Scientists Develop 'Time Machine' Simulation to Study Ancestors of Distant Galaxies

CC0 / Pixabay / Time Machine
Time Machine - Sputnik International, 1920, 12.06.2022
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Light from the galaxies the research team set their sights on travelled a distance of some 11 billion light-years to reach our planet.
A team of researchers led by Kavli Institute for the Physics and Mathematics of the Universe Project researcher Metin Ata and Project Assistant Professor Khee-Gan Lee has managed to produce simulations that recreate full life cycles of whole collections of galaxies observed some 11 billion years ago, SciTechDaily reports.
According to the media outlet, the team sought to gain insight into massive galaxy protoclusters, the ancestors of existing galaxy clusters, but discovered that existing studies of these distant structures were done with simple models rather than with simulations.

“We wanted to try developing a full simulation of the real distant universe to see how structures started out and how they ended”, Ata said.

As it takes light from the galaxies the researchers were focusing on some 11 billion light-years to reach Earth, Lee compared the development of the simulation to building a time machine.
“It’s like finding an old black-and-white picture of your grandfather and creating a video of his life”, he said.
The central smudge in the center of the image is NGC 1052-DF2, the first galaxy discovered by astronomers containing little to no dark matter - Sputnik International, 1920, 20.05.2022
Galaxies Devoid of Dark Matter Formed During Massive Collision Billions of Years Ago, Study Says
The researchers essentially took “snapshots” of those “young grandparent galaxies”, as the media outlet put it, and then fast-forwarded their age to examine the process of galaxy clusters’ formation.

“This is something that is very important for the fate of those structures whether they are isolated or associated with a bigger structure”, Ata remarked. “If you don’t take the environment into account, then you get completely different answers. We were able to take the large-scale environment into account consistently, because we have a full simulation, and that’s why our prediction is more stable”.

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