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Published 2013 | public
Book Section - Chapter

Sky Surveys


Sky surveys represent a fundamental data basis for astronomy. We use them to map in a systematic way the universe and its constituents and to discover new types of objects or phenomena. We review the subject, with an emphasis on the wide-field, imaging surveys, placing them in a broader scientific and historical context. Surveys are now the largest data generators in astronomy, propelled by the advances in information and computation technology, and have transformed the ways in which astronomy is done. This trend is bound to continue, especially with the new generation of synoptic sky surveys that cover wide areas of the sky repeatedly and open a new time domain of discovery. We describe the variety and the general properties of surveys, illustrated by a number of examples, the ways in which they may be quantified and compared, and offer some figures of merit that can be used to compare their scientific discovery potential. Surveys enable a very wide range of science, and that is perhaps their key unifying characteristic. As new domains of the observable parameter space open up thanks to the advances in technology, surveys are often the initial step in their exploration. Some science can be done with the survey data alone (or a combination of data from different surveys), and some require a targeted follow-up of potentially interesting sources selected from surveys. Surveys can be used to generate large, statistical samples of objects that can be studied as populations or as tracers of larger structures to which they belong. They can be also used to discover or generate samples of rare or unusual objects and may lead to discoveries of some previously unknown types. We discuss a general framework of parameter spaces that can be used for an assessment and comparison of different surveys and the strategies for their scientific exploration. As we are moving into the Petascale regime and beyond, an effective processing and scientific exploitation of such large data sets and data streams pose many challenges, some of which are specific to any given survey and some of which may be addressed in the framework of Virtual Observatory and Astroinformatics. The exponential growth of data volumes and complexity makes a broader application of data mining and knowledge discovery technologies critical in order to take a full advantage of this wealth of information. Finally, we discuss some outstanding challenges and prospects for the future.

Additional Information

© 2013 Springer Science+Business Media Dordrecht. We are indebted to many colleagues and collaborators over the years, especially the key members of the survey teams: Nick Weir, Usama Fayyad, Joe Roden, Reinaldo de Carvalho, Steve Odewahn, Roy Gal, Robert Brunner, and Julia Kennefick in the case of DPOSS; Eilat Glikman, Roy Williams, Charlie Baltay, David Rabinowitz, and the rest of the Yale team in the case of PQ; and Steve Larson, Ed Beshore, and the rest of the Arizona and Australia team in the case of CRTS. Likewise, we acknowledge numerous additional colleagues and collaborators in the Virtual Observatory and the Astroinformatics community, especially Alex Szalay, Jim Gray, Giuseppe Longo, Yan Xu, Tom Prince, Mark Stalzer, and many others. Several tens of excellent undergraduate research students at Caltech contributed to our work through the years, many of them supported by the Caltech's SURF program. And last, but not least, the staff of Palomar, Keck, and other observatories who helped the data flow. Our work on sky surveys and their exploration has been supported in part by the NSF grants AST-0122449, AST-0326524, AST-0407448, CNS-0540369, AST-0834235, AST-0909182, and IIS-1118041; the NASA grant 08-AISR08-0085; and by the Ajax and Fishbein Family Foundations. Some of the figures in this chapter have been produced using an immersive VR visualization software, supported in part by the NSF grant HCC-0917814. We thank numerous colleagues, and in particular H. Bond, G. Longo, M. Strauss, and M. Kurtz, whose critical reading improved the text. Finally, we thank The Editors for their saintly patience while waiting for the completion of this chapter.

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August 19, 2023
October 23, 2023