Remnant black hole properties from numerical-relativity-informed perturbation theory and implications for waveform modeling

During binary black hole (BBH) mergers, energy and momenta are carried away from the binary system as gravitational radiation. Access to the radiated energy and momenta allows us to predict the properties of the remnant black hole. We develop a Python package gw_remnant to extract the remnant mass, remnant spin, peak luminosity, and the final kick imparted on the remnant black hole from the gravitational waveforms. Using this package, we extract the remnant properties of the final black hole in case of nonspinning BBH mergers with mass ratios ranging from $q=2.5$ to $q=1000$ using waveform modes generated from BHPTNRSur1dq1e4, a recently developed numerical-relativity-informed surrogate model based on the black hole perturbation theory framework. We validate our results against the remnant properties estimated from numerical relativity (NR) surrogate models in the comparable mass-ratio regime and against recently available high-mass-ratio NR simulations at $q=[15,32,64]$. We find that our remnant property estimates computed from fluxes at future null infinity closely match the estimates obtained from the NR surrogate model of apparent horizon data. Using Gaussian process regression fitting methods, we train a surrogate model, BHPTNR_Remnant, for the properties of the remnant black hole arising from BBH mergers with mass ratios from $q=2.5$ to $q=1000$. Finally, we discuss potential improvements in the BHPTNRSur1dq1e4 waveform model when including remnant information. We make both the gw_remnant and BHPTNR_Remnant packages publicly available.