Natural computation techniques for uncovering low-dimensional topological structures in large scale astronomical simulations

During my PhD studies, I collaborated in an exciting project between astronomeers and computer scientists. We developed machine learning strategies to detect, extract, and denoise manifolds embedded in potentially large amounts of background noise in higher dimensions. Examples of such a dataset are filaments in an N-body simulation of the Cosmic web or streams of a jellyfish-like dwarf galaxy. Furthermore, we proposed a subsampling technique that preserves the crucial topological information of large astronomical datasets. Extracting information
from large data sets, such as discovering bubbles in a dwarf galaxy simulation, is often resource-demanding. As a result, the proposed subsampling is of utmost importance. Besides, to further continue the study, the position of particles on the border of bubbles should be determined. We also suggest a pipeline to recover a tight boundary around each bubble and finally, we conduct an extensive analysis of the temperature, velocity of expansion, age, and other physical features of the discovered bubbles through several snapshots of the dwarf galaxy simulation.