Forecasting constraints on the high-<i>z</i> IGM thermal state from the Lyman-α forest flux autocorrelation function

ABSTRACT The autocorrelation function of the Lyman-$\alpha$ (Ly $\alpha$) forest flux from high-z quasars probes the small-scale structure of the intergalactic medium (IGM). The thermal state of the IGM, determined by the physics of reionization, sets the small-scale power observed in the Ly $\alpha$ forest. To explore the sensitivity of the autocorrelation function to the IGM’s thermal state, we compute the autocorrelation function from a cosmological hydrodynamical simulation with an instantaneous reionization model and 135 post-processed thermal states. Using mock data sets of 20 quasars, we forecast constraints on $T_0$ and $\gamma$, which characterize the post-processed IGM thermal state, at $5.4 \le z \le 6$. While this model simplifies the IGM’s thermal state, it serves as a key first step in assessing future observational prospects. We also perform an inference test on mocks and re-weight out posterior distributions to guarantee that they exhibit statistically correct behaviour. At $z = 5.4$, we find that an idealized data set constrains $T_0$ to 59 per cent and $\gamma$ to 16 per cent at the 1$\sigma$ equivalent confidence level. To explore more realistic, non-instantaneous reionization scenarios, we analyse four models combining temperature and ultraviolet background (UVB) fluctuations at $z = 5.8$. We find that mock data generated from a model with both temperature and UVB fluctuations can rule out a model with only temperature fluctuations at the $&amp;gt; 1\sigma$ level 73.9 per cent of the time.

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Journal
Monthly Notices of the Royal Astronomical Society
Published
2025-05-08
DOI
https://doi.org/10.1093/mnras/staf753
Primary Topic
Statistical and numerical algorithms
Type
article
Field-Weighted Citation Impact
0.00

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article

Forecasting constraints on the high-z IGM thermal state from the Lyman-α forest flux autocorrelation function

Molly Wolfson, Joseph F. Hennawi, Zarija Lukić, Frederick B. Davies et al.
1 links
Monthly Notices of the Royal Astronomical Society
Statistical and numerical algorithms
article

Forecasting constraints on the high-z IGM thermal state from the Lyman-α forest flux autocorrelation function

Molly Wolfson, Joseph F. Hennawi, Zarija Lukić, Frederick B. Davies, José Oñorbe
article en

Abstract

ABSTRACT The autocorrelation function of the Lyman-$\alpha$ (Ly $\alpha$) forest flux from high-z quasars probes the small-scale structure of the intergalactic medium (IGM). The thermal state of the IGM, determined by the physics of reionization, sets the small-scale power observed in the Ly $\alpha$ forest. To explore the sensitivity of the autocorrelation function to the IGM’s thermal state, we compute the autocorrelation function from a cosmological hydrodynamical simulation with an instantaneous reionization model and 135 post-processed thermal states. Using mock data sets of 20 quasars, we forecast constraints on $T_0$ and $\gamma$, which characterize the post-processed IGM thermal state, at $5.4 \le z \le 6$. While this model simplifies the IGM’s thermal state, it serves as a key first step in assessing future observational prospects. We also perform an inference test on mocks and re-weight out posterior distributions to guarantee that they exhibit statistically correct behaviour. At $z = 5.4$, we find that an idealized data set constrains $T_0$ to 59 per cent and $\gamma$ to 16 per cent at the 1$\sigma$ equivalent confidence level. To explore more realistic, non-instantaneous reionization scenarios, we analyse four models combining temperature and ultraviolet background (UVB) fluctuations at $z = 5.8$. We find that mock data generated from a model with both temperature and UVB fluctuations can rule out a model with only temperature fluctuations at the $&amp;gt; 1\sigma$ level 73.9 per cent of the time.

Monthly Notices of the Royal Astronomical SocietyVol. 540(2)
Leiden University (NL), Lawrence Berkeley National Laboratory (US), University of California, Santa Barbara (US), Max Planck Institute for Astronomy (DE), Universidad de Sevilla (ES)
National Science Foundation, European Commission, Universiteit Leiden, Junta de Andalucía, University of California, Santa Barbara, Agencia Estatal de Investigación
Openalex Percentile: Top 86%
Statistical and numerical algorithms
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