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

Abstract

The 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 --10 Gpc yr 3 3 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 0.25 for LSST, and = z 0.04 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.

Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)

“…Using a single SED time-series model to describe GW170817, Scolnic et al (2018a) simulated KN rates in past, present, and future surveys, including LSST. We expect more diversity than this single event, so for PLAsTiCC we included the set of SED time-series models of BNS mergers from Kasen et al (2017).…”

“…A volumetric KN rate of 1×10 −6 yr −1 Mpc −3 is estimated in Scolnic et al (2018a) based on a compilation of rates in Abbott et al (2016). For PLAsTiCC, we increased this rate by a factor of 6 for two reasons: to provide a sufficient training set (∼100), and to reduce the Kaggle score change from correctly identifying each KN.…”

Enabling real-time multi-messenger astrophysics discoveries with deep learning

“…Future analyses may also use photometric classification methods, including DL, for the identification of transients 27,28 . The possibility of serendipitous kilonovae discovery using this approach in wide-field surveys such as LSST have been discussed in REFs 29,30 .…”

Serendipitous discoveries of kilonovae in the LSST main survey: maximizing detections of sub-threshold gravitational wave events

“…We build on the works of Rosswog et al (2017); Scolnic et al (2017) in several ways. We consider the ability of LSST to serendipitously detect kNe but with the eventual aim of using these detections as triggers to search catalogues of GW data to find sub-threshold GW events.…”

“…3.1. Following Scolnic et al (2017), we consider the case where all BNS kNe are represented by the observed spectral energy distribution (SED) evolution of GW170817. We additionally consider a physically-motivated population model of BNS kNe and, for the first time, consider a population model of NSBH kNe.…”