KilonovaRank: gravitational wave counterparts and exotic transients with next-generation surveys

Time-domain astronomy will soon be transformed by powerful instrumentation: the Large Synoptic Survey Telescope (LSST) and upgraded gravitational wave detectors.

We will finally be able to build large samples of rare and multi-messenger transients, allowing new scientific breakthroughs.

But to do so, we must overcome the substantial difficulty of identifying the important events among an expected sea of contaminants.

I will solve this problem by developing novel image classification techniques before LSST begins, and then use this with LSST data to answer some of the most pressing questions about stellar evolution, nucleosynthesis, and high energy physics.

I will discover hundreds of superluminous supernovae (SLSNe), key to unknown physics in massive stars, and tidal disruption events (TDEs) of stars around supermassive black holes, probing black hole accretion in usually inaccessible regimes.

By folding in the sky maps from GW detections of neutron star mergers, I will rapidly find kilonova counterparts in LSST follow-up searches.

With 10-100 kilonovae (compared to 1 well-studied event now), we will understand the nucleosynthesis of all heavy (r-process) elements, determine the equation of state for nuclear matter, and pin down what these mergers leave behind.

Moreover, I will determine the progenitor stars and power source of SLSNe, and the emission mechanisms in TDEs and relation to black hole mass, using even larger samples of those events. We may even find whole new transient classes.

This project could not be more timely as the upgrades in GW detectors, the start of LSST, and the brief era of JWST will overlap in just a few years (and within the time of an ERC-funded project). Everything is in place for success, from guaranteed data access and follow-up resources, to public/prototype codes.

The legacy datasets for rare transient classes will dwarf those from pre-LSST, and our inference framework will lead to an unprecedented understanding of these populations.