Forecasting the Detection of Lyman-Alpha Forest Weak Lensing from the Dark Energy Spectroscopic Instrument and Other Future Surveys

TL;DR

This paper evaluates the potential of Lyman-alpha forest weak lensing detection using DESI survey data and forecasts future survey capabilities, demonstrating that significant detections are feasible with current and upcoming datasets.

Contribution

It extends previous methods to lower source densities, assesses DESI data performance, and forecasts the detectability of weak lensing signals in future spectral surveys.

Findings

A signal-to-noise ratio of ~4 is achievable with DESI data.

Low-density, high-volume surveys are promising for forest weak lensing detection.

Future larger datasets could achieve signal-to-noise >10.

Abstract

The apparent angular positions of quasars are deflected on the sky by the gravitational field sourced by foreground matter. This weak lensing effect is measurable through the distortions it introduces in the lensed quasar spectra. Discrepancies in the statistics of the Lyman-$\alpha$ forest spectral absorption features can be used to reconstruct the foreground lensing potential. We extend the study of this method of Lyman-$\alpha$ forest weak gravitational lensing to lower angular forest spectrum source densities than previous work. We evaluate the performance of the Lyman-$\alpha$ lensing estimator of Metcalf et al. (2020) on mock data based on the angular forest source density ($50$ per square degree) and volume ($\sim$700,000 spectra total) of the DESI survey. We simulate the foreground galaxy distribution and lensing potentials with redshift evolution approximated by N-body simulation and simulate Gaussian-random Lyman-$\alpha$ forests to produce mock data for the entire DESI footprint. By correlating the foreground galaxy distribution with the potential reconstructed by the estimator, we find that a weak lensing detection with signal to noise of $\sim4$ will be possible with the full DESI data. We show that spectral surveys with low density and high volume are promising candidates for forest weak lensing in addition to the high resolution data that have been considered in previous work. We present forecasts for future spectral surveys and show that with larger datasets a detection with signal to noise $>10$ will be possible.