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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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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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The distribution of galaxies on large, cosmological scales holds important clues on the nature of dark matter, the properties of dark energy and the origin of our Universe. Yet, optimally retrieving this information from observations is challenging. MPA researchers are developing a novel analysis approach, where they follow the evolution of cosmic structures through their entire formation history. Enabling a very detailed comparison between theoretical models and observational data, this approach will allow measuring key parameters of dark matter and dark energy very precisely. more

MPA postdoc Maria Werhahn has been awarded the Carl Ramsauer Prize by the Physikalische Gesellschaft zu Berlin for her doctoral thesis “Simulating Galaxy Evolution with Cosmic Rays: The Multi-Frequency View”. The award ceremony took place on 22 November 2023 at the Technical University of Berlin, where she also presented her work in a short lecture. more

At its 2023 Annual Meeting, the German Astronomical Society elected Volker Springel, Director at the Max Planck Institute for Astrophysics as Vice President. more