Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation

Abstract

Detecting substructure within strongly lensed images is a promising route to shed light on the nature of dark matter. However, it is a challenging task, which traditionally requires detailed lens modeling and source reconstruction, taking weeks to analyze each system. We use machine-learning to circumvent the need for lens and source modeling and develop a neural network to both locate subhalos in an image as well as determine their mass using the technique of image segmentation. The network is trained on images with a single subhalo located near the Einstein ring across a wide range of apparent source magnitudes. The network is then able to resolve subhalos with masses $m\gtrsim 10^{8.5}{M}_{\odot }$. Training in this way allows the network to learn the gravitational lensing of light, and remarkably, it is then able to detect entire populations of substructure, even for locations further away from the Einstein ring than those used in training. Over a wide range of the apparent source magnitude, the false-positive rate is around three false subhalos per 100 images, coming mostly from the lightest detectable subhalo for that signal-to-noise ratio. With good accuracy and a low false-positive rate, counting the number of pixels assigned to each subhalo class over multiple images allows for a measurement of the subhalo mass function (SMF). When measured over three mass bins from $10^9{M}_{\odot }$--$10^{10}{M}_{\odot }$ the SMF slope is recovered with an error of 36% for 50 images, and this improves to 10% for 1000 images with Hubble Space Telescope-like noise.

v1:23 + 5 pages, 12 + 2 figures. v2: matches accepted version in ApJ. v3: matches version published in ApJ

References in corpus (16)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Ultralight scalars as cosmological dark matter
• 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
• The stellar mass-halo mass relation of isolated field dwarfs: a critical test of $\Lambda$CDM at the edge of galaxy formation
• Inference of the Cold Dark Matter substructure mass function at z=0.2 using strong gravitational lenses
• Analytic models of plausible gravitational lens potentials
• Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
• Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
• MeV Dark Matter and Small Scale Structure
• On the Power Spectrum of Dark Matter Substructure in Strong Gravitational Lenses
• Direct Detection of Dark Matter Substructure in Strong Lens Images with Convolutional Neural Networks
• Quantifying the Line-of-Sight Halo Contribution to the Dark Matter Convergence Power Spectrum from Strong Gravitational Lenses
• Image segmentation for analyzing galaxy-galaxy strong lensing systems
• Decoding Dark Matter Substructure without Supervision
• Hunting for Dark Matter Subhalos in Strong Gravitational Lensing with Neural Networks

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