Weak lensing the non-linear Lyα forest

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-trace 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 similar to 2.7 for noise free data and a factor of similar to 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 similar to 1.3 at all noise levels. The non-linearity in the forest data is also observed to increase bias in the reconstructed potentials by 5 - 25 per cent, and the ray-traced lensing potential further increases the bias by 20 - 30 per cent. We demonstrate methods for mitigating these issues including Gaussianization and bias correction which could be used in real observations.