Improving IGM temperature constraints using wavelet analysis on high-redshift quasars

TL;DR

This paper introduces a new wavelet-based statistical method to analyze high-redshift quasar spectra, improving constraints on the intergalactic medium's temperature during reionization compared to traditional power spectrum techniques.

Contribution

The authors develop a comprehensive wavelet amplitude PDF analysis that accounts for correlations across all spatial frequencies, enhancing the sensitivity of IGM temperature measurements.

Findings

Wavelet analysis yields 7-12% more sensitive temperature constraints.

The new method fully incorporates correlations across wavelet scales.

Combining wavelet PDFs with the power spectrum does not improve sensitivity.

Abstract

The thermal state of the intergalactic medium (IGM) contains vital information about the epoch of reionization, one of the most transformative yet poorly understood periods in the young universe. This thermal state is encoded in the small-scale structure of Lyman-$\alpha$ (Ly$\alpha$) absorption in quasar spectra. The 1D flux power spectrum measures the average small-scale structure along quasar sightlines. At high redshifts, where the opacity is large, averaging mixes high signal-to-noise ratio transmission spikes with noisy absorption troughs. Wavelet amplitudes are an alternate statistic that maintains spatial information while quantifying fluctuations at the same spatial frequencies as the power spectrum, giving them the potential to more sensitively measure the small-scale structure. Previous Ly$\alpha$ forest studies using wavelet amplitude probability density functions (PDFs) used limited spatial frequencies and neglected strong correlations between PDF bins and across wavelets scales, resulting in sub-optimal and unreliable parameter inference. Here we present a novel method for performing statistical inference using wavelet amplitude PDFs that spans the full range of spatial frequencies probed by the power spectrum and that fully accounts for these correlations. We applied this procedure to realistic mock data drawn from a simple thermal model parameterized by the temperature at mean density, $T_0$, and find that wavelets deliver 1$\sigma$ constraints on $T_0$ that are on average 7% more sensitive at $z=5$ (12% at $z=6$) than those from the power spectrum. We consider the possibility of combing wavelet PDFs with the power, but find that this does not lead to improved sensitivity.