Targeted Likelihood-Free Inference of Dark Matter Substructure in Strongly-Lensed Galaxies

Abstract

The analysis of optical images of galaxy-galaxy strong gravitational lensing systems can provide important information about the distribution of dark matter at small scales. However, the modeling and statistical analysis of these images is extraordinarily complex, bringing together source image and main lens reconstruction, hyper-parameter optimization, and the marginalization over small-scale structure realizations. We present here a new analysis pipeline that tackles these diverse challenges by bringing together many recent machine learning developments in one coherent approach, including variational inference, Gaussian processes, differentiable probabilistic programming, and neural likelihood-to-evidence ratio estimation. Our pipeline enables: (a) fast reconstruction of the source image and lens mass distribution, (b) variational estimation of uncertainties, (c) efficient optimization of source regularization and other hyperparameters, and (d) marginalization over stochastic model components like the distribution of substructure. We present here preliminary results that demonstrate the validity of our approach.

arXiv commentAccepted for the NeurIPS 2020 workshop Machine Learning and the Physical Sciences; 9 pages, 4 figures; minor edits for clarity since v1

References in corpus (15)

• Particle Dark Matter: Evidence, Candidates and Constraints
• Warm Dark Matter as a solution to the small scale crisis: new constraints from high redshift Lyman-alpha forest data
• Gravitational detection of a low-mass dark satellite at cosmological distance
• Detection of a Dark Substructure through Gravitational Imaging
• Bayesian Strong Gravitational-Lens Modeling on Adaptive Grids: Objective Detection of Mass Substructure in Galaxies
• Nonlinear Evolution of Cosmological Structures in Warm Dark Matter Models
• Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks
• Modelling the line-of-sight contribution in substructure lensing
• Kernel Operations on the GPU, with Autodiff, without Memory Overflows
• Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
• Data-Driven Reconstruction of Gravitationally Lensed Galaxies using Recurrent Inference Machines
• Direct Detection of Dark Matter Substructure in Strong Lens Images with Convolutional Neural Networks
• Differentiable Strong Lensing: Uniting Gravity and Neural Nets through Differentiable Probabilistic Programming
• Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation
• Likelihood-free MCMC with Amortized Approximate Ratio Estimators

Cited by in corpus (7)

• Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation
• Sensitivity of strong lensing observations to dark matter substructure: a case study with Euclid
• Scalable inference with Autoregressive Neural Ratio Estimation
• Machine Learning and Cosmology
• Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time
• BitHEP -- The Limits of Low-Precision ML in HEP
• Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling