Richard Shaw, Scott Fleming, Karen Levay, Randy Thompson, Anton Koekemoer, Shui-Ay Tseng, Peter Forshay, Jonathan Hargis, Brian McLean, Anthony Marston, Susan Mullally, J.E. Peek, Bernie Shiao, Richard White
KEYWORDS: Data archive systems, Data modeling, Space telescopes, James Webb Space Telescope, Astronomy, Observatories, Visualization, Machine learning, Process engineering
The Mikulski Archive for Space Telescopesb (MAST), a multi-mission archive that hosts science data products for several NASA missions, has since 2003 solicited collections of processed data, termed High-Level Science Products (HLSPs), from investigators with observing and archive science programs. As of early 2018 there were nearly 130 contributed collections, and the growth rate is expected to accelerate with the start of the TESSc and JWSTd missions. While the data volume of all HLSP collections is only about 1% of the total volume hosted by MAST, they have an outsized impact on science. The aggregate downloaded volume for a given HLSP collection is typically about 40 times the collection size, and the citation rates for HLSP collections are significantly higher than that for typical observing programs. Yet hosting HLSPs presents special challenges for long-term archives. It is often problematic to obtain sufficient metadata to specify fully the data products without requiring work from potential contributors that may discourage them from sharing their collections. Historically, preparing an HLSP collection for distribution via MAST has been quite time-consuming and often required substantial interaction with the collection contributors. We are creating a more automated workflow and using new technologies for HLSP collection management to improve collection discoverability, simplify the process for the investigator, ease the burden for MAST staff, and shorten the timeframe for publishing HLSPs. This work will also help MAST staff better assess the impact of HLSP collections on science outcomes for hosted mission data.
A moon or natural satellite is a celestial body that orbits a planetary body such as a planet, dwarf planet, or an asteroid.
Scientists seek understanding the origin and evolution of our solar system by studying moons of these bodies.
Additionally, searches for satellites of planetary bodies can be important to protect the safety of a spacecraft as it
approaches or orbits a planetary body. If a satellite of a celestial body is found, the mass of that body can also be
calculated once its orbit is determined. Ensuring the Dawn spacecraft's safety on its mission to the asteroid (4) Vesta
primarily motivated the work of Dawn's Satellite Working Group (SWG) in summer of 2011. Dawn mission scientists
and engineers utilized various computational tools and techniques for Vesta's satellite search. The objectives of this
paper are to 1) introduce the natural satellite search problem, 2) present the computational challenges, approaches, and
tools used when addressing this problem, and 3) describe applications of various image processing and computational
algorithms for performing satellite searches to the electronic imaging and computer science community. Furthermore,
we hope that this communication would enable Dawn mission scientists to improve their satellite search algorithms and
tools and be better prepared for performing the same investigation in 2015, when the spacecraft is scheduled to approach
and orbit the dwarf planet (1) Ceres.
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