A new analytical formalism self-consistently predicts both the ionized fraction x_i(z) and photoionization rate Gamma_HI(z), achieving percent-level accuracy in x_i and 20-30% accuracy in Gamma_HI versus radiative transfer simulations at z less than or equal to 6.
Inferring the IGM thermal history during reionisation with the Lyman-$\alpha$ forest power spectrum at redshift $z \simeq 5$
3 Pith papers cite this work. Polarity classification is still indexing.
abstract
We use cosmological hydrodynamical simulations to assess the feasibility of constraining the thermal history of the intergalactic medium during reionisation with the Ly$\alpha$ forest at $z\simeq5$. The integrated thermal history has a measureable impact on the transmitted flux power spectrum that can be isolated from Doppler broadening at this redshift. We parameterise this using the cumulative energy per proton, $u_0$, deposited into a gas parcel at the mean background density, a quantity that is tightly linked with the gas density power spectrum in the simulations. We construct mock observations of the line of sight Ly$\alpha$ forest power spectrum and use a Markov Chain Monte Carlo approach to recover $u_{0}$ at redshifts $5 \leq z \leq 12$. A statistical uncertainty of $\sim 20$ per cent is expected (at 68 per cent confidence) at $z\simeq 5$ using high resolution spectra with a total redshift path length of $\Delta z=4$ and a typical signal-to-noise ratio of $\rm S/N=15$ per pixel. Estimates for the expected systematic uncertainties are comparable, such that existing data should enable a measurement of $u_0$ to within $\sim 30$ per cent. This translates to distinguishing between reionisation scenarios with similar instantaneous temperatures at $z\simeq 5$, but with an energy deposited per proton that differs by $2$-$3\, \rm eV$ over the redshift interval $5\leq z \leq 12$. For an initial temperature of $T\sim 10^{4}\rm\,K$ following reionisation, this corresponds to the difference between early ($z_{\rm re}=12$) and late ($z_{\rm re}=7$) reionisation in our models.
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astro-ph.CO 3years
2026 3roles
background 2polarities
background 2representative citing papers
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
citing papers explorer
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A self-consistent analytical model for both the photoionization rate and reionization history
A new analytical formalism self-consistently predicts both the ionized fraction x_i(z) and photoionization rate Gamma_HI(z), achieving percent-level accuracy in x_i and 20-30% accuracy in Gamma_HI versus radiative transfer simulations at z less than or equal to 6.
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Machine Learning Techniques for Astrophysics and Cosmology: Lyman-$\alpha$ forest
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
- Lyman-$\alpha$ forest constraints on pure and mixed fuzzy dark matter