Reconstructing Large-scale Temperature Profiles around $z\sim 6$ Quasars

TL;DR

This paper introduces a CNN-based method to reconstruct temperature profiles around high-redshift quasars, enabling insights into quasar lifetimes and the ionization state of the intergalactic medium.

Contribution

The study develops and tests a convolutional neural network trained on simulated spectra to accurately recover temperature profiles in quasar proximity zones.

Findings

CNN recovers temperature profiles with ~1400 K accuracy in ideal conditions.

Robustness against uncertainties in quasar properties and spectral hardness.

Potential to constrain quasar lifetimes via HeIII region size.

Abstract

High-redshift quasars ionize HeII into HeIII around them, heating the IGM in the process and creating large regions with elevated temperature. In this work, we demonstrate a method based on a convolutional neural network (CNN) to recover the spatial profile for $T_0$, the temperature at the mean cosmic density, in quasar proximity zones. We train the neural network with synthetic spectra drawn from a Cosmic Reionization on Computers simulation. We discover that the simple CNN is able to recover the temperature profile with an accuracy of $\approx 1400$ K in an idealized case of negligible observational uncertainties. We test the robustness of the CNN and discover that it is robust against the uncertainties in quasar host halo mass, quasar continuum and ionizing flux. We also find that the CNN has good generality with regard to the hardness of quasar spectra. Saturated pixels pose a bigger problem for accuracy and may downgrade the accuracy to $1700$ K in the outer parts of the proximity zones. Using our method, one could distinguish whether gas is inside or outside the HeIII region created by the quasar. Because the size of the HeIII region is closely related to the total quasar lifetime, this method has great potential in constraining the quasar lifetime on $\sim $Myr timescales.