Analysis of methods for detecting the proximity effect in quasar spectra

TL;DR

This study evaluates various methods for measuring the proximity effect in quasar spectra using high-resolution simulations, confirming the reliability of the simple averaging technique over more complex likelihood-based approaches.

Contribution

The paper introduces and compares three new likelihood-based methods for estimating the proximity effect, demonstrating their limitations compared to the simple averaging technique.

Findings

Simple averaging accurately recovers the proximity effect strength.

New likelihood methods do not unbiasedly recover the input strength.

Biases in standard techniques are confirmed and analyzed.

Abstract

Using numerical simulations of structure formation, we investigate multiple methods of determining the strength of the proximity effect in the HI Lyalpha forest. We analyze three high resolution (~10kpc) redshift snapshots (z=4,3,2.25) of a Hydro-Particle-Mesh simulation to obtain realistic absorption spectra of the HI Lyalpha forest. We begin our analysis investigating the intrinsic biases thought to arise in the widely adopted standard technique of combining multiple lines of sight when searching for the proximity effect. We confirm the existence of this biases. We then concentrate on the analysis of the proximity effect along individual lines of sight. We construct the proximity effect strength distribution (PESD) and confirm that the PESD inferred from a simple averaging technique accurately recovers the input strength of the proximity effect at all redshifts. Moreover, the PESD closely follows the behaviors found in observed samples of quasar spectra. However, the PESD obtained from our new simulated sight lines presents some differences to that of simple Monte Carlo simulations. After developing three new theoretical methods of recovering the strength of the proximity effect on individual lines of sight, we compare their accuracy to the PESD from the simple averaging technique. All our new approaches are based on the maximization of the likelihood function, albeit invoking some modifications. The new techniques presented here fail to recover the input proximity effect in an un-biased way. Thus, employing complex 3D simulations, we provide strong evidence in favor of the proximity effect strength distribution obtained from the simple averaging technique, as method of estimating the UV background intensity, free of any biases.