Automating Inference of Binary Microlensing Events with Neural Density Estimation
This addresses the need for scalable and automated analysis of thousands of binary microlensing events expected from the Roman Space Observatory, offering a domain-specific improvement over existing methods.
The paper tackled the problem of automating inference for binary microlensing events, which is slow and requires expert input with traditional methods, by using neural density estimation; the result showed that this approach produces fast, accurate, and precise posteriors on simulated data for the upcoming Roman Space Observatory survey.
Automated inference of binary microlensing events with traditional sampling-based algorithms such as MCMC has been hampered by the slowness of the physical forward model and the pathological likelihood surface. Current analysis of such events requires both expert knowledge and large-scale grid searches to locate the approximate solution as a prerequisite to MCMC posterior sampling. As the next generation, space-based microlensing survey with the Roman Space Observatory is expected to yield thousands of binary microlensing events, a new scalable and automated approach is desired. Here, we present an automated inference method based on neural density estimation (NDE). We show that the NDE trained on simulated Roman data not only produces fast, accurate, and precise posteriors but also captures expected posterior degeneracies. A hybrid NDE-MCMC framework can further be applied to produce the exact posterior.