Revealing the Local Cosmic Web from Galaxies by Deep Learning

Sungwook E. Hong, Donghui Jeong, Ho Seong Hwang, Juhan Kim

Research output: Contribution to journalArticlepeer-review

Abstract

A total of 80% of the matter in the universe is in the form of dark matter that composes the skeleton of the large-scale structure called the cosmic web. As the cosmic web dictates the motion of all matter in galaxies and intergalactic 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 intergalactic 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 of 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.

Original languageEnglish (US)
Article number76
JournalAstrophysical Journal
Volume913
Issue number1
DOIs
StatePublished - May 20 2021

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

Fingerprint

Dive into the research topics of 'Revealing the Local Cosmic Web from Galaxies by Deep Learning'. Together they form a unique fingerprint.

Cite this