no code implementations • 10 Feb 2021 • Keming Zhang, Joshua S. Bloom, B. Scott Gaudi, Francois Lanusse, Casey Lam, Jessica R. Lu

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: 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.

no code implementations • 8 Oct 2020 • Keming Zhang, Joshua S. Bloom, B. Scott Gaudi, Francois Lanusse, Casey Lam, Jessica Lu

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.

1 code implementation • 12 Dec 2016 • Jolyon K. Bloomfield, Stephen H. P. Face, Alan H. Guth, Saarik Kalia, Casey Lam, Zander Moss

An integral to compute the number density of stationary points at a given field amplitude is constructed.

Mathematical Physics Cosmology and Nongalactic Astrophysics Mathematical Physics

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