Forecast Cosmological Constraints with the 1D Wavelet Scattering Transform and the Lyman-α Forest

Area of Science:

• Cosmology
• Astrophysics
• Data Analysis

Background:

• The Lyman-α forest is a key probe of large-scale structure and cosmological parameters.
• Traditional analyses often rely on the flux power spectrum, which may not capture all available information.

Purpose of the Study:

• To forecast the constraining power of the 1D wavelet scattering transform for Lyman-α forest cosmology surveys.
• To quantify the additional cosmological information provided by the wavelet scattering transform compared to the flux power spectrum.

Main Methods:

• Utilized mock simulations of the Lyman-α forest.
• Employed a Fisher matrix analysis to forecast parameter constraints.
• Estimated mock covariance matrices assuming specific noise models.

Main Results:

• The wavelet scattering transform coefficients contain substantial cosmological information not present in the flux power spectrum.
• Significant improvements were observed in the first-order wavelet power covariance and second-order coefficients probing non-Gaussianity.
• Forecast constraints on cosmological parameters were over an order of magnitude tighter than power spectrum methods, reducing a 4D parameter space by a factor of 10^6.

Conclusions:

• The wavelet scattering transform offers a powerful new tool for extracting cosmological information from the Lyman-α forest.
• Future instruments like the Dark Energy Spectroscopic Instrument could leverage this technique to achieve significant advances in constraining inflationary models and neutrino mass.