Galaxy Merger Reconstruction with Generative Graph Neural Networks
Date: Tuesday, 10 January 2023
Time: 8:00 UTC (7:00 pm AEST)
Speaker: A/Prof Yuan-Sen Ting (Australian National University, Australia)
Contact the organizer: Andriy Olenko (a.olenko@latrobe.edu.au)
Abstract:
A key yet unresolved question in modern-day astronomy is how galaxies formed and evolved. The quest to understand how galaxies evolve has led many semi-analytic models to infer the galaxy properties from their merger history. However, most classical approaches rely on studying the global connection between dark matter haloes and galaxies, often reducing the study to crude summary statistics. The recent advancement in graph neural networks might open up many new possibilities; graphs are a natural descriptor of galaxy progenitor systems – any progenitor system at a high redshift can be regarded as a graph, with individual progenitors as nodes on the graph. In this presentation, I will discuss the power of generative graph neural networks to connect high-redshift progenitor systems with local observables. We showed that based on equivariant graph normalizing flow, our model could robustly recover the progenitor systems, including their masses, merging redshifts and pairwise distances at redshift z = 2 conditioned on their z = 0 properties. In addition, the probabilistic nature of our model enables other downstream tasks, including detecting anomalies in galaxy configuration and identifying subtle correlations of the progenitor features.
