Forecast Cosmological Constraints with the 1D Wavelet Scattering Transform and the Lyman-$\alpha$ forest

TL;DR

This paper forecasts that the 1D Wavelet Scattering Transform significantly enhances cosmological parameter constraints from Lyman-$eta$ forest data, surpassing traditional power spectrum methods by over an order of magnitude.

Contribution

It introduces the use of the 1D Wavelet Scattering Transform for Lyman-$eta$ forest analysis, demonstrating its potential to extract non-Gaussian information and improve cosmological constraints.

Findings

WST coefficients contain additional information beyond the flux power spectrum.

Forecasts show over tenfold improvement in parameter constraints using WST.

Potential for DESI to tightly constrain inflationary parameters and neutrino mass.

Abstract

We make forecasts for the constraining power of the 1D Wavelet Scattering Transform (WST) when used with a Lyman-$\alpha$ forest cosmology survey. Using mock simulations and a Fisher matrix, we show that there is considerable cosmological information in the scattering transform coefficients not captured by the flux power spectrum. We estimate mock covariance matrices assuming uncorrelated Gaussian pixel noise for each quasar, at a level drawn from a simple lognormal model. The extra information comes from a smaller estimated covariance in the first-order wavelet power, and from second-order wavelet coefficients which probe non-Gaussian information in the forest. Forecast constraints on cosmological parameters from the WST are more than an order of magnitude tighter than for the power spectrum, shrinking a $4D$ parameter space by a factor of $10^6$. Should these improvements be realised with DESI, inflationary running would be constrained to test common inflationary models predicting $\alpha_s = - 6\times 10^{-4}$ and neutrino mass constraints would be improved enough for a $5-\sigma$ detection of the minimal neutrino mass.