Reconstructing large-scale temperature profiles around z ∼ 6 quasars

High-redshift quasars ionize He II into He III around them, heating the intergalactic medium 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 approximate to 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. This shows that with noiseless spectra, one could use a simple CNN to distinguish gas inside or outside the He III region created by the quasar. Because the size of the He III region is closely related to the total quasar lifetime, this method has great potential in constraining the quasar lifetime on similar to Myr time-scales. However, noise poses a big problem for accuracy and could downgrade the accuracy to approximate to 2340 K even for very high signal-to-noise (greater than or similar to 50) spectra. Future studies are needed to reduce the error associated with noise to constrain the lifetimes of reionization epoch quasars with currently available data.