Spherinator and HiPSter: Representation Learning for Unbiased Knowledge Discovery from Simulations

TL;DR

This paper introduces a novel machine learning approach using a rotational invariant hyperspherical autoencoder to create low-dimensional, interpretable representations of astrophysical simulation data, facilitating exploration and analysis.

Contribution

It presents a flexible, unbiased method for extracting meaningful insights from large simulations, enabling interactive visualization and analysis in the Exascale era.

Findings

Created a hyperspherical autoencoder for galaxy data

Generated a Hubble tuning fork-like similarity space

Demonstrated interactive visualization with HiPS tilings

Abstract

Simulations are the best approximation to experimental laboratories in astrophysics and cosmology. However, the complexity, richness, and large size of their outputs severely limit the interpretability of their predictions. We describe a new, unbiased, and machine learning based approach to obtaining useful scientific insights from a broad range of simulations. The method can be used on today's largest simulations and will be essential to solve the extreme data exploration and analysis challenges posed by the Exascale era. Furthermore, this concept is so flexible, that it will also enable explorative access to observed data. Our concept is based on applying nonlinear dimensionality reduction to learn compact representations of the data in a low-dimensional space. The simulation data is projected onto this space for interactive inspection, visual interpretation, sample selection, and local analysis. We present a prototype using a rotational invariant hyperspherical variational convolutional autoencoder, utilizing a power distribution in the latent space, and trained on galaxies from IllustrisTNG simulation. Thereby, we obtain a natural Hubble tuning fork like similarity space that can be visualized interactively on the surface of a sphere by exploiting the power of HiPS tilings in Aladin Lite.