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Title: Optimizing Cadences with Realistic Light-curve Filtering for Serendipitous Kilonova Discovery with Vera Rubin Observatory
Abstract

Current and future optical and near-infrared wide-field surveys have the potential to find kilonovae, the optical and infrared counterparts to neutron star mergers, independently of gravitational-wave or high-energy gamma-ray burst triggers. The ability to discover fast and faint transients such as kilonovae largely depends on the area observed, the depth of those observations, the number of revisits per field in a given time frame, and the filters adopted by the survey; it also depends on the ability to perform rapid follow-up observations to confirm the nature of the transients. In this work, we assess kilonova detectability in existing simulations of the Legacy Survey of Space and Time strategy for the Vera C. Rubin Wide Fast Deep survey, with focus on comparing rolling to baseline cadences. Although currently available cadences can enable the detection of >300 kilonovae out to ∼1400 Mpc over the 10 year survey, we can expect only 3–32 kilonovae similar to GW170817 to be recognizable as fast-evolving transients. We also explore the detectability of kilonovae over the plausible parameter space, focusing on viewing angle and ejecta masses. We find that observations in redderizybands are crucial for identification of nearby (within 300 Mpc) kilonovae that could be spectroscopically more » classified more easily than more distant sources. Rubin’s potential for serendipitous kilonova discovery could be increased by gain of efficiency with the employment of individual 30 s exposures (as opposed to 2 × 15 s snap pairs), with the addition of red-band observations coupled with same-night observations ingorrbands, and possibly with further development of a new rolling-cadence strategy.

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Authors:
; ; ; ; ; ; ; ; ; ; ; ; ;
Award ID(s):
1831682
Publication Date:
NSF-PAR ID:
10360862
Journal Name:
The Astrophysical Journal Supplement Series
Volume:
258
Issue:
1
Page Range or eLocation-ID:
Article No. 5
ISSN:
0067-0049
Publisher:
DOI PREFIX: 10.3847
Sponsoring Org:
National Science Foundation
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