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Automated inference of binary microlensing events with traditionalsampling-based algorithms such as MCMC has been hampered by the slowness of thephysical forward model and the pathological likelihood surface. Currentanalysis of such events requires both expert knowledge and large-scale gridsearches to locate the approximate solution as a prerequisite to MCMC posteriorsampling. As the next generation, space-based microlensing survey with theRoman Space Observatory is expected to yield thousands of binary microlensingevents, a new scalable and automated approach is desired. Here, we present anautomated inference method based on neural density estimation (NDE). We showthat the NDE trained on simulated Roman data not only produces fast, accurate,and precise posteriors but also captures expected posterior degeneracies. Ahybrid NDE-MCMC framework can further be applied to produce the exactposterior.

Automating Inference of Binary Microlensing Events with Neural Density Estimation