Searching for EM counterparts to GW sources during O4
As a member of the Zwicky Transient Facility (ZTF's) multi-messenger astronomy group, I work on various efforts aimed at discovering and characterizing kilonovae from gravitational-wave (GW) events. During (LIGO-Virgo-KAGRA) LVK's third observing run, we conducted 13 wide-field optical searches for counterparts to GW events, and followed up candidates identified in those searches with large-aperture facilities (i.e. Palomar, Gemini, Keck) but found no kilonovae (Kasliwal, SA et al. 2020). Based on lessons learned from LVK's third observing run, we are developing metrics for which GW events to trigger on, as well as the tools necessary for vetting candidates to distinguish kilonovae from impostors. We are also conducting simulations to determine the optimal triggering strategy (i.e. filters, exposure times, and cadence) for conducting target-of-opportunity searches.
A schematic diagram of the model of r-process nucleosynthesis in a collapsar. This model is parameterized by the total ejecta mass, kinetic energy, Nickel mass, r-process ejecta mass, and mixing fraction. r-process ejecta is embedded in the core of the supernova; depending on the amount of mixing between the inner and outer supernova layers, signatures of r-process nucleosynthesis could be detectable during or after the supernova's photospheric phase (Barnes & Metzger 2022).
Testing whether collapsars synthesize r-process elements
One of the big open questions regarding r-process nucleosynthesis is whether kilonovae are the only heavy element production site, or whether other explosions, such as core-collapse supernovae, can product heavy elements too. Over the past few years, I have been working with Dr. Jennifer Barnes to test the theoretical prediction (Siegel et. al. 2019, Barnes & Metzger 2022) that the supernovae from collapsing massive stars (collapsars) harbor signatures of r-process production. To this end, I have been leading a photometric follow-up campaign of ZTF-discovered broadlined Ic supernovae to search for signatures of a near-infrared excess in their late-time lightcurves and compare their lightcurves with the theoretical models (Anand et al. 2022, in prep.).
2-D posterior probability of kilonova absolute magnitude (x-axis) and evolution rate (y-axis) over realistic ZTF survey limits. Representative points are shown for the characteristic peak magnitude and rise rate (derived from the characteristic timescale) for some categories of transients -- SN Ia (red), Core Collapse SNe-CCSNe (gold), Faint, fast SNIIb (dark-cyan), ILRT/LRNe (dark-orchid) and GW170817 (grey) (Mohite, Rajkumar, SA et al.). This plot illustrates how evolution rate can be used to distinguish between KNe and its various impostors.
Building a photometric kilonova classifier
In deep photometric searches for kilonovae with wide-field surveys, the problem of distinguishing kilonovae from various impostors is notoriously difficult. To aid this process, I am developing a binary classifier to identify kilonovae detected at ZTF's cadence and distinguish them from other common impostors such as shock breakout SNe, GRB afterglows, and CVs, using a simulated dataset. This classifier will be used as a part of a reinforcement learning algorithm that will inform a photometric data augmentation system.
Inferring the properties of kilonovae from observations
Both detections and non-detections of kilonova light curves can be used to infer or constrain their intrinsic parameters -- including their dynamical and wind ejecta masses, lanthanide fractions, and viewing angles. During LVK's third observing run (O3), we used the non-detection limits from ZTF's observations of two neutron star--black hole mergers to constrain the properties of their associated kilonovae, as well as their binary properties (Anand & Coughlin et al. 2021). Currently I am working on an updated inference of the kilonova properties of AT2017gfo using its latest inclination angle measurement from Hubble Space Telescope and Very Long Baseline Interferometry (Anand et al., in prep.).