Revealing the Local Cosmic Web from Galaxies by Deep Learning

TL;DR

This paper demonstrates a deep learning approach to reconstruct the Cosmic Web's dark matter distribution from galaxy data, enabling insights into the Universe's large-scale structure.

Contribution

It introduces a convolutional neural network method trained on cosmological simulations to map galaxy positions to the Cosmic Web, including the local dark matter distribution.

Findings

Successfully reconstructed the Cosmic Web from galaxy data.

Validated the method using Illustris-TNG and EAGLE simulations.

Produced a local dark-matter map from Cosmicflows-3 data.

Abstract

The 80% of the matter in the Universe is in the form of dark matter that comprises the skeleton of the large-scale structure called the Cosmic Web. As the Cosmic Web dictates the motion of all matter in galaxies and inter-galactic media through gravity, knowing the distribution of dark matter is essential for studying the large-scale structure. However, the Cosmic Web's detailed structure is unknown because it is dominated by dark matter and warm-hot inter-galactic media, both of which are hard to trace. Here we show that we can reconstruct the Cosmic Web from the galaxy distribution using the convolutional-neural-network-based deep-learning algorithm. We find the mapping between the position and velocity of galaxies and the Cosmic Web using the results of the state-of-the-art cosmological galaxy simulations, Illustris-TNG. We confirm the mapping by applying it to the EAGLE simulation. Finally, using the local galaxy sample from Cosmicflows-3, we find the dark-matter map in the local Universe. We anticipate that the local dark-matter map will illuminate the studies of the nature of dark matter and the formation and evolution of the Local Group. High-resolution simulations and precise distance measurements to local galaxies will improve the accuracy of the dark-matter map.