Café-club GECO

Rémy KOSKAS (Laboratoire Univers et Théories, Observatoire de Paris Meudon, PSL, Université Paris Cité):“Dark matter halos shape as a strong cosmological probe”.

Virtual rooom: https://univ-amu-fr.zoom.us/j/96876832544?pwd=NnBaZEdXd3dFUFBRSkRFY05WS1d4dz09

Abstract: Halo collapse is a complex nonlinear process, intertwining both gravitational and cosmological phenomena. One of the manifestations of this complexity is the shape of the resulting present-day halos: simulations and observations show that they are triaxial objects. Interestingly, those shapes carry cosmological information; I shall prove by two methods that cosmology, and particularly the dark energy model, leaves a lasting trace on the present-day halos and their properties. First, we show that the overall shape of the halo exhibits a different behavior when the DE model is varied. Then, we implement machine learning methods to classify halos according to their corresponding cosmology. Those are defined to efficiently keep track of the matter distribution anisotropies. Such attributes allow a properly trained learning device to find the dark energy model of the Universe within which these halos have grown, for more than 72% of our 10^13-10^14 Msun test halos. We also study the misleading ML methodological biases, aka “Clever Hans effects”, and the way to fix them. To that end, I will use the Dark Energy Universe Simulations (DEUS) halos: they grew assuming several dark energy models whose parameters were chosen in agreement with both CMB and SN Ia data. The resulting halos are thus extremely close from one cosmological model to another.