Direct Detection of Dark Matter Substructure in Strong Lens Images with Convolutional Neural Networks

Abstract

Strong gravitational lensing is a promising way of uncovering the nature of dark matter, by finding perturbations to images that cannot be well accounted for by modeling the lens galaxy without additional structure, be it subhalos (smaller halos within the smooth lens) or line-of-sight (LOS) halos. We present results attempting to infer the presence of substructure from images without requiring an intermediate step in which a smooth model has to be subtracted, using a simple convolutional neural network (CNN). We find that the network is only able to infer the presence of subhalos with $>75\%$ accuracy when they have masses of $\ge 5\times 10^9$M${}_{\odot }$ if they lie within the main lens galaxy. Since less massive foreground LOS halos can have the same effect as higher mass subhalos, the CNN can probe lower masses in the halo mass function. The accuracy does not improve significantly if we add a population of less massive subhalos. With the expectation of experiments such as HST and Euclid yielding thousands of high-quality strong lensing images in the next years, having a way of analyzing images quickly to identify candidates that merit further analysis to determine individual subhalo properties while preventing extensive resources being used for images that would yield null detections could be very useful. By understanding the sensitivity as a function of substructure mass, non-detections could be combined with the information from images with substructure to constrain the cold dark matter scenario, in particular if the sensitivity can be pushed to lower masses.

12 pages+appendix, 7 figures, v2: incorporates changes in the accuracy of the network due to additional modelling steps, v3: matches published version

References in corpus (8)

• Too big to fail? The puzzling darkness of massive Milky Way subhaloes
• Bayesian Strong Gravitational-Lens Modeling on Adaptive Grids: Objective Detection of Mass Substructure in Galaxies
• Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks
• Inference of the Cold Dark Matter substructure mass function at z=0.2 using strong gravitational lenses
• Dark matter subhalos and the dwarf satellites of the Milky Way
• Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
• Using Convolutional Neural Networks to identify Gravitational Lenses in Astronomical images
• On the Power Spectrum of Dark Matter Substructure in Strong Gravitational Lenses

Cited by in corpus (17)

• From Images to Dark Matter: End-To-End Inference of Substructure From Hundreds of Strong Gravitational Lenses
• Sensitivity of strong lensing observations to dark matter substructure: a case study with Euclid
• Quantifying the Line-of-Sight Halo Contribution to the Dark Matter Convergence Power Spectrum from Strong Gravitational Lenses
• GIGA-Lens: Fast Bayesian Inference for Strong Gravitational Lens Modeling
• Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses
• Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting
• Strong Lensing by Galaxies
• Astrophysical Tests of Dark Matter Self-Interactions
• Machine Learning and Cosmology
• Targeted Likelihood-Free Inference of Dark Matter Substructure in Strongly-Lensed Galaxies
• Modeling lens potentials with continuous neural fields in galaxy-scale strong lenses
• Towards a method to anticipate dark matter signals with deep learning at the LHC
• Dark Matter Subhalos, Strong Lensing and Machine Learning
• Measuring the Substructure Mass Power Spectrum of 23 SLACS Strong Galaxy-Galaxy Lenses with Convolutional Neural Networks
• Strong Gravitational Lensing Effects of the Rotating Short-Haired Black Hole and Constraints from EHT Observations
• A dense dark matter core of the subhalo in the strong lensing system JVAS B1938+666
• Revisiting the dynamics of Bose-Einstein condensates in a double well by deep learning with a hybrid network