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Open Access Publications from the University of California

A New Method to Measure the Post-reionization Ionizing Background from the Joint Distribution of Lyα and Lyβ Forest Transmission

  • Author(s): Davies, FB
  • Hennawi, JF
  • Eilers, AC
  • Lukić, Z
  • et al.

© 2018. The American Astronomical Society. All rights reserved. The amplitude of the ionizing background that pervades the intergalactic medium (IGM) at the end of the epoch of reionization provides a valuable constraint on the emissivity of the sources that reionized the universe. While measurements of the ionizing background at lower redshifts rely on a simulation-calibrated mapping between the photoionization rate and the mean transmission of the Lyα forest, at z 6 the IGM becomes increasingly opaque and transmission arises solely in narrow spikes separated by saturated Gunn-Peterson troughs. In this regime, the traditional approach of measuring the average transmission over large ∼50 Mpc/h regions is less sensitive and suboptimal. In addition, the five times smaller oscillator strength of the Lyβ transition implies that the Lyβ forest is considerably more transparent at z6, even in the presence of contamination by foreground z ∼ 5 Lyα forest absorption. In this work we present a novel statistical approach to analyze the joint distribution of transmission spikes in the cospatial z ∼ 6 Lyα and Lyβ forests. Our method relies on approximate Bayesian computation (ABC), which circumvents the necessity of computing the intractable likelihood function describing the highly correlated Lyα and Lyβ transmission. We apply ABC to mock data generated from a large-volume hydrodynamical simulation combined with a state-of-the-art model of ionizing background fluctuations in the post-reionization IGM and show that it is sensitive to higher IGM neutral hydrogen fractions than previous techniques. As a proof of concept, we apply this methodology to a real spectrum of a z = 6.54 quasar and measure the ionizing background from 5.4 ≤ z ≤ 6.4 along this sightline with ∼0.2 dex statistical uncertainties.

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