Simulation-Based Inference Accelerates Kilonova Analysis

This paper presents a simulation-based inference (SBI) framework for rapid and robust kilonova parameter estimation. The framework addresses the limitations of traditional Bayesian parameter estimation methods, such as Markov chain Monte Carlo (MCMC), which can be time-consuming and rely on explicit likelihood approximations that may break down when modeling uncertainties are significant. The SBI framework utilizes density-estimation likelihood-free inference and a Gaussian process emulator trained on approximately 1300 radiative transfer simulations generated with the POSSIS code. The results demonstrate that SBI provides a rapid alternative to MCMC for inference with emulators or approximate likelihoods, and is robust to emulator uncertainty and likelihood misspecification. The SBI method accurately recovers injected parameters on simulated data and produces posterior predictive light curves consistent with the data, while the MCMC posterior recovery suffers from systematic bias caused by likelihood misspecification. When analyzing AT2017gfo, the SBI and MCMC methods yield similar light-curve predictions but different posterior distributions. Once trained, the SBI framework generates approximately 2x10^4 posterior samples in seconds. This development is crucial for the next generation of electromagnetic and gravitational wave observatories, which will require rapid analysis methods for kilonova data.

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