Reconstructing the assembly history of our Galaxy using neural networks

Student: Andrea Sante
Supervisors: Andreea Font (ARI, LJMU), Sandra Ortega Martorell (CSM, LJMU), Ivan Olier-Caparroso (CSM, LJMU)
Internship: Liverpool Heart and Chest Hospital

In the standard cosmological model, galaxies form hierarchically by accreting and tidally disrupting smaller ('dwarf') galaxies over billions of years.  The debris left from the destruction of satellite dwarf galaxies can be found today in the form of tidal streams in the stellar haloes of host galaxies.  From the number and shapes of these stellar streams one can reconstruct the accretion history of galaxies and constrain the nature of dark matter. 

The identification of tidal streams in our Galaxy is difficult because these features are extremely faint, and they also lose coherence quickly after a few orbits around the Galaxy. Machine learning (ML) techniques are critical in finding these features and in categorizing their shapes. In this project, we will use a combination of convolutional and recurrent neural networks (CNN and RNN) to extract the contextual information related to the tidal streams and to model their time evolution.

We will train these networks on Artemis, a suite of state-of-the cosmological simulations that follows the formation of 45 galaxies like the Milky Way in a realistic cosmological context. This suite contains a large dataset of simulated tidal streams that will allow us to test and reduce the bias of current ML stream identification techniques. Additional improvements over the existing techniques will be achieved by including other information captured by these simulations, namely the kinematics and chemical abundance of the stars in the streams. By training the NNs on a multi-dimensional parameter space we will improve the identification of tidal streams, not only in the simulations but also in observational surveys (e.g. Gaia, LSST, Euclid). 

The methods developed in this project will be also applied for the detection of features in cardiac images in collaboration with the Liverpool Heart and Chest Hospital.