作者
Mario Jurić, Jeffrey Kantor, KT Lim, Robert H Lupton, Gregory Dubois-Felsmann, Tim Jenness, Tim S Axelrod, Jovan Aleksić, Roberta A Allsman, Yusra AlSayyad, Jason Alt, Robert Armstrong, Jim Basney, Andrew C Becker, Jacek Becla, Steven J Bickerton, Rahul Biswas, James Bosch, Dominique Boutigny, Matias Carrasco Kind, David R Ciardi, Andrew J Connolly, Scott F Daniel, Gregory E Daues, Frossie Economou, Hsin-Fang Chiang, Angelo Fausti, Merlin Fisher-Levine, D Michael Freemon, Perry Gee, Philippe Gris, Fabio Hernandez, Joshua Hoblitt, Željko Ivezić, Fabrice Jammes, Darko Jevremović, R Lynne Jones, J Bryce Kalmbach, Vishal P Kasliwal, K Simon Krughoff, Dustin Lang, John Lurie, Nate B Lust, Fergal Mullally, Lauren A MacArthur, Peter Melchior, Joachim Moeyens, David L Nidever, Russell Owen, John K Parejko, J Matt Peterson, Donald Petravick, Stephen R Pietrowicz, Paul A Price, David J Reiss, Richard A Shaw, Jonathan Sick, Colin T Slater, Michael A Strauss, Ian S Sullivan, John D Swinbank, Schuyler Van Dyk, Veljko Vujčić, Alexander Withers, Peter Yoachim
发表日期
2015/12/24
期刊
arXiv preprint arXiv:1512.07914
简介
The Large Synoptic Survey Telescope (LSST) is a large-aperture, wide-field, ground-based survey system that will image the sky in six optical bands from 320 to 1050 nm, uniformly covering approximately deg of the sky over 800 times. The LSST is currently under construction on Cerro Pach\'on in Chile, and expected to enter operations in 2022. Once operational, the LSST will explore a wide range of astrophysical questions, from discovering "killer" asteroids to examining the nature of Dark Energy. The LSST will generate on average 15 TB of data per night, and will require a comprehensive Data Management system to reduce the raw data to scientifically useful catalogs and images with minimum human intervention. These reductions will result in a real-time alert stream, and eleven data releases over the 10-year duration of LSST operations. To enable this processing, the LSST project is developing a new, general-purpose, high-performance, scalable, well documented, open source data processing software stack for O/IR surveys. Prototypes of this stack are already capable of processing data from existing cameras (e.g., SDSS, DECam, MegaCam), and form the basis of the Hyper-Suprime Cam (HSC) Survey data reduction pipeline.
学术搜索中的文章
M Jurić, J Kantor, KT Lim, RH Lupton… - arXiv preprint arXiv:1512.07914, 2015