An Optimized Ly$\alpha$ Forest Inversion Tool Based on a Quantitative Comparison of Existing Reconstruction Methods

TL;DR

This paper compares leading Lyα forest inversion methods, introduces a hybrid approach with regularization, and demonstrates improved accuracy and robustness in reconstructing the matter density field from synthetic data.

Contribution

It provides a comprehensive comparison of existing inversion algorithms and develops a new regularized statistical method that enhances reconstruction accuracy with low complexity.

Findings

Gauss-Newton method is most accurate but complex and assumption-heavy.

Statistical approach is faster, robust, and performs well at low S/N.

The new regularized method outperforms previous approaches in accuracy at low S/N.

Abstract

We present a same-level comparison of the most prominent inversion methods for the reconstruction of the matter density field in the quasi-linear regime from the Ly$\alpha$ forest flux. Moreover, we present a pathway for refining the reconstruction in the framework of numerical optimization. We apply this approach to construct a novel hybrid method. The methods which are used so far for matter reconstructions are the Richardson-Lucy algorithm, an iterative Gauss-Newton method and a statistical approach assuming a one-to-one correspondence between matter and flux. We study these methods for high spectral resolutions such that thermal broadening becomes relevant. The inversion methods are compared on synthetic data (generated with the lognormal approach) with respect to their performance, accuracy, their stability against noise, and their robustness against systematic uncertainties. We conclude that the iterative Gauss-Newton method offers the most accurate reconstruction, in particular at small S/N, but has also the largest numerical complexity and requires the strongest assumptions. The other two algorithms are faster, comparably precise at small noise-levels, and, in the case of the statistical approach, more robust against inaccurate assumptions on the thermal history of the intergalactic medium (IGM). We use these results to refine the statistical approach using regularization. Our new approach has low numerical complexity and makes few assumptions about the history of the IGM, and is shown to be the most accurate reconstruction at small S/N, even if the thermal history of the IGM is not known. Our code will be made publicly available under https://github.com/hmuellergoe/reglyman.