Neural networks based dark matter Substructures Finder (NSF)

Hunting for dark matter substructure in Strong lensing with Neural networks

Joshua Yao-Yu Lin*, Hang Yu*, Warren Morningstar, Jian Peng, Gilbert Holder

University of Illinois at Urbana-Champaign
Stanford University

Dark matter substructures are interesting since they can reveal the properties of dark matter, especially the cold dark matter small-scale problems such as missing satellites problem. In recent years, it has become possible to detect individual dark matter subhalos near images of strongly lensed extended background galaxies. In this work, we discuss the possibility of using deep neural networks to detect dark matter subhalos, and showing some preliminary result with simulated data.

In this work, we show that the neural networks are able to preform fast and automatic detection of dark matter subhalos in strong gravitational system. In the future, we would have more strong gravitaitional galaxy-galaxy lensing system, so neural networks provide a way to tackle the big datasets to constraint dark matter models.