Deep learning the astrometric signature of dark matter substructure. (arXiv:2008.11577v1 [astro-ph.CO])
<a href="http://arxiv.org/find/astro-ph/1/au:+Vattis_K/0/1/0/all/0/1">Kyriakos Vattis</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Toomey_M/0/1/0/all/0/1">Michael W. Toomey</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Koushiappas_S/0/1/0/all/0/1">Savvas M. Koushiappas</a>
We study the application of machine learning techniques for the detection of
the astrometric signature of dark matter substructure. In this proof of
principle a population of dark matter subhalos in the Milky Way will act as
lenses for sources of extragalactic origin such as quasars. We train ResNet-18,
a state-of-the-art convolutional neural network to classify angular velocity
maps of a population of quasars into lensed and no lensed classes. We show that
an SKA -like survey with extended operational baseline can be used to probe the
substructure content of the Milky Way.
We study the application of machine learning techniques for the detection of
the astrometric signature of dark matter substructure. In this proof of
principle a population of dark matter subhalos in the Milky Way will act as
lenses for sources of extragalactic origin such as quasars. We train ResNet-18,
a state-of-the-art convolutional neural network to classify angular velocity
maps of a population of quasars into lensed and no lensed classes. We show that
an SKA -like survey with extended operational baseline can be used to probe the
substructure content of the Milky Way.
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