Optimal machine-driven acquisition of future cosmological data

TL;DR

This paper introduces a method to map regions of the sky based on their potential to provide information about the universe's structure, guiding targeted data collection for cosmology.

Contribution

It presents a novel physical modeling approach that surpasses traditional statistical summaries, enabling detailed analysis of individual 3D cosmic structures for optimal data acquisition.

Findings

Regions near filaments and cluster cores are most informative for structure formation.

The approach demonstrates the potential for targeted cosmological searches based on information gain.

First of its kind to map inhomogeneous distribution of cosmological information.

Abstract

We present maps classifying regions of the sky according to their information gain potential as quantified by the Fisher information. These maps can guide the optimal retrieval of relevant physical information with targeted cosmological searches. Specifically, we calculate 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 demonstrate this using galaxy counts data and 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 about 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 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.