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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 OSTI2023

MESA-Web: A cloud resource for stellar evolution in astronomy curriculum

Here, we present MESA-Web,a cloud resource with an online interface to the Modules for Experiments in Stellar Astrophysics (MESA) software instrument. MESA-Web allows learners to evolve stellar models without the need to download and install MESA. Since being released in 2015, MESA-Web has delivered over 17,000 calculations to over 2,200 unique learners and currently performs about 11 jobs per day. MESA-Web can be used as an educational tool for stars in the classroom or for scientific investigations. We report on new capabilities of MESA-Web introduced since its 2015 release including learner-supplied nuclear reaction rates, custom stopping conditions, and an expanded selection of input parameters. To foster collaboration we have created a Zenodo MESA-Web community hub, where instructors can openly share examples of using MESA-Web in the classroom. We discuss two examples in the current community hub. The first example is a lesson module on Red Giant Branch stars that includes a suite of exercises designed to fit a range of learners and a Jupyter workbook for additional analysis. The second example is lesson materials for an upper-level Astronomy majors course in Stars and Radiation that includes an assignment verifying some of the expected trends that are presented in a popular stellar physics textbook.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

DUNE Data Management: Network Visualization Monitoring Software

Fermilab’s fagship Deep Underground Neutrino Experiment (DUNE) seeks to better understand the nature of neutrinos within the context of Leptogenesis, neutrino oscillations, multi-messenger Astronomy, and other scientifc phenomena. The experiment will send a beam of neutrinos from the Fermilab site in Illinois to the Sanford Underground Neutrino Facility (SURF) in South Dakota, generating petabytes of scientifc data. Given the high volume of data expected when measurements begin at the end of the decade, DUNE computing and the data management group must carefully monitor data transfers across the 15 remote storage sites and, more generally, the 36 global DUNE computing sites. This report will describe both the frontend and backend data monitoring software designed to analyze and visualize these data transfers. Specifc emphasis will be placed on the software’s setup, usage, and methods for future implementations. The full software code can be found under the DUNE/data-mgmt-testing GitHub repository.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

CLMM : a LSST-DESC cluster weak lensing mass modeling library for cosmology

ABSTRACT We present the v1.0 release of CLMM, an open source python library for the estimation of the weak lensing masses of clusters of galaxies. CLMM is designed as a stand-alone toolkit of building blocks to enable end-to-end analysis pipeline validation for upcoming cluster cosmology analyses such as the ones that will be performed by the Vera C. Rubin Legacy Survey of Space and Time-Dark Energy Science Collaboration (LSST-DESC). Its purpose is to serve as a flexible, easy-to-install, and easy-to-use interface for both weak lensing simulators and observers and can be applied to real and mock data to study the systematics affecting weak lensing mass reconstruction. At the core of CLMM are routines to model the weak lensing shear signal given the underlying mass distribution of galaxy clusters and a set of data operations to prepare the corresponding data vectors. The theoretical predictions rely on existing software, used as backends in the code, that have been thoroughly tested and cross-checked. Combined theoretical predictions and data can be used to constrain the mass distribution of galaxy clusters as demonstrated in a suite of example Jupyter Notebooks shipped with the software and also available in the extensive online documentation.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1973

Research at the Stanford Center for Radar Astronomy

Theoretical and experimental radio and radar studies are presented concerning lunar and planetary atmospheres and surfaces; the sun and interplanetary medium; and software and hardware conceived while doing research. Emphasis is given to probe and radio accumulation measurements of planetary atmospheres. A list is included of recent publications, technical and scientific reports, and symposia with papers.

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NASA NTRS1992

XRONOS: A timing analysis software package. User's guide, version 3.00

XRONOS is a general purpose timing analysis package. Although it was designed mainly for x-ray astronomy, XRONOS is basically detector and wavelength independent. It has been used to analyze data from the Einstein Observatory, Exosat and Ginga, as well as optical photometry and helioseismology data. It includes programs for: light curve(s), hardness ratio and color-color plotting, epoch folding, power spectrum, autocorrelation, cross correlation, time skewness, and statistical analysis. An overview of XRONOS and walkthrough of a XRONOS session are presented. The QDP/PLT plotting and function fitting program used by XRONOS is outlined and the algorithm and features of XRONOS are detailed.

Stella, L.↗
DOE OSTI2021

Scaling pair count to next galaxy surveys

ABSTRACT Counting pairs of galaxies or stars according to their distance is at the core of real-space correlation analyses performed in astrophysics and cosmology. Upcoming galaxy surveys (LSST, Euclid) will measure properties of billions of galaxies challenging our ability to perform such counting in a minute-scale time relevant for the usage of simulations. The problem is only limited by efficient access to the data, hence belongs to the big data category. We use the popular Apache Spark framework to address it and design an efficient high-throughput algorithm to deal with hundreds of millions to billions of input data. To optimize it, we revisit the question of non-hierarchical sphere pixelization based on cube symmetries and develop a new one dubbed the ‘Similar Radius Sphere Pixelization’ (SARSPix) with very close to square pixels. It provides the most adapted indexing over the sphere for all distance-related computations. Using LSST-like fast simulations, we compute autocorrelation functions on tomographic bins containing between a hundred million to one billion data points. In each case, we achieve the construction of a standard pair-distance histogram in about 2 min, using a simple algorithm that is shown to scale, over a moderate number of nodes (16–64). This illustrates the potential of this new techniques in the field of astronomy where data access is becoming the main bottleneck. They can be easily adapted to other use-cases as nearest-neighbours search, catalogue cross-match or cluster finding. The software is publicly available from https://github.com/astrolabsoftware/SparkCorr.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2000

Future Directions for Astronomical Image Display

In the "Future Directions for Astronomical Image Displav" project, the Smithsonian Astrophysical Observatory (SAO) and the National Optical Astronomy Observatories (NOAO) evolved our existing image display program into fully extensible. cross-platform image display software. We also devised messaging software to support integration of image display into astronomical analysis systems. Finally, we migrated our software from reliance on Unix and the X Window System to a platform-independent architecture that utilizes the cross-platform Tcl/Tk technology.

Mandel, Eric↗
DOE OSTI2022

Photometric identification of compact galaxies, stars, and quasars using multiple neural networks

We present MargNet, a deep learning-based classifier for identifying stars, quasars, and compact galaxies using photometric parameters and images from the Sloan Digital Sky Survey Data Release 16 catalogue. MargNet consists of a combination of convolutional neural network and artificial neural network architectures. Using a carefully curated data set consisting of 240 000 compact objects and an additional 150 000 faint objects, the machine learns classification directly from the data, minimizing the need for human intervention. MargNet is the first classifier focusing exclusively on compact galaxies and performs better than other methods to classify compact galaxies from stars and quasars, even at fainter magnitudes. This model and feature engineering in such deep learning architectures will provide greater success in identifying objects in the ongoing and upcoming surveys, such as Dark Energy Survey and images from the Vera C. Rubin Observatory.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Periodicity significance testing with null-signal templates: reassessment of PTF’s SMBH binary candidates

Periodograms are widely employed for identifying periodicity in time series data, yet they often struggle to accurately quantify the statistical significance of detected periodic signals when the data complexity precludes reliable simulations. We develop a data-driven approach to address this challenge by introducing a null-signal template (NST). The NST is created by carefully randomizing the period of each cycle in the periodogram template, rendering it non-periodic. It has the same frequentist properties as a periodic signal template, and we show with simulations that the distribution of false positives is the same as with the original periodic template, regardless of the underlying data. Thus, performing a periodicity search with the NST acts as an effective simulation of the null (no-signal) hypothesis, without having to simulate the noise properties of the data. We apply the NST method to the supermassive black hole binaries (SMBHB) search in the Palomar Transient Factory (PTF), where Charisi et al. had previously proposed 33 high signal-to-noise candidates utilizing simulations to quantify their significance. Our approach reveals that these simulations do not capture the complexity of the real data. There are no statistically significant periodic signal detections above the non-periodic background. To improve the search sensitivity, we introduce a Gaussian quadrature based algorithm for the Bayes Factor with correlated noise as a test statistic. We show with simulations that this improves sensitivity to true signals by more than an order of magnitude. However, the Bayes Factor approach also results in no statistically significant detections in the PTF data.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2004

Standardized Sky Partitioning for the Next Generation Astronomy and Space Science Archives

The Johns Hopkins University and Space Telescope Science Institute are working together on this project to develop a library of standard software for data archives that will benefit the wider astronomical community. The ultimate goal was to develop and distribute a software library aimed at providing a common system for partitioning and indexing the sky in manageable sized regions and provide complex queries on the objects stored in this system. Whilst ongoing maintenance work will continue the primary goal has been completed. Most of the next generation sky surveys in the different wavelengths like 2MASS, GALEX, SDSS, GSC-II, DPOSS and FIRST have agreed on this common set of utilities. In this final report, we summarize work on the work elements assigned to the STScI project team.

Lal, Nand↗
DOE OSTI2021

Tidal disruption discs formed and fed by stream–stream and stream–disc interactions in global GRHD simulations

When a star passes close to a supermassive black hole (BH), the BH’s tidal forces rip it apart into a thin stream, leading to a tidal disruption event (TDE). In this work, we study the post-disruption phase of TDEs in general relativistic hydrodynamics (GRHD) using our GPU-accelerated code h-amr. We carry out the first grid-based simulation of a deep-penetration TDE (β = 7) with realistic system parameters: a black hole-to-star mass ratio of 10 6 , a parabolic stellar trajectory, and a non-zero BH spin. We also carry out a simulation of a tilted TDE whose stellar orbit is inclined relative to the BH midplane. We show that for our aligned TDE, an accretion disc forms due to the dissipation of orbital energy with ~20 percent of the infalling material reaching the BH. The dissipation is initially dominated by violent self-intersections and later by stream–disc interactions near the pericentre. The self-intersections completely disrupt the incoming stream, resulting in five distinct self-intersection events separated by approximately 12 h and a flaring in the accretion rate. We also find that the disc is eccentric with mean eccentricity e ≈ 0.88. For our tilted TDE, we find only partial self-intersections due to nodal precession near pericentre. Although these partial intersections eject gas out of the orbital plane, an accretion disc still forms with a similar accreted fraction of the material to the aligned case. These results have important implications for disc formation in realistic tidal disruptions. For instance, the periodicity in accretion rate induced by the complete stream disruption may explain the flaring events from Swift J1644+57.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2004

Nanotechnology Infrared Optics for Astronomy Missions

We have used the "MicroStripes" code (Flomerics, Inc.) to perform full-, near- and far-field diffraction modeling of metal mesh performance on substrates. Our Miles Code software, which approximates the full calculation in a quick, gui-based window, is useful as an iterative device by adjusting the input parameters (index of refraction, thickness, etc.) to provide agreement with the full calculation. However, despite the somewhat extravagant claims by the MicroStripes manufacturer, this code is also not perfect because numerous free parameters must be set. Key among these, as identified in our earlier papers and proposal documents, is the high frequency (i.e., far IR) character of the real and imaginary parts of the index of refraction of the metal mesh, the high frequency character of the real and imaginary parts of the index of refraction of the substrate, and the character of the interface between the mesh and the substrate material, and in particular the suppression (or possible enhancement) of surface effects at the interface.

Frogel, Jay↗
NASA NTRS2005

Generating Mosaics of Astronomical Images

"Montage" is the name of a service of the National Virtual Observatory (NVO), and of software being developed to implement the service via the World Wide Web. Montage generates science-grade custom mosaics of astronomical images on demand from input files that comply with the Flexible Image Transport System (FITS) standard and contain image data registered on projections that comply with the World Coordinate System (WCS) standards. "Science-grade" in this context signifies that terrestrial and instrumental features are removed from images in a way that can be described quantitatively. "Custom" refers to user-specified parameters of projection, coordinates, size, rotation, and spatial sampling. The greatest value of Montage is expected to lie in its ability to analyze images at multiple wavelengths, delivering them on a common projection, coordinate system, and spatial sampling, and thereby enabling further analysis as though they were part of a single, multi-wavelength image. Montage will be deployed as a computation-intensive service through existing astronomy portals and other Web sites. It will be integrated into the emerging NVO architecture and will be executed on the TeraGrid. The Montage software will also be portable and publicly available.

Bergou, Attila↗
DOE OSTI2021

Photometric redshifts for the S-PLUS Survey: Is machine learning up to the task?

The Southern Photometric Local Universe Survey (S-PLUS) is a novel project that aims to map the Southern Hemisphere using a twelve filter system, comprising five broad-band SDSS-like filters and seven narrow-band filters optimized for important stellar features in the local universe. In this paper we use the photometry and morphological information from the first S-PLUS data release (S-PLUS DR1) cross-matched to unWISE data and spectroscopic redshifts from Sloan Digital Sky Survey DR15. We explore three different machine learning methods (Gaussian Processes with GPz and two Deep Learning models made with TensorFlow) and compare them with the currently used template-fitting method in the S-PLUS DR1 to address whether machine learning methods can take advantage of the twelve filter system for photometric redshift prediction. Using tests for accuracy for both single-point estimates such as the calculation of the scatter, bias, and outlier fraction, and probability distribution functions (PDFs) such as the Probability Integral Transform (PIT), the Continuous Ranked Probability Score (CRPS) and the Odds distribution, we conclude that a deep-learning method using a combination of a Bayesian Neural Network and a Mixture Density Network offers the most accurate photometric redshifts for the current test sample. In conclusion, it achieves single-point photometric redshifts with scatter (σ NMAD ) of 0.023, normalized bias of -0.001, and outlier fraction of 0.64% for galaxies with r_auto magnitudes between 16 and 21.

79 ASTRONOMY AND ASTROPHYSICS↗
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 OSTI2024

guppy i : a code for reducing the storage requirements of cosmological simulations

ABSTRACT As cosmological simulations have grown in size, the permanent storage requirements of their particle data have also grown. Even modest simulations present a major logistical challenge for the groups which run these boxes and researchers without access to high performance computing facilities often need to restrict their analysis to lower quality data. In this paper, we present guppy, a compression algorithm and code base tailored to reduce the sizes of dark matter-only cosmological simulations by approximately an order of magnitude. guppy is a ‘lossy’ algorithm, meaning that it injects a small amount of controlled and uncorrelated noise into particle properties. We perform extensive tests on the impact that this noise has on the internal structure of dark matter haloes, and identify conservative accuracy limits which ensure that compression has no practical impact on single-snapshot halo properties, profiles, and abundances. We also release functional prototype libraries in C, Python, and Go for reading and creating guppy data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Comparing Early Evolution of Flames in X-Ray Bursts in Two and Three Dimensions

We explore the early evolution of flame ignition and spreading on the surface of a neutron star in three dimensions, in the context of X-ray bursts. We look at the nucleosynthesis and morphology of the burning front and compare to two-dimensional axisymmetric simulations to gauge how important a full three-dimensional treatment of the flame is for the early dynamics. Finally, we discuss the progress toward full-star resolved flame simulations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1985

Research and technology: Fiscal year 1984 report

Topics covered include extraterrestrial physics, high energy astrophysics, astronomy, solar physics, atmospheres, oceans, terrestrial physics, space technology, sensors, techniques, user space data systems, space communications and navigation, and system and software engineering.

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