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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 NTRS2012

The HEASARC Swift Gamma-Ray Burst Archive: The Pipeline and the Catalog

Since its launch in late 2004, the Swift satellite triggered or observed an average of one gamma-ray burst (GRB) every 3 days, for a total of 771 GRBs by 2012 January. Here, we report the development of a pipeline that semi automatically performs the data-reduction and data-analysis processes for the three instruments on board Swift (BAT, XRT, UVOT). The pipeline is written in Perl, and it uses only HEAsoft tools and can be used to perform the analysis of a majority of the point-like objects (e.g., GRBs, active galactic nuclei, pulsars) observed by Swift. We run the pipeline on the GRBs, and we present a database containing the screened data, the output products, and the results of our ongoing analysis. Furthermore, we created a catalog summarizing some GRB information, collected either by running the pipeline or from the literature. The Perl script, the database, and the catalog are available for downloading and querying at the HEASARC Web site.

BURST ARCHIVE↗
NASA NTRS2016

Identifying and Characterizing Impact Melt Outcrops in the Nectaris Basin

The Nectaris Basin is an 820-km diameter, multi-ring impact basin located on the near side of the Moon. Nectaris is a defining stratigraphic horizon based on relationships between ejecta units, giving its name to the Nectarian epoch of lunar history. Lunar basin chronology based on higher resolution LRO imagery and topography, while assigning some important basins like Serenitatis to pre-Nectarian time, were generally consistent with those previously derived. Based on this stratigraphy, at least 11 large basins formed in the time between Nectaris and Imbrium. The absolute age of Nectaris, therefore, is a crucial marker in the lunar time-stratigraphic sequence for understanding the impact flux on the Moon, and by extension, the entire inner solar system. For several decades, workers have attempted to constrain the age of the Nectaris basin through radiometric dating of lunar samples. However, there is little agreement on which samples in our collection represent Nectaris, if any, and what the correct radiometric age of such samples is. The importance of the age of Nectaris goes far beyond assigning a stratigraphic marker to lunar chronology. Several dynamical models use Nectaris as their pin date, so that this date becomes crucial in understanding the time-correlated effects in the rest of the solar system. The importance of the Nectaris basin age, coupled with its nearside, mid-latitude location, make remnants of the impact-melt sheet an attractive target for a future mission, either for in-situ dating or for sample return. We have started exploring this possibility. We have begun a consortium data-analysis effort bringing multiple datasets and analysis methods to bear on these putative impact-melt deposits to characterize their extent, elemental composition and mineralogy, maturity and geologic setting, and to identify potential landing sites that meet both operational safety and science requirements.

Nectaris Basin↗
DOE OSTI2022

Kinematic Variables and Feature Engineering for Particle Phenomenology

Kinematic variables have been playing an important role in collider phenomenology, as they expedite discoveries of new particles by separating signal events from unwanted background events and allow for measurements of particle properties such as masses, couplings, spins, etc. For the past 10 years, an enormous number of kinematic variables have been designed and proposed, primarily for the experiments at the Large Hadron Collider, allowing for a drastic reduction of high-dimensional experimental data to lower-dimensional observables, from which one can readily extract underlying features of phase space and develop better-optimized data-analysis strategies. We review these recent developments in the area of phase space kinematics, summarizing the new kinematic variables with important phenomenological implications and physics applications. We also review recently proposed analysis methods and techniques specifically designed to leverage the new kinematic variables. As machine learning is nowadays percolating through many fields of particle physics including collider phenomenology, we discuss the interconnection and mutual complementarity of kinematic variables and machine learning techniques. We finally discuss how the utilization of kinematic variables originally developed for colliders can be extended to other high-energy physics experiments including neutrino experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

A discrete integral transform for rapid spectral synthesis

Accurate synthetic spectra that rely on large Line-By-Line (LBL)-databases are used in a wide range of applications such as high temperature combustion, atmospheric re-entry, planetary surveillance and laboratory plasmas. Conventionally synthetic spectra are calculated by computing a lineshape for every spectral line in the database and adding those together, which may take multiple hours for large databases. In this paper we propose a new approach for spectral synthesis based on an integral transform: the synthetic spectrum is calculated as the integral over the product of a Voigt profile and a newly proposed three-dimensional “lineshape distribution function”, which is a function of spectral position and Gaussian- & Lorentzian width coordinates. A fast discrete version of this transform based on the Fast Fourier Transform (FFT) is proposed, which improves performance compared to the conventional approach by several orders of magnitude while maintaining accuracy. Strategies that minimize the discretization error are discussed. A Python implementation of the method is compared against state-of-the-art spectral code RADIS, and is since adopted as RADIS's default synthesis method. The synthesis of a benchmark CO2 spectrum consisting of 1.8 M spectral lines and 200k spectral points took only 3.1 s using the proposed method (1011 lines × spectral points/s), a factor ~300 improvement over the state-of-the-art, with the relative improvement generally increasing for higher number of lines and/or number of spectral points. Finally, an experimental GPU-implementation of the method was also benchmarked, which demonstrated another 2~3 orders performance increase, achieving up to 5 ∙ 10 14 lines × spectral points/s.

42 ENGINEERING↗