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We study quasar proximity zones in a simulation that includes a self-consistent quasar formation model and realistic intergalactic medium (IGM) environments. The quasar host halo is 1013 M⊙ at z = 6, more massive than typical halos studied in previous work. Between 6 < z < 7.5, the quasar luminosity varies rapidly, with a mean magnitude of MUV, mean = −24.8 and the fluctuation reaching up to two orders of magnitude. Using this light curve to post-process the dense environment around the quasar, we find that the proximity zone size (Rp) ranges between 0.5 and 5 pMpc. We show that the light curve variability causes a similar degree of scatter in Rp as does the density fluctuation, both of which result in a standard deviation of ∼0.3 pMpc. The Rp traces the light curve fluctuations closely but with a time delay of ∼104 yr, breaking the correspondence between the Rp and the contemporaneous MUV. This also indicates that we can only infer quasar activity within the past ∼104 yr instead of the integrated lifetime from Rp in the later part of cosmic reionization. Compared with the variable light curve, a constant light curve underestimates the Rp by 13 per cent at the dim end (MUV ∼ −23.5), and overestimates the Rp by 30 per cent at the bright end (MUV ∼ −26). By calculating the Rp generated by a number of quasars, we show that variable light curves predict a wider Rp distribution than lightbulb models, and readily explain the extremely small Rp values that have been observed.

In this work, we extend our recently developed super-resolution (SR) model for cosmological simulations to produce fully time-consistent evolving representations of the particle phase-space distribution. We employ a style-based constrained generative adversarial network (StyleGAN), where the changing cosmic time is an input style parameter to the network. The matter power spectrum and halo mass function agree well with results from high-resolution N-body simulations over the full trained redshift range (10 ≤ z ≤ 0). Furthermore, we assess the temporal consistency of our SR model by constructing halo merger trees. We examine progenitors, descendants, and mass growth along the tree branches. All statistical indicators demonstrate the ability of our SR model to generate satisfactory high-resolution simulations based on low-resolution inputs.

We evaluate the performance of the Lyman α forest weak gravitational lensing estimator of Metcalf et al. on forest data from hydrodynamic simulations and ray-trace simulated lensing potentials. We compare the results to those obtained from the Gaussian random field simulated Lyα forest data and lensing potentials used in previous work. We find that the estimator is able to reconstruct the lensing potentials from the more realistic data and investigate dependence on spectrum signal to noise. The non-linearity and non-Gaussianity in this forest data arising from gravitational instability and hydrodynamics causes a reduction in signal to noise by a factor of ∼2.7 for noise free data and a factor of ∼1.5 for spectra with signal to noise of order unity (comparable to current observational data). Compared to Gaussian field lensing potentials, using ray-traced potentials from N-body simulations incurs a further signal-to-noise reduction of a factor of ∼1.3 at all noise levels. The non-linearity in the forest data is also observed to increase bias in the reconstructed potentials by $5-25{{\ \rm per\ cent}}$, and the ray-traced lensing potential further increases the bias by $20-30{{\ \rm per\ cent}}$. We demonstrate methods for mitigating these issues including Gaussianization and bias correction which could be used in real observations.

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 T0, 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 ≈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 ∼Myr time-scales. However, noise poses a big problem for accuracy and could downgrade the accuracy to ≈2340 K even for very high signal-to-noise (≳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.

We explore the use of deep learning to infer the temperature of the intergalactic medium from the transmitted flux in the high-redshift Ly α forest. We train neural networks on sets of simulated spectra from redshift z = 2–3 outputs of cosmological hydrodynamic simulations, including high-temperature regions added in post-processing to approximate bubbles heated by He ii reionization. We evaluate how well the trained networks are able to reconstruct the temperature from the effect of Doppler broadening in the simulated input Ly α forest absorption spectra. We find that for spectra with high resolution (10 $\, {\rm km}\, {\rm s}^{-1}$ pixel) and moderate signal-to-noise ratio (20–50), the neural network is able to reconstruct the intergalactic medium temperature smoothed on scales of $\sim 6 \, h^{-1}\, {\rm Mpc}$ quite well. Concentrating on discontinuities, we find that high-temperature regions of width $25 \, h^{-1}\, {\rm Mpc}$ and temperature $20\, 000$ K can be fairly easily detected and characterized. We show an example where multiple sightlines are combined to yield tomographic images of hot bubbles. Deep learning techniques may be useful in this way to help us understand the complex temperature structure of the intergalactic medium around the time of helium reionization.

We present a mock image catalogue of ∼100 000 MUV ≃ −22.5 to −19.6 mag galaxies at z = 7–12 from the bluetides cosmological simulation. We create mock images of each galaxy with the James Webb Space Telescope (JWST), Hubble, Roman, and Euclid Space Telescopes, as well as Subaru, and VISTA, with a range of near- and mid-infrared filters. We perform photometry on the mock images to estimate the success of these instruments for detecting high-z galaxies. We predict that JWST will have unprecedented power in detecting high-z galaxies, with a 95 per cent completeness limit at least 2.5 mag fainter than VISTA and Subaru, 1.1 mag fainter than Hubble, and 0.9 mag fainter than Roman, for the same wavelength and exposure time. Focusing on JWST, we consider a range of exposure times and filters, and find that the NIRCam F356W and F277W filters will detect the faintest galaxies, with 95 per cent completeness at m ≃ 27.4 mag in 10-ks exposures. We also predict the number of high-z galaxies that will be discovered by upcoming JWST imaging surveys. We predict that the COSMOS-Web survey will detect ∼1000 M1500 Å < −20.1 mag galaxies at 6.5 < z < 7.5, by virtue of its large survey area. JADES-Medium will detect almost $100{{\ \rm per\ cent}}$ of M1500 Å ≲ −20 mag galaxies at z < 8.5 due to its significant depth, however, with its smaller survey area it will detect only ∼100 of these galaxies at 6.5 < z < 7.5. Cosmic variance results in a large range in the number of predicted galaxies each survey will detect, which is more evident in smaller surveys such as CEERS and the PEARLS NEP and GOODS-S fields.

We study the sizes of galaxies in the Epoch of Reionization using a sample of ${\sim 100\, 000}$ galaxies from the BlueTides cosmological hydrodynamical simulation from z = 7 to 11. We measure the galaxy sizes from stellar mass and luminosity maps, defining the effective radius as the minimum radius that could enclose the pixels containing 50 per cent of the total mass/light in the image. We find an inverse relationship between stellar mass and effective half-mass radius, suggesting that the most massive galaxies are more compact and dense than lower mass galaxies, which have flatter mass distributions. We find a mildly negative relation between intrinsic far-ultraviolet luminosity and size, while we find a positive size–luminosity relation when measured from dust-attenuated images. This suggests that dust is the predominant cause of the observed positive size–luminosity relation, with dust preferentially attenuating bright sightlines resulting in a flatter emission profile and thus larger measured effective radii. We study the size–luminosity relation across the rest-frame ultraviolet and optical, and find that the slope decreases at longer wavelengths; this is a consequence of the relation being caused by dust, which produces less attenuation at longer wavelengths. We find that the far-ultraviolet size–luminosity relation shows mild evolution from z = 7 to 11, and galaxy size evolves with redshift as R ∝ (1 + z)−m, where m = 0.662 ± 0.009. Finally, we investigate the sizes of z = 7 quasar host galaxies, and find that while the intrinsic sizes of quasar hosts are small relative to the overall galaxy sample, they have comparable sizes when measured from dust-attenuated images.

Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in artificial intelligence (AI; specifically deep learning) to address this problem. Neural networks have been developed to learn from high-resolution (HR) image data and then make accurate superresolution (SR) versions of different low-resolution (LR) images. We apply such techniques to LR cosmological N-body simulations, generating SR versions. Specifically, we are able to enhance the simulation resolution by generating 512 times more particles and predicting their displacements from the initial positions. Therefore, our results can be viewed as simulation realizations themselves, rather than projections, e.g., to their density fields. Furthermore, the generation process is stochastic, enabling us to sample the small-scale modes conditioning on the large-scale environment. Our model learns from only 16 pairs of small-volume LR-HR simulations and is then able to generate SR simulations that successfully reproduce the HR matter power spectrum to percent level up to$16\hspace{0.17em}{h}^{-1}\mathrm{\text{Mpc}}$and the HR halo mass function to within$10\%$down to$10^{11}\hspace{0.17em}{M}_{\odot }$. We successfully deploy the model in a box 1,000 times larger than the training simulation box, showing that high-resolution mock surveys can be generated rapidly. We conclude that AI assistance has the potential to revolutionize modeling of small-scale galaxy-formation physics in large cosmological volumes.