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ABSTRACT Feedback from active galactic nuclei and stellar processes changes the matter distribution on small scales, leading to significant systematic uncertainty in weak lensing constraints on cosmology. We investigate how the observable properties of group-scale haloes can constrain feedback’s impact on the matter distribution using Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS). Extending the results of previous work to smaller halo masses and higher wavenumber, k, we find that the baryon fraction in haloes contains significant information about the impact of feedback on the matter power spectrum. We explore how the thermal Sunyaev Zel’dovich (tSZ) signal from group-scale haloes contains similar information. Using recent Dark Energy Survey weak lensing and Atacama Cosmology Telescope tSZ cross-correlation measurements and models trained on CAMELS, we obtain 10 per cent constraints on feedback effects on the power spectrum at $$k \sim 5\, h\, {\rm Mpc}^{-1}$$. We show that with future surveys, it will be possible to constrain baryonic effects on the power spectrum to $$\mathcal {O}(\lt 1~{{\ \rm per\ cent}})$$ at $$k = 1\, h\, {\rm Mpc}^{-1}$$ and $$\mathcal {O}(3~{{\ \rm per\ cent}})$$ at $$k = 5\, h\, {\rm Mpc}^{-1}$$ using the methods that we introduce here. Finally, we investigate the impact of feedback on the matter bispectrum, finding that tSZ observables are highly informative in this case. 

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 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 In this work, we establish and test methods for implementing dynamical friction (DF) for massive black hole pairs that form in large volume cosmological hydrodynamical simulations that include galaxy formation and black hole growth. We verify our models and parameters both for individual black hole dynamics and for the black hole population in cosmological volumes. Using our model of DF from collisionless particles, black holes can effectively sink close to the galaxy centre, provided that the black hole’s dynamical mass is at least twice that of the lowest mass resolution particles in the simulation. Gas drag also plays a role in assisting the black holes’ orbital decay, but it is typically less effective than that from collisionless particles, especially after the first billion years of the black hole’s evolution. DF from gas becomes less than $$1{{\ \rm per\ cent}}$$ of DF from collisionless particles for BH masses >107 M⊙. Using our best DF model, we calculate the merger rate down to z = 1.1 using an Lbox = 35 Mpc h−1 simulation box. We predict ∼2 mergers per year for z > 1.1 peaking at z ∼ 2. These merger rates are within the range obtained in previous work using similar resolution hydrodynamical simulations. We show that the rate is enhanced by factor of ∼2 when DF is taken into account in the simulations compared to the no-DF run. This is due to $${\gt}40{{\ \rm per\ cent}}$$ more black holes reaching the centre of their host halo when DF is added. 

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 study the alignments of satellite galaxies, and their anisotropic distribution, with respect to location and orientation of their host central galaxy in MassiveBlack-II (MB-II) and IllustrisTNG simulations. We find that: the shape of the satellite system in haloes of mass ($$\gt 10^{13}\, h^{-1}\, \mathrm{M}_{\odot }$$) is well aligned with the shape of the central galaxy at z = 0.06 with the mean alignment between the major axes being ∼Δθ = 12° when compared to a uniform random distribution; that satellite galaxies tend to be anisotropically distributed along the major axis of the central galaxy with a stronger alignment in haloes of higher mass or luminosity; and that the satellite distribution is more anisotropic for central galaxies with lower star formation rate, which are spheroidal, and for red central galaxies. Radially, we find that satellites tend to be distributed along the major axis of the shape of the stellar component of central galaxies at smaller scales and the dark matter component on larger scales. We find that the dependence of satellite anisotropy on central galaxy properties and the radial distance is similar in both the simulations with a larger amplitude in MB-II. The orientation of satellite galaxies tends to point toward the location of the central galaxy at small scales and this correlation decreases with increasing distance, and the amplitude of satellite alignment is higher in high-mass haloes. However, the projected ellipticities do not exhibit a scale-dependent radial alignment, as has been seen in some observational measurements. 

ABSTRACT We investigate the redshift evolution of the intrinsic alignments (IAs) of galaxies in the MassiveBlackII (MBII) simulation. We select galaxy samples above fixed subhalo mass cuts ($$M_h\gt 10^{11,12,13}\,\mathrm{M}_{\odot }\, h^{-1}$$) at z = 0.6 and trace their progenitors to z = 3 along their merger trees. Dark matter components of z = 0.6 galaxies are more spherical than their progenitors while stellar matter components tend to be less spherical than their progenitors. The distribution of the galaxy–subhalo misalignment angle peaks at ∼10 deg with a mild increase with time. The evolution of the ellipticity–direction (ED) correlation amplitude ω(r) of galaxies (which quantifies the tendency of galaxies to preferentially point towards surrounding matter overdensities) is governed by the evolution in the alignment of underlying dark matter (DM) subhaloes to the matter density of field, as well as the alignment between galaxies and their DM subhaloes. At scales $$\sim 1~\mathrm{Mpc}\, h^{-1}$$, the alignment between DM subhaloes and matter overdensity gets suppressed with time, whereas the alignment between galaxies and DM subhaloes is enhanced. These competing tendencies lead to a complex redshift evolution of ω(r) for galaxies at $$\sim 1~\mathrm{Mpc}\, h^{-1}$$. At scales $$\gt 1~\mathrm{Mpc}\, h^{-1}$$, alignment between DM subhaloes and matter overdensity does not evolve significantly; the evolution of the galaxy–subhalo misalignment therefore leads to an increase in ω(r) for galaxies by a factor of ∼4 from z = 3 to 0.6 at scales $$\gt 1~\mathrm{Mpc}\, h^{-1}$$. The balance between competing physical effects is scale dependent, leading to different conclusions at much smaller scales ($$\sim 0.1~\mathrm{Mpc}\, h^{-1}$$).