Sample variance and Lyman-alpha forest transmission statistics

TL;DR

This study compares observed Lyman-alpha forest transmission statistics with cosmological simulations, finding good agreement and highlighting the importance of accurately estimating sample variance.

Contribution

It demonstrates that bootstrap and jack-knife methods often underestimate variance and uses simulations to better estimate it, confirming the consistency between observations and models.

Findings

Observed and simulated transmission PDFs agree well.

Bootstrap and jack-knife underestimate sample variance.

No need for inverted temperature-density relation.

Abstract

We compare the observed probability distribution function of the transmission in the \HI\ Lyman-alpha forest, measured from the UVES 'Large Programme' sample at redshifts z=[2,2.5,3], to results from the GIMIC cosmological simulations. Our measured values for the mean transmission and its PDF are in good agreement with published results. Errors on statistics measured from high-resolution data are typically estimated using bootstrap or jack-knife resampling techniques after splitting the spectra into chunks. We demonstrate that these methods tend to underestimate the sample variance unless the chunk size is much larger than is commonly the case. We therefore estimate the sample variance from the simulations. We conclude that observed and simulated transmission statistics are in good agreement, in particular, we do not require the temperature-density relation to be 'inverted'.