Forward vs Backward: Improving BAO Constraints with Field-Level Inference

TL;DR

This paper introduces a field-level inference method for BAO scale measurement that improves constraints by explicitly sampling initial conditions, surpassing standard reconstruction techniques.

Contribution

It presents a novel EFT-based forward modeling approach for BAO inference that enhances precision over traditional methods.

Findings

Field-level inference improves BAO scale constraints by 20-40%.

Explicit initial condition sampling adds significant information.

Method outperforms standard BAO reconstruction on the same data.

Abstract

We present results of field-level inference of the baryon acoustic oscillation (BAO) scale $r_s$ on rest-frame dark matter halo catalogs. Our field-level constraint on $r_s$ is obtained by explicitly sampling the initial conditions along with the bias and noise parameters via the LEFTfield EFT-based forward model. Comparing with a standard reconstruction pipeline applied to the same data and over the same scales, the field-level constraint on the BAO scale improves by a factor of $\sim 1.2-1.4$ over standard BAO reconstruction. We point to a surprisingly simple source of the additional information.