How Many Kilonovae Can Be Found in Past, Present, and Future Survey Data Sets?

dc.contributor.authorScolnic, D.
dc.contributor.authorKessler, R
dc.contributor.authorSoares-Santos, M.
dc.contributor.authorAnnis, James
dc.contributor.authorDiehl, H. Thomas
dc.contributor.authorFrieman, Joshua A.
dc.contributor.authorNugent, Peter
dc.contributor.authorAllam, Sahar
dc.contributor.authorBuckley-Geer, E.
dc.contributor.authorCarrasco Kind, M.
dc.contributor.authorFlaugher, Brenna
dc.contributor.authorGruendl, R A
dc.contributor.authorHartley, W.
dc.contributor.authorHonscheid, K
dc.contributor.authorKuehn, Kyler
dc.contributor.authorLima, M
dc.contributor.authorMaia, M. A. G.
dc.contributor.authorMarshall, J.
dc.contributor.authorScarpine, V
dc.contributor.authorSmith, R. Chris
dc.contributor.authorThomas, R. C.
dc.contributor.authorTucker, D.
dc.contributor.authorMA ller, Anais
dc.date.accessioned2026-01-12T03:29:27Z
dc.date.available2026-01-12T03:29:27Z
dc.date.issued2018
dc.date.updated2023-10-22T07:17:14Z
dc.description.abstractThe discovery of a kilonova (KN) associated with the Advanced LIGO (aLIGO)/Virgo event GW170817 opens up new avenues of multi-messenger astrophysics. Here, using realistic simulations, we provide estimates of the number of KNe that could be found in data from past, present, and future surveys without a gravitational-wave trigger. For the simulation, we construct a spectral time-series model based on the DES-GW multi-band light curve from the single known KN event, and we use an average of BNS rates from past studies of 103Gpc-3 yr-1, consistent with the one event found so far. Examining past and current data sets from transient surveys, the number of KNe we expect to find for ASAS-SN, SDSS, PS1, SNLS, DES, and SMT is between 0 and 0.3. We predict the number of detections per future survey to be 8.3 from ATLAS, 10.6 from ZTF, 5.5/69 from LSST (the Deep Drilling/Wide Fast Deep), and 16.0 from WFIRST. The maximum redshift of KNe discovered for each survey is z=0.8 for WFIRST, z=0.8 for LSST, and z=0.8 for ZTF and ATLAS. This maximum redshift for WFIRST is well beyond the sensitivity of aLIGO and some future GW missions. For the LSST survey, we also provide contamination estimates from Type Ia and core-collapse supernovae: after light curve and template-matching requirements, we estimate a background of just two events. More broadly, we stress that future transient surveys should consider how to optimize their search strategies to improve their detection efficiency and to consider similar analyses for GW follow-up programs.
dc.description.sponsorshipFunding for the DES Projects has been provided by the DOE and NSF(USA), MEC/MICINN/MINECO(Spain), STFC(UK), HEFCE(UK). NCSA(UIUC), KICP(U. Chicago), CCAPP(Ohio State), MIFPA(Texas A&M), CNPQ, FAPERJ, FINEP (Brazil), DFG(Germany) and the Collaborating Institutions in the Dark Energy Survey. The DES Data Management System is supported by the NSF under grant numbers AST-1138766 and AST-1536171. This work was supported in part by the Kavli Institute for Cosmological Physics at the University of Chicago through grant NSF PHY-1125897 and an endowment from the Kavli Foundation and its founder Fred Kavli. We gratefully acknowledge support from NASA grant 14-WPS14-0048. D.S. is supported by NASA through Hubble Fellowship grant HSTHF2-51383.001 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS 5-26555. This analysis was done using the Midway-RCC computing cluster at University of Chicago. The Berger Time-Domain Group at Harvard is supported in part by the NSF through grants AST-1411763 and AST- 1714498, and by NASA through grants NNX15AE50G and NNX16AC22G. The UCSC group is supported in part by NSF grant AST–1518052, the Gordon & Betty Moore Foundation, the Heising-Simons Foundation, generous donations from many individuals through a UCSC Giving Day grant, and from fellowships from the Alfred P. Sloan Foundation and the David and Lucile Packard Foundation to R.J.F. R.B. acknowledges partial support from the Washington Research Foundation Fund for Innovation in Data-Intensive Discovery and the Moore/Sloan Data Science Environments Project at the University of Washington.
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn2041-8205
dc.identifier.urihttps://hdl.handle.net/1885/733804018
dc.language.isoen_AUen_AU
dc.publisherInstitute of Physics Publishing Ltd.
dc.sourceAstrophysical Journal Letters
dc.titleHow Many Kilonovae Can Be Found in Past, Present, and Future Survey Data Sets?
dc.typeJournal article
local.bibliographicCitation.issue1
local.bibliographicCitation.lastpage7
local.bibliographicCitation.startpage1
local.contributor.affiliationScolnic, D., University of Chicago
local.contributor.affiliationKessler, R., University of Chicago
local.contributor.affiliationSoares-Santos, M., Fermi National Accelerator Laboratory
local.contributor.affiliationAnnis, James, Fermi National Accelerator Laboratory
local.contributor.affiliationDiehl, H. Thomas, Fermi National Accelerator Laboratory
local.contributor.affiliationFrieman, Joshua A, Fermi National Accelerator Laboratory
local.contributor.affiliationNugent, Peter, Lawrence Berkeley National Laboratory
local.contributor.affiliationAllam, Sahar, Fermi National Accelerator Laboratory
local.contributor.affiliationBuckley-Geer, E., Fermi National Accelerator Laboratory
local.contributor.affiliationCarrasco Kind, M., University of Illinois
local.contributor.affiliationFlaugher, Brenna, Fermi National Accelerator Laboratory
local.contributor.affiliationGruendl, R. A., University of Illinois
local.contributor.affiliationHartley, W., ETH Zurich
local.contributor.affiliationHonscheid, K., The Ohio State University
local.contributor.affiliationKuehn, Kyler, Australian Astronomical Observatory
local.contributor.affiliationLima, M., Universidade de São Paulo
local.contributor.affiliationMaia, M.A.G, Observatório Nacional
local.contributor.affiliationMarshall, J., Texas A&M University
local.contributor.affiliationScarpine, V., Fermi National Accelerator Laboratory
local.contributor.affiliationSmith, R. Chris, Cerro Tololo Inter-American Observatory
local.contributor.affiliationThomas, R. C., Lawrence Berkeley National Laboratory
local.contributor.affiliationTucker, D., Fermi National Accelerator Laboratory
local.contributor.affiliationMA ller, Anais, College of Science, ANU
local.contributor.authoruidMA ller, Anais, u1018833
local.description.embargo2099-12-31
local.description.notesImported from ARIES
local.identifier.absfor460200 - Artificial intelligence
local.identifier.absfor510100 - Astronomical sciences
local.identifier.absseo280115 - Expanding knowledge in the information and computing sciences
local.identifier.absseo280120 - Expanding knowledge in the physical sciences
local.identifier.ariespublicationa383154xPUB9267
local.identifier.citationvolume852
local.identifier.doi10.3847/2041-8213/aa9d82
local.identifier.scopusID2-s2.0-85040378206
local.identifier.thomsonIDWOS:000418677500003
local.type.statusPublished Version
publicationvolume.volumeNumber852

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