Skip to ContentSkip to Navigation
About us Faculty of Science and Engineering Data Science & Systems Complexity (DSSC) About Data Science & Systems Complexity (DSSC)

Reseach profile P. (Petra) Awad

P. (Petra) Awad

Project: Machine Learning for the Detection of "Filament-like" Astronomical Structures.

We propose studying filamentary stellar streams present in the Milky Way with the aim of uncovering evolutionary properties of our galaxy through its past interactions with galaxy satellites. Such interactions result in the formation of the streams which we wish to study. The outcomes of this project can also serve as a tool for galaxy mass measurements, dark matter studies, and exploration of the cosmic web. We plan to extend and enhance the tools developed by the SUNDIAL network which have shown promising results for similar applications. These tools include: strategies from Evolutionary Computation particularly a biologically inspired ant colony algorithm for the detection of dense structures within point clouds, Structure Aware Filtering for reduction and pre-processing of the data while preserving topological structure, and N-body simulations for studying the temporal evolution of the structures at hand. We demonstrate the strategies’ performance by comparing computational predictions to synthetic and observational datasets provided by the newest satellite data sources. Our contribution to the previous work will improve on the developed tools taking advantage of newly released datasets, and will provide quantitative measures of topological features of stellar streams extracted from simulations and observational data.

Keywords: Machine Learning, Structure Detection, Stellar Streams, Milky Way.

Fields of Expertise: Data Science, Machine Learning, Astronomy.

Last modified:01 October 2020 12.47 p.m.