Abstract

We present a set of maps classifying regions of the sky according to their information gain potential as quantified by Fisher information. These maps can guide the optimal retrieval of relevant physical information with targeted cosmological searches. Specifically, we calculated the response of observed cosmic structures to perturbative changes in the cosmological model and we charted their respective contributions to 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 demonstrate this advantage using galaxy counts data and we showcase the potential of our approach by studying the information gain 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 with regard to our physical model of structure formation in the Universe. Hence, collecting data in those regions would be most optimal for testing our model predictions. The results presented in this work are the first of their kind to elucidate the inhomogeneous distribution of cosmological information in the Universe. This study paves a new way forward for the performance of 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.

Highlights

  • One of the most outstanding questions of astrophysical research asks where we might look next to find something new in the universe

  • This study paves a new way forward for the performance of 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

  • The results presented in this work demonstrate the feasibility of machine-aided targeted searches for cosmological physics signals

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Summary

Introduction

One of the most outstanding questions of astrophysical research asks where we might look next to find something new in the universe. This has become feasible due to the availability of large-scale inferences that are informed by physics and causality of the cosmic large-scale structures and their evolution with time. We have proposed the Bayesian physical forward-modeling framework (Jasche & Wandelt 2013; Lavaux & Jasche 2016; Jasche & Lavaux 2019) as a novel method enabling us to reconstruct the full 3D density field underlying observed galaxies in surveys with high fidelity This machinery performs a causal inference that is informed by physics of the cosmic large-scale structures, their initial. Our proposed methodology is not limited to the nearby Universe, and is applicable to any existing cosmological data sets

Results
Evolved density field
Discussion
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