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ABSTRACT 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. 

ABSTRACT 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. 

ABSTRACT We forecast the prospects for cross-correlating future line intensity mapping (LIM) surveys with the current and future Ly-α forest measurements. Using large cosmological hydrodynamic simulations, we model the emission from the CO rotational transition in the CO Mapping Array Project LIM experiment at the 5-yr benchmark and the Ly-α forest absorption signal for extended Baryon Acoustic Oscillations (BOSS), Dark energy survey instrument (DESI), and Prime Focus multiplex Spectroscopy survey (PFS). We show that CO × Ly-α forest significantly enhances the detection signal-to-noise ratio (S/N) of CO, with up to $$300{{\ \rm per\, cent}}$$ improvement when correlated with the PFS Ly-α forest survey and a 50–75 per cent enhancement with the available eBOSS or the upcoming DESI observations. This is competitive with even CO × spectroscopic galaxy surveys. Furthermore, our study suggests that the clustering of CO emission is tightly constrained by CO × Ly-α forest due to the increased sensitivity and the simplicity of Ly-α absorption modelling. Foreground contamination or systematics are expected not to be shared between LIM and Ly-α forest observations, providing an unbiased inference. Ly-α forest will aid in detecting the first LIM signals. We also estimate that [C ii] × Ly-α forest measurements from Experiment for Cryogenic Large-Aperture Intensity Mapping and DESI/eBOSS should have a larger S/N than planned [C ii] × quasar observations by about an order of magnitude. 

ABSTRACT 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. 

Abstract We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets that extend the model parameter space based on the previous frameworks of CAMELS-TNG and CAMELS-SIMBA, to provide broader training sets and testing grounds for machine-learning algorithms designed for cosmological studies. CAMELS-ASTRID employs the galaxy formation model following the ASTRID simulation and contains 2124 hydrodynamic simulation runs that vary three cosmological parameters (Ω_m,σ₈, Ω_b) and four parameters controlling stellar and active galactic nucleus (AGN) feedback. Compared to the existing TNG and SIMBA simulation suites in CAMELS, the fiducial model of ASTRID features the mildest AGN feedback and predicts the least baryonic effect on the matter power spectrum. The training set of ASTRID covers a broader variation in the galaxy populations and the baryonic impact on the matter power spectrum compared to its TNG and SIMBA counterparts, which can make machine-learning models trained on the ASTRID suite exhibit better extrapolation performance when tested on other hydrodynamic simulation sets. We also introduce extension simulation sets in CAMELS that widely explore 28 parameters in the TNG and SIMBA models, demonstrating the enormity of the overall galaxy formation model parameter space and the complex nonlinear interplay between cosmology and astrophysical processes. With the new simulation suites, we show that building robust machine-learning models favors training and testing on the largest possible diversity of galaxy formation models. We also demonstrate that it is possible to train accurate neural networks to infer cosmological parameters using the high-dimensional TNG-SB28 simulation set. 

ABSTRACT We introduce the Astrid  simulation, a large-scale cosmological hydrodynamic simulation in a $$250 \, h^{-1}\mathrm{Mpc}$$ box with 2 × 55003 particles. Astrid contains a large number of high redshift galaxies, which can be compared to future survey data, and resolves galaxies in haloes more massive than $$2\times 10^9 \, \mathrm{M}_{\odot }$$. Astrid  has been run from z = 99 to 3. As a particular focus is modelling the high redshift Universe, it contains models for inhomogeneous hydrogen and helium reionization, baryon relative velocities and massive neutrinos, as well as supernova and AGN feedback. The black hole model includes mergers driven by dynamical friction rather than repositioning. We briefly summarize the implemented models, and the technical choices we took when developing the simulation code. We validate the model, showing good agreement with observed ultraviolet luminosity functions, galaxy stellar mass functions and specific star formation rates (SFRs). We show that the redshift at which a given galaxy underwent hydrogen reionization has a large effect on the halo gas fraction. Finally, at z = 6, haloes with $$M \sim 2\times 10^9 \, \mathrm{M}_{\odot }$$ which have been reionized have an SFR 1.5 times greater than those which have not yet been reionized. 

ABSTRACT We explore the use of Deep Learning to infer physical quantities from the observable transmitted flux in the Ly α forest. We train a Neural Network using redshift z = 3 outputs from cosmological hydrodynamic simulations and mock data sets constructed from them. We evaluate how well the trained network is able to reconstruct the optical depth for Ly α forest absorption from noisy and often saturated transmitted flux data. The Neural Network outperforms an alternative reconstruction method involving log inversion and spline interpolation by approximately a factor of 2 in the optical depth root mean square error. We find no significant dependence in the improvement on input data signal to noise, although the gain is greatest in high optical depth regions. The Ly α forest optical depth studied here serves as a simple, one dimensional, example but the use of Deep Learning and simulations to approach the inverse problem in cosmology could be extended to other physical quantities and higher dimensional data. 

ABSTRACT We examine massive black hole (MBH) mergers and their associated gravitational wave signals from the large-volume cosmological simulation Astrid . Astrid includes galaxy formation and black hole models recently updated with an MBH seed population between 3 × 104h−1M⊙ and 3 × 105h−1M⊙ and a sub-grid dynamical friction (DF) model to follow the MBH dynamics down to 1.5 ckpc h−1. We calculate the initial eccentricities of MBH orbits directly from the simulation at kpc-scales, and find orbital eccentricities above 0.7 for most MBH pairs before the numerical merger. After approximating unresolved evolution on scales below $${\sim 200\, \text{pc}}$$, we find that the in-simulation DF on large scales accounts for more than half of the total orbital decay time ($$\sim 500\, \text{Myr}$$) due to DF. The binary hardening time is an order of magnitude longer than the DF time, especially for the seed-mass binaries (MBH < 2Mseed). As a result, only $$\lesssim 20{{\rm per \,cent}}$$ of seed MBH pairs merge at z > 3 after considering both unresolved DF evolution and binary hardening. These z > 3 seed-mass mergers are hosted in a biased population of galaxies with the highest stellar masses of $$\gt 10^9\, {\rm M}_\odot$$. With the higher initial eccentricity prediction from Astrid , we estimate an expected merger rate of 0.3−0.7 per year from the z > 3 MBH population. This is a factor of ∼7 higher than the prediction using the circular orbit assumption. The Laser Interferometer Space Antenna events are expected at a similar rate, and comprise $$\gtrsim 60\,{\rm{per\,cent}}$$ seed-seed mergers, $$\sim 30\,{\rm{per\,cent}}$$ involving only one seed-mass MBH, and $$\sim 10\,{\rm{per\,cent}}$$ mergers of non-seed MBHs. 

ABSTRACT In this work, we expand and test the capabilities of our recently developed superresolution (SR) model to generate high-resolution (HR) realizations of the full phase-space matter distribution, including both displacement and velocity, from computationally cheap low-resolution (LR) cosmological N-body simulations. The SR model enhances the simulation resolution by generating 512 times more tracer particles, extending into the deeply nonlinear regime where complex structure formation processes take place. We validate the SR model by deploying the model in 10 test simulations of box size 100 h−1 Mpc, and examine the matter power spectra, bispectra, and two-dimensional power spectra in redshift space. We find the generated SR field matches the true HR result at per cent level down to scales of k ∼ 10 h  Mpc−1. We also identify and inspect dark matter haloes and their substructures. Our SR model generates visually authentic small-scale structures that cannot be resolved by the LR input, and are in good statistical agreement with the real HR results. The SR model performs satisfactorily on the halo occupation distribution, halo correlations in both real and redshift space, and the pairwise velocity distribution, matching the HR results with comparable scatter, thus demonstrating its potential in making mock halo catalogues. The SR technique can be a powerful and promising tool for modelling small-scale galaxy formation physics in large cosmological volumes. 

ABSTRACT 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.