Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation
This work addresses the challenge of analyzing dark matter substructure in astrophysics, offering a faster alternative to traditional methods, but it is incremental as it applies machine learning to a known bottleneck in the field.
The paper tackled the problem of detecting dark matter substructure in strong lens images by developing a neural network for image segmentation to locate subhalos and determine their mass, achieving detection of masses above 10^8.5 M_sun with a false-positive rate of about 3 per 100 images and recovering the subhalo mass function slope with errors as low as 10% for 1000 images.
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 $10^9M_{\odot}$--$10^{10} M_{\odot}$ 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.