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- W3024037224 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 $10^9M_odot$ 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." @default.
- W3024037224 created "2020-05-21" @default.
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- W3024037224 date "2020-05-11" @default.
- W3024037224 modified "2023-10-14" @default.
- W3024037224 title "Dark Matter Subhalos, Strong Lensing and Machine Learning" @default.
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