Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation
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Detecting substructure within strongly lensed images is a promising route to shed light on the nature of dark matter. 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 method 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. Training in this way allows the network to learn the gravitational lensing of light and it is then able to accurately detect entire populations of substructure, even far from the Einstein ring. In images with a single subhalo and without noise, the network detects subhalos of mass 10^6 M_⊙ 62 the correct mass bin. The detection accuracy increases for heavier masses. When random noise at the level of 1 (which is a realistic approximation HST, for sources brighter than magnitude 20), the network loses sensitivity to the low-mass subhalos; with noise, the 10^8.5M_⊙ subhalos are detected 86 M_⊙subhalos are only detected 38 is around 2 false subhalos per 100 images with and without noise, coming mostly from masses≤10^8 M_⊙. 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 five mass bins from10^8 M_⊙to10^10 M_⊙the SMF slope is recovered with an error of 14.2 (16.3) this improves to 2.1 (2.6)
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