The Alcock-Paczyński effect from Lyman-α forest correlations: Analysis validation with synthetic data

Andrei Cuceu, Andreu Font-Ribera, Paul Martini, Benjamin Joachimi, Seshadri Nadathur, James Rich, Alma X. González-Morales, Hélion du Mas des Bourboux, James Farr

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Abstract

The three-dimensional distribution of the Lyα forest has been extensively used to constrain cosmology through measurements of the baryon acoustic oscillations (BAO) scale. However, more cosmological information could be extracted from the full shapes of the Lyα forest correlations through the Alcock-Paczynski (AP) effect. In this work, we prepare for a cosmological analysis of the full shape of the Lyα forest correlations by studying synthetic data of the extended Baryon Oscillation Spectroscopic Survey (eBOSS). We use a set of one hundred eBOSS synthetic data sets in order to validate such an analysis. These mocks undergo the same analysis process as the real data. We perform a full-shape analysis on the mean of the correlation functions measured from the one hundred eBOSS realizations, and find that our model of the Lyα correlations performs well on current data sets. We show that we are able to obtain an unbiased full-shape measurement of DM/DH(zeff), where DM is the transverse comoving distance, DH is the Hubble distance, and zeff is the effective redshift of the measurement. We test the fit over a range of scales, and decide to use a minimum separation of rmin=25 h-1 Mpc. We also study and discuss the impact of the main contaminants affecting Lyα forest correlations, and give recommendations on how to perform such analysis with real data. While the final eBOSS Lyα BAO analysis measured DM/DH(zeff=2.33) with 4% statistical precision, a full-shape fit of the same correlations could provide a ~2% measurement.
Original languageEnglish
Pages (from-to)3773–3790
JournalMonthly Notices of the Royal Astronomical Society
Volume523
Issue number3
Early online date14 Jun 2023
DOIs
Publication statusPublished - 1 Aug 2023

Keywords

  • astro-ph.CO
  • large-scale structure
  • distance scale
  • methods: data analysis
  • UKRI
  • STFC
  • ST/T005009/2

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