English

Lyman-$\alpha$ forest power spectrum and its cross-correlation with dark matter halos in different astrophysical models

Cosmology and Nongalactic Astrophysics 2024-10-23 v1

Abstract

The Lyα\alpha forest, a series of HI absorption lines in the quasar spectra, is a powerful tool for probing the large-scale structure of the intergalactic medium. Its three-dimensional (3D) correlation and cross-correlations with quasars allow precise measurements of the baryon acoustic oscillation feature and redshift space distortions at redshifts z>2z>2. Understanding small-scale astrophysical phenomena, such as star formation and feedback, is crucial for full-shape analyses. In this study, we measure the 3D auto-power spectrum of the Lyα\alpha forest and its cross-power spectrum with halos using hydrodynamic simulations from the GADGET3-OSAKA code, which includes models for star formation and supernova feedback. Across five astrophysical models, we find significant deviations from the Fiducial model, with 510%5-10\,\% differences for wavenumbers k>2hMpc1k>2\,h\mathrm{Mpc}^{-1} in the Lyα\alpha auto-power spectrum. The Lyα×\alpha\,\times\,halo cross-power spectra show even larger deviations, exceeding 10%10\,\% in some cases. Using the fitting models of Arinyo-i-Prats et al. (2015) and Givans et al. (2022), we jointly fit the Lyα\alpha auto- and Lyα\alpha ×\times halo cross-power spectra, and assess the accuracy of the estimated fσ8f\sigma_8 parameter by comparing it with the ground truth from the simulations, while varying the maximum wavenumber kmaxk_\mathrm{max} and minimum halo mass MhM_h. Our results demonstrate that the extended model of Givans et al. (2022) is highly effective in reproducing fσ8f\sigma_8 at kmax3.0hMpc1k_\mathrm{max}\leq3.0\,h\mathrm{Mpc}^{-1} for Mh>1010.5MM_h>10^{10.5} M_\odot, and remains robust against astrophysical uncertainties.

Keywords

Cite

@article{arxiv.2410.16740,
  title  = {Lyman-$\alpha$ forest power spectrum and its cross-correlation with dark matter halos in different astrophysical models},
  author = {Koichiro Nakashima and Atsushi J. Nishizawa and Kentaro Nagamine and Yuri Oku and Ikkoh Shimizu},
  journal= {arXiv preprint arXiv:2410.16740},
  year   = {2024}
}

Comments

14 pages, 8 figures