy Images by Deep Learning

Abstract Surveys conducted by radio astronomy observatories, such as SKA, MeerKAT, Very Large Array, and ASKAP, have generated massive astronomical images containing radio galaxies (RGs). This generation of massive RG images has imposed strict requirements on the detection and classification of RGs and makes manual classification and detection increasingly difficult, even impossible. Rapid classification and detection of images of different types of RGs help astronomers make full use of the observed astronomical image data for further processing and analysis. The classification of FRI and FRII is relatively easy, and there are more studies and literature on them at present, but FR0 and FRI are similar, so it is difficult to distinguish them. It poses a greater challenge to image processing. At present, deep learning has made breakthrough progress in the field of image analysis and processing and has preliminary applications in astronomical data processing. Compared with classification algorithms that can only classify galaxies, object detection algorithms that can locate and classify RGs simultaneously are preferred. In target detection algorithms, YOLOv5 has outstanding advantages in the classification and positioning of small targets. Therefore, we propose a deep-learning method based on an improved YOLOv5 object detection model that makes full use of multisource data, combining FIRST radio with SDSS optical image data, and realizes the automatic detection of FR0, FRI, and FRII RGs. The innovation of our work is that on the basis of the original YOLOv5 object detection model, we introduce the SE Net attention mechanism, increase the number of preset anchors, adjust the network structure of the feature pyramid, and modify the network structure, thereby allowing our model to demonstrate galaxy classification and position detection effects. Our improved model produces satisfactory results, as evidenced by experiments. Overall, the mean average precision (mAP@0.5) of our improved model on the test set reaches 89.4%, which can determine the position (R.A. and decl.) and automatically detect and classify FR0s, FRIs, and FRIIs. Our work contributes to astronomy because it allows astronomers to locate FR0, FRI, and FRII galaxies in a relatively short time and can be further combined with other astronomically generated data to study the properties of these galaxies. The target detection model can also help astronomers find FR0s, FRIs, and FRIIs in future surveys and build a large-scale star RG catalog. Moreover, our work is also useful for the detection of other types of galaxies.

Astronomy & Astrophysics↗
NASA NTRS1999

Modeling the unidentified infrared emission with combinations of polycyclic aromatic hydrocarbons

The infrared emission band spectrum associated with many different interstellar objects can be modeled successfully by using combined laboratory spectra of neutral and positively charged polycyclic aromatic hydrocarbons (PAHs). These model spectra, shown here for the first time, alleviate the principal spectroscopic criticisms previously leveled at the PAH hypothesis and demonstrate that mixtures of free molecular PAHs can indeed account for the overall appearance of the widespread interstellar infrared emission spectrum. Furthermore, these models give us insight into the structures, stabilities, abundances, and ionization balance of the interstellar PAH population. These, in turn, reflect conditions in the emission zones and shed light on the microscopic processes involved in the carbon nucleation, growth, and evolution in circumstellar shells and the interstellar medium.

Spectrophotometry, Infrared↗