English

Dark Matter Subhalos, Strong Lensing and Machine Learning

Cosmology and Nongalactic Astrophysics 2020-05-13 v1 Astrophysics of Galaxies

Abstract

We investigate the possibility of applying machine learning techniques to images of strongly lensed galaxies to detect a low mass cut-off in the spectrum of dark matter sub-halos within the lens system. We generate lensed images of systems containing substructure in seven different categories corresponding to lower mass cut-offs ranging from 109M10^9M_\odot down to 106M10^6M_\odot. We use convolutional neural networks to perform a multi-classification sorting of these images and see that the algorithm is able to correctly identify the lower mass cut-off within an order of magnitude to better than 93% accuracy.

Keywords

Cite

@article{arxiv.2005.05353,
  title  = {Dark Matter Subhalos, Strong Lensing and Machine Learning},
  author = {Sreedevi Varma and Malcolm Fairbairn and Julio Figueroa},
  journal= {arXiv preprint arXiv:2005.05353},
  year   = {2020}
}

Comments

20 pages

R2 v1 2026-06-23T15:28:08.809Z