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Published December 2017 | Published + Submitted
Journal Article Open

Scientific Synergy between LSST and Euclid

Abstract

Euclid and the Large Synoptic Survey Telescope (LSST) are poised to dramatically change the astronomy landscape early in the next decade. The combination of high-cadence, deep, wide-field optical photometry from LSST with high-resolution, wide-field optical photometry, and near-infrared photometry and spectroscopy from Euclid will be powerful for addressing a wide range of astrophysical questions. We explore Euclid/LSST synergy, ignoring the political issues associated with data access to focus on the scientific, technical, and financial benefits of coordination. We focus primarily on dark energy cosmology, but also discuss galaxy evolution, transient objects, solar system science, and galaxy cluster studies. We concentrate on synergies that require coordination in cadence or survey overlap, or would benefit from pixel-level co-processing that is beyond the scope of what is currently planned, rather than scientific programs that could be accomplished only at the catalog level without coordination in data processing or survey strategies. We provide two quantitative examples of scientific synergies: the decrease in photo-z errors (benefiting many science cases) when high-resolution Euclid data are used for LSST photo-z determination, and the resulting increase in weak-lensing signal-to-noise ratio from smaller photo-z errors. We briefly discuss other areas of coordination, including high-performance computing resources and calibration data. Finally, we address concerns about the loss of independence and potential cross-checks between the two missions and the potential consequences of not collaborating.

Additional Information

© 2017 The American Astronomical Society. Received 2017 July 31; revised 2017 October 17; accepted 2017 October 18; published 2017 December 7. J.R. and A.K. were supported by JPL, which is run under a contract for NASA by Caltech. J.R., A.K., P.C., R.B., R.L., and R. Mandelbaum were supported in part by NASA ROSES grant 12-EUCLID12-0004. A.N.T. is supported by a UK Space Agency Euclid grant and a Royal Society Wolfson Research Merit Award. Y.M. was supported by the French CNES Space Agency and Institut National des Sciences de l'Univers of CNRS. T.S. acknowledges travel support from the German Federal Ministry for Economic Affairs and Energy (BMWi) provided via DLR under project No. 50QE1103. T.K. is supported by a Royal Society University Research Fellowship. V.F.C. is funded by the Italian Space Agency (ASI) through contract Euclid—IC (I/031/10/0) and acknowledges financial contribution from the agreement ASI/INAF/I/023/12/0. R. Massey acknowledges support from UK STFC grant ST/N001494/1. B.N. is supported by a UK Space Agency Euclid grant. This paper benefited from ideas developed in the UK Dark Energy Strategy 2020 Document.(37) We thank Michael Brown, Elisa Chisari, Henk Hoekstra, Željko Ivezić Bhuvnesh Jain, Roberto Scaramella, and Peter Schneider for useful feedback on this paper. We thank Katrin Heitmann for useful discussions on high-performance computing. We thank the anonymous referee for useful suggestions on adding quantitative rigor to sections of this paper and useful feedback on the structure of the paper.

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Published - Rhodes_2017_ApJS_233_21.pdf

Submitted - 1710.08489.pdf

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August 19, 2023
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October 17, 2023