This page will be used to track the progress of my research project. My goal is to discover structures in the universe so vast that their existence is impossible under natural formation processes. First, some definitions.

Computational Astrophysics: A branch of astrophysics that uses numerical methods, algorithms, and computer simulations to model and study complex astrophysical systems and phenomena that cannot be solved analytically.

Ultra-Large-Scale Structure (ULSS): A vast, gravitationally organized arrangement of matter in the universe—such as galaxy filaments, walls, and voids—spanning hundreds of millions to billions of light-years, beyond the scale of individual clusters or superclusters.

Machine Learning (ML): A field of computer science that develops algorithms enabling systems to learn patterns from data and make predictions or decisions without being explicitly programmed for specific tasks.

Background

A recently published paper reported the discovery of structures within the universe so vast that they break our current understanding of cosmic structure formation. According to current theory, these structures are so large that their existence is impossible under natural formation processes. I have reason to believe that there are many such structures within the universe, and will attempt to discover more of them by applying ML algorithms to astronomical datasets.

This page will be updated as the project progresses. Andrew