Spherinator and HiPSter: Representation Learning for Unbiased Knowledge Discovery from Simulations
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要