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

  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},
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… 

MAGIC: Microlensing Analysis Guided by Intelligent Computation

This work presents MAGIC, which is a machine-learning framework to efficiently and accurately infer the microlensing parameters of binary events with realistic data quality and is able to locate degenerate solutions even when large data gaps are introduced.

A Ubiquitous Unifying Degeneracy in 2-body Microlensing Systems

The offset degeneracy is presented, which unifies the previously known close–wide and inner–outer degeneracies, generalises to resonant caustics, and upon reanalysis, is ubiquitous in previously published planetary events with 2-fold degenerate solutions.

Monte Carlo Techniques for Addressing Large Errors and Missing Data in Simulation-based Inference

Upcoming astronomical surveys will observe billions of galaxies across cosmic time, providing a unique opportunity to map the many pathways of galaxy assembly to an incredibly high resolution.

Fast and robust Bayesian Inference using Gaussian Processes with GPry

The GPry algorithm for fast Bayesian inference of general (non-Gaussian) posteriors with a moderate number of parameters is presented, based on generating a Gaussian Process surrogate model of the log-posterior aided by a Support Vector Machine classifier that excludes extreme or non-flnite values.

${\rm S{\scriptsize IM}BIG}$: A Forward Modeling Approach To Analyzing Galaxy Clustering

We present the first-ever cosmological constraints from a simulation-based inference (SBI) analysis of galaxy clustering from the new Sim- BIG forward modeling framework. SimBIG leverages the

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State-of-the-art spectral energy distribution (SED) analyses use Bayesian inference to derive physical properties of galaxies from observed photometry or spectra. They require sampling from a

DIGS: deep inference of galaxy spectra with neural posterior estimation

With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The

AI providing new light on lensing degeneracies

Accelerated Bayesian SED Modeling Using Amortized Neural Posterior Estimation

This work presents SEDflow, an ANPE method for producing the posteriors of the recent Hahn et al.



Modeling microlensing events with MulensModel

Predictions of the WFIRST Microlensing Survey. I. Bound Planet Detection Rates

The Wide Field InfraRed Survey Telescope (WFIRST) is the next NASA astrophysics flagship mission, to follow the James Webb Space Telescope. The WFIRST mission was chosen as the top-priority large


-As an alternative to their description as vibrational levels, the low excited states of even-even nuclei can be described as rotational states of a helion, dineutron, diproton, or other cluster

of the Association for Computational Linguistics:

MulensModel: Microlensing light curves modeling

  • 2019

arXiv:1608.03983 [cs, math], arXiv: 1608.03983

  • 2017

SciPy: Open Source Scientific Tools for Python

OGLE-2018-BLG-0677Lb: A Super-Earth Near the Galactic Bulge

We report the analysis of the microlensing event OGLE-2018-BLG-0677. A small feature in the light curve of the event leads to the discovery that the lens is a star–planet system. Although there are

The frontier of simulation-based inference

The rapidly developing field of simulation-based inference is reviewed and the forces giving additional momentum to the field are identified to describe how the frontier is expanding.

A machine learning classifier for microlensing in wide-field surveys