The Effects of AGN Feedback on the Lyman-$\alpha$ Forest Flux Power Spectrum

TL;DR

This study investigates how AGN feedback, especially jet feedback, influences the Lyman-alpha forest flux power spectrum across different redshifts using Simba simulations, revealing significant effects at low redshift and minor impacts at high redshift.

Contribution

It demonstrates the impact of AGN feedback modes on the Lyman-alpha forest flux power spectrum, highlighting the importance of AGN jets in cosmological simulations.

Findings

AGN feedback significantly affects P1D at z<1

AGN jets alter the IGM's thermal state and hydrogen ionization

Effects are minimal at high redshift (z>2) after normalization

Abstract

We study the effects of AGN feedback on the Lyman-$\alpha$ forest 1D flux power spectrum (P1D). Using the Simba cosmological-hydrodynamic simulations, we examine the impact that adding different AGN feedback modes has on the predicted P1D. We find that, for Simba, the impact of AGN feedback is most dramatic at lower redshifts ($z<1$) and that AGN jet feedback plays the most significant role in altering the P1D. The effects of AGN feedback can be seen across a large range of wavenumbers ($1.5\times10^{-3}<k<10^{-1}$ s/km) changing the ionization state of hydrogen in the IGM through heating. AGN feedback can also alter the thermal evolution of the IGM and thermally broaden individual Lyman-$\alpha$ absorbers. For the Simba model, these effects become observable at $z \lesssim 1.0$. At higher redshifts ($z>2.0$), AGN feedback has a $2\%$ effect on the P1D for $k<5\times10^{-2}$ s/km and an $8\%$ effect for $k>5\times10^{-2}$ s/km. We show that the small scale effect is reduced when normalizing the simulation to the observed mean flux. On large scales, the effect of AGN feedback appears via a change in the IGM temperature and is thus unlikely to bias cosmological parameters. The strong AGN jets in the Simba simulation can reproduce the $z>2$ Lyman-$\alpha$ forest. We stress that analyses comparing different AGN feedback models to future higher precision data will be necessary to determine the full extent of this effect.