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A Software Roadmap for Solar System Science with the Large Synoptic Survey Telescope
Gemini Observatory, Northern Operations Center, Hilo, USA.
Planetary Science Institute, Tucson, USA.
Queen's University Belfast, Belfast, UK.
Auburn University, Auburn, USA.
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2019 (English)In: Research Notes of the AAS, E-ISSN 2515-5172, Vol. 3, no 3, article id 51Article in journal (Refereed) Published
Abstract [en]

The 8.4 m Large Synoptic Survey Telescope (LSST) will provide an unprecedented view of the Solar System (Ivezić et al. 2008; LSST Science Collaboration et al. 2009). LSST will detect millions of asteroids and tens of thousands of distant Solar System bodies, within approximately 16 and 24.5 mag (in r-band). Over a ten year period, most of these minor planets will receive hundreds of observations divided between 6 filters (ugrizy). What specifically LSST project will deliver for Solar System detections will soon be updated in the LSST Data Products Definition Document (DPDD; Jurić et al. 2013). A preliminary version of the new LSST Solar System data products schema is available at http://ls.st/ssd and http://ls.st/oug.

The LSST Solar System Science Collaboration (SSSC; http://www.lsstsssc.org) produced a science roadmap (Schwamb et al. 2018) which outlines the collaboration's highest ranked research priorities utilizing LSST. To achieve these science goals, the SSSC has identified crucial software products and tools that will be required but will not be provided by the LSST project. These will have to be developed by the SSSC and the broader planetary community. To spur this effort, we present below this list of LSST community software development tasks.

Place, publisher, year, edition, pages
Institute of Physics (IOP), 2019. Vol. 3, no 3, article id 51
Keywords [en]
comets: general, Kuiper belt: general, methods, data analysis, methods: observational, minor planets, asteroids: general
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Onboard space systems
Identifiers
URN: urn:nbn:se:ltu:diva-73580DOI: 10.3847/2515-5172/ab0e10OAI: oai:DiVA.org:ltu-73580DiVA, id: diva2:1304010
Available from: 2019-04-11 Created: 2019-04-11 Last updated: 2019-04-11Bibliographically approved

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Granvik, Mikael

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