Extracting the Alcock-Paczyński signal from voids: A novel approach via reconstruction

¹ Aix-Marseille Université, CNRS/IN2P3, CPPM Marseille, France
² Dipartimento di Fisica, Università di Roma Tre Via della Vasca Navale 84 I-00146 Roma, Italy
³ SISSA, International School for Advanced Studies Via Bonomea 265 34136 Trieste TS, Italy
⁴ ICSC – Centro Nazionale di Ricerca in High Performance Computing, Big Data e Quantum Computing Via Magnanelli 2 Bologna, Italy
⁵ INFN, Sezione di Trieste Via Valerio 2 34127 Trieste TS, Italy
⁶ Université Clermont Auvergne, CNRS/IN2P3, LPCA F-63000 Clermont-Ferrand, France
⁷ Department of Physics, Università di Genova Via Dodecaneso 33 16146 Genova, Italy
⁸ Istituto Nazionale di Fisica Nucleare, Sezione di Genova Via Dodecaneso 33 16146 Genova, Italy
⁹ INAF-Osservatorio Astronomico di Brera Via Brera 28 20122 Milano, Italy
¹⁰ Department of Astrophysical Sciences, Peyton Hall, Princeton University Princeton NJ 08544, USA
¹¹ Université Claude Bernard Lyon 1, IUF, IP2I Lyon 4 rue Enrico Fermi 69622 Villeurbanne, France

^★ Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 18 September 2025
Accepted: 15 December 2025

Abstract

The void-galaxy cross-correlation function is a powerful tool to extract cosmological information. Through the void-galaxy cross-correlation function, cosmic voids – the underdense regions in the galaxy distribution – are used for refined deductions of the Universe’s content by correcting apparent geometric distortions. This study proposes a novel procedure for optimally extracting the Alcock-Paczyǹski (AP) signal from cosmic voids through a cosmological reconstruction technique. Employing cosmological reconstruction, specifically using the Zel’dovich approximation, we estimate the true positions of galaxies from their redshift-space locations, reducing distortions introduced by peculiar velocities. Unlike previous analyses, we identify voids and measure the void-galaxy cross-correlation function directly in reconstructed space. This approach enables us, for the first time, to include in our analysis small nonlinear voids, typically discarded in previous studies, thus enhancing the statistical power of void studies and significantly improving their cosmological constraining power. Reconstruction is particularly effective even at small scales for voids, due to their clean and dynamically simple environment. This ability to recover information encoded on small scales significantly enhances the precision of the analysis, leading to a ∼23% improvement in the constraints on the AP parameters compared to previous methods where the analysis is performed in redshift space and, consequently, to a better estimate of the derived cosmological parameters. Our analysis also includes a comprehensive set of consistency checks, demonstrating its robustness. We expect this methodology to yield a substantial gain in constraining power when applied to data from modern large-scale structure surveys.