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

¹ Institut für Physik und Astronomie, Universität Potsdam, Haus 28, Karl-Liebknecht-Str. 24/25, 14476 Potsdam, Germany
² Institute for Gravitational and Subatomic Physics (GRASP), Utrecht University, Princetonplein 1, 3584 CC Utrecht, The Netherlands
³ Nikhef, Science Park 105, 1098 XG Amsterdam, The Netherlands
⁴ Department of Physics and Earth Science, University of Ferrara, via Saragat 1, 44122 Ferrara, Italy
⁵ INFN, Sezione di Ferrara, via Saragat 1, 44122 Ferrara, Italy
⁶ INAF, Osservatorio Astronomico d’Abruzzo, via Mentore Maggini snc, 64100 Teramo, Italy
⁷ DTU Space, National Space Institute, Technical University of Denmark, Elektrovej 327/328, 2800 Kongens Lyngby, Denmark
⁸ Max Planck Institute for Gravitational Physics (Albert Einstein Institute), Am Mühlenberg 1, Potsdam 14476, Germany

^★ Corresponding authors: hauke.koehn@uni-potsdam.de; t.r.i.wouters@uu.nl

Received: 28 July 2025
Accepted: 20 October 2025

Abstract

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 FIESTA, a PYTHON 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 AFTERGLOWPY and PYBLASTAFTERGLOW, along with a new surrogate for KN emission based on the POSSIS code. Our surrogates enable evaluation of the light-curve posterior within minutes. We also provide built-in posterior sampling capabilities in FIESTA that rely on the FLOWMC package, which efficiently scale to higher dimensions when adding up to tens of nuisance sampling parameters. Because of its use of the JAX framework, FIESTA 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 PYBLASTAFTERGLOW model for the first time in Bayesian inference.