BAO reconstruction: a swift numerical action method for massive spectroscopic surveys

TL;DR

This paper introduces a new non-linear reconstruction algorithm based on the least-action principle, designed for massive spectroscopic surveys, which effectively recovers the BAO feature and improves measurement accuracy over linear methods.

Contribution

It presents a novel non-linear BAO reconstruction algorithm that extends the Fast Action Minimisation method, outperforming linear approaches in recovering the BAO scale in large survey data.

Findings

Successfully recovers BAO features in real and redshift space.

Reduces non-linear displacement parameter significantly after reconstruction.

Outperforms linear approximation in unbiased BAO scale measurement.

Abstract

A new fully non-linear reconstruction algorithm for the accurate recovery of the Baryonic Acoustic Oscillations (BAO) scale in two-point correlation functions is proposed, based on the least-action principle and extending the Fast Action Minimisation method by Nusser & Branchini (2000). Especially designed for massive spectroscopic surveys, it is tested on dark-matter halo catalogues extracted from the DEUS-FUR $\Lambda$CDM simulation to trace the trajectories of up to $\sim207,000$ haloes backward-in-time, well beyond the first-order Lagrangian approximation. The new algorithm successfully recovers the BAO feature in real and redshift-space in both the monopole and the anisotropic two-point correlation function, also for anomalous samples showing misplaced or absent signature of BAO. In redshift-space the non-linear displacement parameter $\Sigma_\mathrm{NL}$ is reduced from $11.8\pm0.3h^{-1}$Mpc at redshift $z=0$ (pre-reconstruction) to $4.0\pm0.5h^{-1}$Mpc at $z\simeq37$ after reconstruction. A comparison with the first-order Lagrangian reconstruction is presented, showing that this techniques outperforms the linear approximation in recovering an unbiased measurement of the acoustic scale.