Machine-driven searches for cosmological physics

We present maps revealing the expected information content of cosmic large-scale structures concerning cosmological physics. These maps can guide the optimal retrieval of relevant physical information with targeted cosmological searches. This approach has become feasible through the recent development of causal inference machinery that is informed on the physics of cosmic structure formation. Specifically, we measure the response of observed cosmic structures to perturbative changes in the cosmological model and chart their respective contributions to the Fisher information. Our physical forward modeling machinery transcends the limitations of contemporary analyses based on statistical summaries to yield detailed characterizations of individual 3D structures. We showcase the potential of our approach by studying the information content of the Coma cluster. We find that regions in the vicinity of the filaments and cluster core, where mass accretion ensues from gravitational infall, are the most informative. The results presented in this work are the first of their kind and elucidate the inhomogeneous distribution of cosmological information in the Universe. This study paves a new way forward to perform efficient targeted searches for the fundamental physics of the Universe, where search strategies are progressively refined with new cosmological data sets within an active learning framework.