Dark Matter Subhalos, Strong Lensing and Machine Learning. Varma, S., Fairbairn, M., & Figueroa, J. arXiv e-prints, 2005:arXiv:2005.05353, May, 2020. Paper abstract bibtex 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 \$10{\textasciicircum}9M_{\textbackslash}odot\$ down to \$10{\textasciicircum}6M_{\textbackslash}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.
@article{varma_dark_2020,
title = {Dark {Matter} {Subhalos}, {Strong} {Lensing} and {Machine} {Learning}},
volume = {2005},
url = {http://adsabs.harvard.edu/abs/2020arXiv200505353V},
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 \$10{\textasciicircum}9M\_{\textbackslash}odot\$ down to \$10{\textasciicircum}6M\_{\textbackslash}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.},
urldate = {2020-05-15},
journal = {arXiv e-prints},
author = {Varma, Sreedevi and Fairbairn, Malcolm and Figueroa, Julio},
month = may,
year = {2020},
keywords = {Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics},
pages = {arXiv:2005.05353},
}
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