Cosmological Inference with Cosmic Voids and Neural Network Emulators

TL;DR

This paper introduces neural network emulators for cosmic void statistics, significantly speeding up cosmological parameter estimation and capturing more information than traditional analytical models, with robustness to simulation variables.

Contribution

The authors develop neural emulators for void size function and density profiles that outperform analytical models and are robust to simulation resolution and baryonic effects.

Findings

Emulators enable orders-of-magnitude faster parameter estimation.

Void statistics improve constraints on $\

The approach is robust to simulation resolution and baryonic physics effects.

Abstract

Cosmic Voids are a promising probe of cosmology for spectroscopic galaxy surveys due to their unique response to cosmological parameters. Their combination with other probes promises to break parameter degeneracies. Due to simplifying assumptions, analytical models for void statistics are only representative of a subset of the full void population. We present a set of neural-based emulators for void summary statistics of watershed voids, which retain more information about the full void population than simplified analytical models. We build emulators for the void size function and void density profiles traced by the halo number density using the Quijote suite of simulations for a broad range of the $\Lambda\mathrm{CDM}$ parameter space. The emulators replace the computation of these statistics from computationally expensive cosmological simulations. We demonstrate the cosmological constraining power of voids using our emulators, which offer orders-of-magnitude acceleration in parameter estimation, capture more cosmological information compared to analytic models, and produce more realistic posteriors compared to Fisher forecasts. We find that the parameters $\Omega_m$ and $\sigma_8$ in this Quijote setup can be recovered to $14.4\%$ and $8.4\%$ accuracy respectively using void density profiles; including the additional information in the void size function improves the accuracy on $\sigma_8$ to $6.8\%$. We demonstrate the robustness of our approach to two important variables in the underlying simulations, the resolution, and the inclusion of baryons. We find that our pipeline is robust to variations in resolution, and we show that the posteriors derived from the emulated void statistics are unaffected by the inclusion of baryons with the Magneticum hydrodynamic simulations. This opens up the possibility of a baryon-independent probe of the large-scale structure.