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Physical Cosmology Group

                                                                                                                        Abell 2218 (Credit: NASA Images)

abell

We are located at the University Observatory Munich (USM). We work on the interface between theoretical and observational cosmology. Our main research interest is in confronting modern cosmological theories with observations. Here we have in particular a strong research program in exploiting galaxy clusters and cosmic voids, but also in more general probes of the large-scale structure and the cosmic microwave back ground. One of our main motivations is to understand the nature of the cosmic acceleration in the Universe. Here we try to constrain theoretical models from standard dark energy, coupled scalar fields to theories which extend Einstein's gravity at large distances. In order to achieve this goal we use analytical and numerical methods, such as N-body simulations of the structure formation process, state of the art statistical analysis tools and modern Bayesian techniques. Furthermore we have a strong research program in machine learning applications in astrophysics, but also apply these methods in medical physics and string theory.

We are involved in the following national and international collaborations: The Dark Energy Survey DES, The Euclid satellite mission of ESA, the Dark Energy Science Collaboration DESC at the Rubin Observatory Legacy Survey of Space and Time, the Hobby-Eberly Telescope Dark Energy Experiment  HETDEX, the eRosita x-ray satellite mission, the Square Kilometer Array SKA and the LiteBird satellite mission.

We work together with other groups at the Max Planck institutes. Among others: OPINAS and High Energy Astrophysics groups at MPE, the Cosmology research group at MPA, as well as with members of other groups at the Excellence Cluster and groups at MPP.

Recent Papers by Group Members

Euclid preparation. XXX. Performance assessment of the NISP red grism through spectroscopic simulations for the wide and deep surveys, Euclid Collaboration et al.,Astronomy and Astrophysics, 676, A34, (2023)

The XMM cluster survey: exploring scaling relations and completeness of the dark energy survey year 3 redMaPPer cluster catalogue, Upsdell, E. W. et al.,Monthly Notices of the Royal Astronomical Society, 522, 5267, (2023)

Cosmological Constraints from the BOSS DR12 Void Size Function, Contarini, Sofia et al.,The Astrophysical Journal, 953, 46, (2023)

Why cosmic voids matter: nonlinear structure & linear dynamics, Schuster, Nico et al.,Journal of Cosmology and Astroparticle Physics, 2023, 031, (2023)

On the relative bias of void tracers in the Dark Energy Survey, Pollina, G. et al.,Monthly Notices of the Royal Astronomical Society, 487, 2836, (2019)

The Dark Energy Survey Image Processing Pipeline, Morganson, E. et al.,Publications of the Astronomical Society of the Pacific, 130, 074501, (2018)

Simulating the inflationary Universe: from single-field to the axion-U(1) model, Caravano, Angelo et al.,arXiv:2209.13616

Lattice simulations of Abelian gauge fields coupled to axions during inflation, Caravano, Angelo et al.,Physical Review D, 105, 123530, (2022)

Improving the accuracy of estimators for the two-point correlation function, Kerscher, Martin et al.,Astronomy and Astrophysics, 666, A181, (2022)

Cosmological covariance of fast radio burst dispersions, Reischke, Robert et al.,Monthly Notices of the Royal Astronomical Society, 524, 2237, (2023)

Consistent constraints on the equivalence principle from localized fast radio bursts, Reischke, Robert et al.,Monthly Notices of the Royal Astronomical Society, 523, 6264, (2023)

Euclid preparation. XXVIII. Forecasts for ten different higher-order weak lensing statistics, Euclid Collaboration et al.,Astronomy and Astrophysics, 675, A120, (2023)

Euclid preparation. XXIX. Water ice in spacecraft Part I: The physics of ice formation and contamination, Euclid Collaboration et al.,Astronomy and Astrophysics, 675, A142, (2023)

CYJAX: A package for Calabi-Yau metrics with JAX, Gerdes, Mathis et al.,Machine Learning: Science and Technology, 4, 025031, (2023)

The bias of cosmic voids in the presence of massive neutrinos, Schuster, Nico et al.,Journal of Cosmology and Astroparticle Physics, 2019, 055, (2019)