Manifold Learning for Cosmic Structures

We present a scalable manifold learning approach to represent galaxies in a low-dimensional embedding space based on the geometry of their surrounding structure. We validate this method on a toy dataset consisting of points in balls and lines in space, and demonstrate its utility for astrophysics research on the realistic TNG100 galaxy simulation box. For both datasets, our method effectively captures the local structure around each galaxy. For the TNG100 simulations we show that our first embedding dimension correlates with halo mass and star-formation rate, which aligns with known physical relationships.