Impacts of source morphology on the detectability of subhalos in strong lenses

TL;DR

This study demonstrates that a convolutional neural network can effectively detect dark matter subhalos in strong galaxy-galaxy lens images despite complex source light morphology and varying data resolution, highlighting its robustness and potential for dark matter research.

Contribution

The paper introduces a ResNet50-based neural network trained on a large simulated dataset to identify subhalo signals in complex lensing scenarios, showing high detection accuracy even at lower resolutions.

Findings

Network detects subhalos down to low masses distinguishing dark matter models.

Source complexity has minimal impact on detection accuracy beyond 3 clumps.

Model maintains 74% accuracy at natural seeing resolution.

Abstract

We provide an analysis of a convolutional neural network's ability to identify the lensing signal of single dark matter subhalos in strong galaxy-galaxy lenses in the presence of increasingly complex source light morphology. We simulate a balanced dataset of 800,000 strong lens images both perturbed and unperturbed by a single subhalo ranging in virial mass between $10^{7.5} M_{\odot} - 10^{11}M_{\odot}$ and characterise the source complexity by the number of Sersic clumps present in the source plane ranging from 1 to 5. Using the ResNet50 architecture we train the network to classify images as either perturbed or unperturbed. We find that the network is able to detect subhalos at low enough masses to distinguish between dark matter models even with complex sources and that source complexity has little impact on the accuracy beyond 3 clumps. The model was more confident in its classification when the clumps in the source were compact, but cared little about their spatial distribution. We also tested for the resolution of the data, finding that even in conditions akin to natural seeing the model was still able to achieve an accuracy of 74% in our highest peak signal-to-noise datasets, though this is heavily dominated by the high mass subhalos. It's robustness against resolution is attributed to the model learning the flux ratio anomalies in the perturbed lenses which are conserved in the lower resolution data.