Efficient Bayesian analysis of kilonovae and gamma ray burst afterglows with FIESTA

Gamma-ray burst (GRB) afterglows and kilonovae (KNe) are electromagnetic transients that can accompany binary neutron star (BNS) mergers. Therefore, studying their emission processes is of general interest for constraining cosmological parameters or the behavior of ultra-dense matter. One common method to analyze electromagnetic data from BNS mergers is to sample a Bayesian posterior over the parameters of a physical model for the transient. However, sampling the posterior is computationally costly and because of the many likelihood evaluations required in this process, detailed models are too expensive to be used directly in Bayesian inference. In this paper, we address the problem by introducing <ns0:sc>FIESTA</ns0:sc>, a <ns0:sc>PYTHON</ns0:sc> package to train machine learning (ML) surrogates for GRB afterglow and kilonova models that have the capacity to accelerate likelihood evaluations. Specifically, we introduce extensive ML surrogates for the state-of-the-art GRB afterglow models <ns0:sc>AFTERGLOWPY</ns0:sc> and <ns0:sc>PYBLASTAFTERGLOW</ns0:sc>, along with a new surrogate for KN emission based on the <ns0:sc>POSSIS</ns0:sc> code. Our surrogates enable evaluation of the light-curve posterior within minutes. We also provide built-in posterior sampling capabilities in <ns0:sc>FIESTA</ns0:sc> that rely on the <ns0:sc>FLOWMC</ns0:sc> package, which efficiently scale to higher dimensions when adding up to tens of nuisance sampling parameters. Because of its use of the <ns0:sc>JAX</ns0:sc> framework, <ns0:sc>FIESTA</ns0:sc> also allows for GPU acceleration during both surrogate training and posterior sampling. We applied our framework to reanalyze AT2017gfo/GRB170817A and GRB211211A with our surrogates, thus employing the new <ns0:sc>PYBLASTAFTERGLOW</ns0:sc> model for the first time in Bayesian inference.