The low redshift Lyman-$α$ Forest as a constraint for models of AGN feedback

Burkhart, Blakesley; Tillman, Megan; Gurvich, Alexander B.; Bird, Simeon; Tonnesen, Stephanie; Bryan, Greg L.; Hernquist, Lars E.; Somerville, Rachel S.

doi:10.48550/arxiv.2204.09712

Cited by 1 publication

(1 citation statement)

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“…We aim to investigate this problem by applying our inference method to observational data in future works, which we expect would provide an accurate measurement of the thermal state of the low-𝑧 IGM and possibly solve this discrepancy. In addition, we also look forward to applying our method to other recent cosmological galaxy formation simulations like Illustris (TNG) (Genel et al 2014;Weinberger et al 2017), to study the effect of feedback on the Ly𝛼 forest which is not yet completely understood (see for e.g, Gurvich et al 2017;Christiansen et al 2020;Burkhart et al 2022).…”

Section: Discussionmentioning

confidence: 99%

Measuring the thermal and ionization state of the low-z IGM using likelihood free inference

Teng

Khaire

Walther

et al. 2022

Monthly Notices of the Royal Astronomical Society

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We present a new approach to measure the power-law temperature density relationship $T=T_0 (\rho \ / \bar{\rho })^{\gamma -1}$ and the UV background photoionization rate $\Gamma _{{{{\rm H\, {\small i}}}}{}}$ of the intergalactic medium(IGM) based on the Voigt profile decomposition of the Lyα forest into a set of discrete absorption lines with Doppler parameter b and the neutral hydrogen column density $N_{\rm H\, {\small i}}$. Previous work demonstrated that the shape of the $b-N_{{{{\rm H\, {\small i}}}}{}}$ distribution is sensitive to the IGM thermal parameters T0 and γ, whereas our new inference algorithm also takes into account the normalization of the distribution, i.e. the line-density dN/dz, and we demonstrate that precise constraints can also be obtained on $\Gamma _{{{{\rm H\, {\small i}}}}{}}$. We use density-estimation likelihood-free inference (DELFI) to emulate the dependence of the $b-N_{{{{\rm H\, {\small i}}}}{}}$ distribution on IGM parameters trained on an ensemble of 624 Nyx hydrodynamical simulations at z = 0.1, which we combine with a Gaussian process emulator of the normalization. To demonstrate the efficacy of this approach, we generate hundreds of realizations of realistic mock HST/COS datasets, each comprising 34 quasar sightlines, and forward model the noise and resolution to match the real data. We use this large ensemble of mocks to extensively test our inference and empirically demonstrate that our posterior distributions are robust. Our analysis shows that by applying our new approach to existing Lyα forest spectra at z ≃ 0.1, one can measure the thermal and ionization state of the intergalactic medium(IGM) with very high precision ($\sigma _{\log T_0} \sim 0.08$ dex, σγ ∼ 0.06, and $\sigma _{\log \Gamma _{{{{\rm H\, {\small i}}}}{}}} \sim 0.07$ dex).

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