Reconstructing the Baryonic Acoustic Oscillations in the presence of photo-$z$ uncertainties

TL;DR

This paper develops a method to reconstruct transverse BAO signals from photometric survey data, accounting for photo-$z$ uncertainties, and demonstrates its effectiveness through simulations, improving BAO signal strength and correlation.

Contribution

The paper introduces a formalism for BAO reconstruction in photometric data using a 2D potential approach, adapting Zel'dovich reconstruction to handle photo-$z$ uncertainties.

Findings

Transverse BAO reconstruction enhances signal strength in dense samples.

Cross correlation with initial conditions increases by 20-40%.

Good agreement between 2D potential and 3D Poisson solutions.

Abstract

The reconstruction method has been widely employed to improve the Baryon Acoustic Oscillations (BAO) measurement in spectroscopic survey data analysis. In this study, we explore the reconstruction of the BAO signals in the realm of photometric data. By adapting the Zel'dovich reconstruction technique, we develop a formalism to reconstruct the transverse BAO in the presence of photo-$z$ uncertainties \change{under the plane-parallel approximation}. We access the performance of the BAO reconstruction through comoving $N$-body simulations. The transverse reconstruction potential can be derived by solving a 2D potential equation, with the surface density and the radial potential contribution acting as the source terms. The solution is predominantly determined by the surface density. As is evident in dense samples, such as the matter field, the transverse BAO reconstruction can enhance both the strength of the BAO signals and their cross correlation with the initial conditions. At $z=0$, the cross correlation is increased by a factor of 1.2 at $ k_\perp = 0.2 \, \mathrm{Mpc}^{-1}h $ and 1.4 at $ k_\perp = 0.3 \, \mathrm{Mpc}^{-1}h $, respectively. We contrast the 2D potential results with the 3D Poisson equation solution, wherein we directly solve the potential equation using the position in photo-$z$ space, and find good agreement. Additionally, we examine the impact of various conditions, such as the smoothing scales and the level of photo-$z$ uncertainties, on the reconstruction results. We envision the straightforward application of this method to survey data.