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During binary black hole (BBH) mergers, energy and momenta are carried awayfrom the binary system as gravitational radiation. Access to the radiatedenergy and momenta allows us to predict the properties of the remnant blackhole. We develop a Python package gw_remnant to compute the remnant mass,remnant spin, peak luminosity, and the final kick imparted on the remnant blackhole from the gravitational radiation. Using this package, we compute theremnant properties of the final black hole in case of non-spinning BBH mergerswith mass ratios ranging from q=2.5 to q=1000 using waveform modes generatedfrom BHPTNRSur1dq1e4, a recently developed numerical-relativity-informedsurrogate model based on black-hole perturbation theory framework. We validateour results against the remnant properties estimated from numerical relativity(NR) surrogate models in the comparable mass ratio regime and against recentlyavailable high-mass-ratio NR simulations at q=[15,32,64]. We find that ourremnant property estimates computed from fluxes at future null infinity closelymatch the estimates obtained from the NR surrogate model of apparent horizondata. Using Gaussian process regression fitting methods, we train a surrogatemodel, {BHPTNR_Remnant, for the properties of the remnant black hole arisingfrom BBH mergers with mass ratios from q=2.5 to q=1000. Finally, we discusspotential improvements in the BHPTNRSur1dq1e4 waveform model when includingremnant information. We make both the gw_remnant and BHPTNR_Remnant packagespublicly available.

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