Equivariant Neural Networks for Classification of Dark Matter Substructure

  • The study of substructures in the dark matter has shown signs of promise to deliver on the open-ended problem of the identity of dark matter. Strong gravitational lensing has been proved to be a strong probe for dark matter substructure.

  • Current convolutional neural networks are only capable of translational equivariance. However, in a number of application (including ours), a larger groups of symmetries, including rotations and reflections are present in the data as well that needs to be exploited. This gives rise to the notion of Equivariant Convolutional Networks.

  • This project is the follow up work of the previous attempts to study and determine the morphology of dark matter substructure using deep learning based approaches.

  • Read more about the project on my Medium article.

Apoorva Vikram Singh
Apoorva Vikram Singh
Masters Student in Neural Information Processing

My research interests include Theoretical Machine Learning.