Weak Lensing the non-Linear Ly-alpha Forest

TL;DR

This study assesses the effectiveness of a weak lensing estimator on realistic Ly-alpha forest data from hydrodynamic simulations, revealing impacts of non-linearity and non-Gaussianity on signal quality and bias, and proposing mitigation techniques.

Contribution

It evaluates the performance of a weak lensing estimator on realistic Ly-alpha forest data, highlighting the effects of non-linearity and non-Gaussianity, and introduces mitigation methods.

Findings

Estimator successfully reconstructs lensing potentials from realistic data.

Non-linearity reduces signal-to-noise ratio by ~2.7 for noise-free data.

Ray-traced potentials further decrease signal-to-noise by ~1.3.

Abstract

We evaluate the performance of the Lyman-$\alpha$ forest weak gravitational lensing estimator of Metcalf et al. on forest data from hydrodynamic simulations and ray-traced simulated lensing potentials. We compare the results to those obtained from the Gaussian random field simulated Ly$\alpha$ forest data and lensing potentials used in previous work. We find that the estimator is able to reconstruct the lensing potentials from the more realistic data, and investigate dependence on spectrum signal to noise. The non-linearity and non-Gaussianity in this forest data arising from gravitational instability and hydrodynamics causes a reduction in signal to noise by a factor of $\sim2.7$ for noise free data and a factor of $\sim 1.5$ for spectra with signal to noise of order unity (comparable to current observational data). Compared to Gaussian field lensing potentials, using ray-traced potentials from N-body simulations incurs a further signal to noise reduction of a factor of $\sim1.3$ at all noise levels. The non-linearity in the forest data is also observed to increase bias in the reconstructed potentials by $5-25\%$, and the ray-traced lensing potential further increases the bias by $20-30\%$. We demonstrate methods for mitigating these issues including Gaussianization and bias correction which could be used in real observations.