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arXiv 2005.05353astro-ph.COastro-ph.GA
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Dark Matter Subhalos, Strong Lensing and Machine Learning

Sreedevi Varma, Malcolm Fairbairn, Julio Figueroa

英文摘要:

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 $10^9M_\odot$ down to $10^6M_\odot$. 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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