Real-time Likelihood-free Inference of Roman Binary Microlensing Events with Amortized Neural Posterior Estimation

@article{Zhang2021RealtimeLI,
  title={Real-time Likelihood-free Inference of Roman Binary Microlensing Events with Amortized Neural Posterior Estimation},
  author={Keming 可名 Zhang 张 and Joshua S. Bloom and B. Scott Gaudi and François Lanusse and Casey Y. Lam and Jessica R. Lu},
  journal={The Astronomical Journal},
  year={2021},
  volume={161}
}
Fast and automated inference of binary-lens, single-source (2L1S) microlensing events with sampling-based Bayesian algorithms (e.g., Markov Chain Monte Carlo, MCMC) is challenged on two fronts: the high computational cost of likelihood evaluations with microlensing simulation codes, and a pathological parameter space where the negative-log-likelihood surface can contain a multitude of local minima that are narrow and deep. Analysis of 2L1S events usually involves grid searches over some… 

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