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Researchers capture direct high-definition image of the “Cosmic Web”

Matter in intergalactic space is distributed in a vast network of interconnected filamentary structures, collectively referred to as the cosmic web. With hundreds of hours of observations, an international team of researchers has now obtained an unprecedented high-definition image of a cosmic filament inside this web, connecting two active forming galaxies – dating back to when the Universe was about 2 billion years old. more

Towards direct observation of large samples of intergalactic filaments in the early universe

The distribution of matter in the universe is predicted by supercomputer simulations to occur in a network of filaments, known as the "cosmic web", where galaxies form and evolve. The vast majority of this intricate structure is in the form of diffuse hydrogen gas, so rarefied that it is extremely challenging to observe it directly. A collaboration led by MPA researchers has targeted the active supermassive black holes of galaxy pairs at close separations to reveal the connecting filamentary structures of the cosmic web in the early universe. The results are promising and unveil evidence for such structures stretching between the observed pairs, ultimately providing excellent targets for future ultra-deep observations. more

Field-Level Inference: Unlocking the Full Potential of Galaxy Maps to Explore New Physics

Galaxies are not islands in the cosmos. While globally the universe expands – driven by the mysterious ‘dark energy’ – locally, galaxies cluster through gravitational interactions, forming the cosmic web held together by dark matter’s gravity. For cosmologists, galaxies are test particles to study gravity, dark matter and dark energy. For the first time, MPA researchers and alumni have now used a novel method that fully exploits all information in galaxy maps and applied it to simulated but realistic datasets. Their study demonstrates that this new method will provide a much more stringent test of the cosmological standard model, and has the potential to shed new light on gravity and the dark universe. more

Explaining the density profiles of dark matter halos with neural networks

Can machine learning make new discoveries in astrophysics? An ‘explainable’ neural network is employed to get insights into the origin of dark matter halo density profiles. The network discovers that the shape of the profile in the halo outskirts is described by a single parameter related to the most recent accretion of mass. This is done without prior knowledge of the halo’s evolution history being provided during training.
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Understanding the cosmic web: Unveiling the evolution of cosmic filaments with the MillenniumTNG simulation

A careful analysis of the filaments in the cosmic large-scale structure has revealed interesting new findings about the evolution and complexities of the cosmic web. While some filaments show a significant evolution – depending on their cosmic environment – global filament properties are preserved, which could be used in future cosmological studies. The MPA team also developed a new method to allow for rigorous calibration of the filament catalogues.
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