New Simulation-Based Inference Method Accelerates Kilonova Analysis for Next-Generation Observatories
Researchers developed a simulation-based inference (SBI) framework that rapidly analyzes kilonova data—electromagnetic and gravitational wave signatures from neutron star mergers—without relying on traditional Bayesian methods. The method uses a Gaussian process emulator trained on ~1,300 simulations and generates posterior samples in seconds rather than hours, while avoiding systematic biases inherent in standard likelihood approximations. This advancement is critical as new observatories come online and astronomers need faster, more robust analysis tools to characterize neutron star mergers and their nucleosynthesis products.
Astronomers have developed a new computational framework for rapidly analyzing kilonovae—the electromagnetic emissions from neutron star mergers—using simulation-based inference instead of traditional Markov chain Monte Carlo (MCMC) methods. The framework employs a Gaussian process emulator trained on approximately 1,300 POSSIS simulations to perform density-estimation likelihood-free inference, enabling posterior sample generation in seconds. A key advantage is robustness to likelihood misspecification: standard Gaussian likelihood approximations fail to capture the non-Gaussian, correlated structure of emulator uncertainty, causing MCMC analyses to suffer systematic bias and posterior pileup at prior boundaries. The researchers validated their method on the well-studied kilonova AT2017gfo, inferring a total ejecta mass of ~0.087 solar masses dominated by lanthanide-poor material and excluding certain ejecta geometries at high confidence. With next-generation electromagnetic and gravitational wave observatories beginning operations, this rapid and robust analysis method addresses a critical need for timely characterization of neutron star mergers.
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- arXiv astro-phCenter
Rapid and robust simulation-based inference for kilonovae
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