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In the near future, projects like Laser Interferometer Space Antenna (LISA) and pulsar timing arrays are expected to detect gravitational waves from mergers between supermassive black holes, and it is crucial to precisely model the underlying merger populations now to maximize what we can learn from this new data. Here, we characterize expected high-redshift (z > 2) black hole mergers using the very large volume Astrid cosmological simulation, which uses a range of seed masses to probe down to low-mass black holes (BHs), and directly incorporates dynamical friction so as to accurately model the dynamical processes that bring black holes to the galaxy centre where binary formation and coalescence will occur. The black hole populations in Astrid include black holes down to $\sim 10^{4.5} \, \mathrm{M}_\odot$, and remain broadly consistent with the TNG simulations at scales $\gt 10^6 \, \mathrm{M}_\odot$ (the seed mass used in TNG). By resolving lower mass black holes, the overall merger rate is ∼5× higher than in TNG. However, incorporating dynamical friction delays mergers compared to a recentring scheme, reducing the high-z merger rate mass-matched mergers by a factor of ∼2×. We also calculate the expected LISA signal-to-noise values, and show that the distribution peaks at high SNR (>100), emphasizing the importance of implementing a seed mass well below LISA’s peak sensitivity ($\sim 10^6 \, \mathrm{M}_\odot$) to resolve the majority of LISA’s gravitational wave detections.

AI super-resolution, combining deep learning and N-body simulations, has been shown to successfully reproduce the large-scale structure and halo abundances in the Lambda cold dark matter cosmological model. Here, we extend its use to models with a different dark matter content, in this case fuzzy dark matter (FDM), in the approximation that the difference is encoded in the initial power spectrum. We focus on redshift z = 2, with simulations that model smaller scales and lower masses, the latter by two orders of magnitude, than has been done in previous AI super-resolution work. We find that the super-resolution technique can reproduce the power spectrum and halo mass function to within a few per cent of full high-resolution calculations. We also find that halo artefacts, caused by spurious numerical fragmentation of filaments, are equally present in the super-resolution outputs. Although we have not trained the super-resolution algorithm using full quantum pressure FDM simulations, the fact that it performs well at the relevant length and mass scales means that it has promise as a technique that could avoid the very high computational cost of the latter, in some contexts. We conclude that AI super-resolution can become a useful tool to extend the range of dark matter models covered in mock catalogues.

We look for simulated star-forming linear features such as the one recently discovered by van Dokkum et al. in the cosmological hydrodynamical simulationASTRID. Among the runaway black holes inASTRID, none are able to produce clear star-forming wakes. Meanwhile, flyby encounters, typically involving a compact galaxy (with a central black hole) and a star-forming galaxy (with a duo of black holes), reproduce remarkably well many of the key properties (length and linearity, recent star formation, etc.) of the observed star-forming linear feature. We predict that the feature will persist for approximately 100 Myr in such a system and hence constitute a rare event. The feature contains a partly stripped galaxy (withM_gal= 10⁹–10¹⁰M_⊙) and a dual black hole system (M_BH= 10⁵–10⁷M_⊙) in its brightest knot. The X-ray emission from AGN in the knot should be detectable in such systems. After 100–200 Myr from the first flyby, the galaxies merge, leaving behind a triple black hole system in a (still) actively star-forming early-type remnant of mass ∼5 × 10¹⁰M_⊙. Follow-up JWST observations may be key for revealing the nature of these linear features by potentially detecting the older stellar populations constituting the bright knot. Confirmation of such detections may therefore help discriminate a flyby encounter from a massive black hole wake to reveal the origin of such features.

Massive black holes in the centres of galaxies today must have grown by several orders of magnitude from seed black holes formed at early times. Detecting a population of intermediate mass black holes (IMBHs) can provide constraints on these elusive BH seeds. Here, we use the large volume cosmological hydrodynamical simulation Astrid, which includes IMBH seeds and dynamical friction to investigate the population of IMBH seeds. Dynamical friction is largely inefficient at sinking and merging seed IMBHs at high-z. This leads to an extensive population (several hundred per galaxy) of wandering IMBHs in large haloes at $z\sim 2$. A small fraction of these IMBHs are detectable as HLXs, Hyper Luminous X-ray sources. Importantly, at $z\sim 2$, IMBHs mergers produce the peak of GW events. We find close to a million GW events in Astrid between $z=\rm{2\!-\!3}$ involving seed IMBH mergers. These GW events (almost all detectable by LISA) at cosmic noon should provide strong constraints on IMBH seed models and their formation mechanisms. At the centre of massive galaxies, where the number of IMBHs can be as high as 10–100, SMBH-IMBH pairs can form. These Intermediate mass ratio inspirals (IMRIs) and extreme mass ratio inspirals (EMRIs), will require the next generation of milli-$\mu$Hz space-based GW interferometers to be detected. Large populations of IMBHs around massive black holes will probe their environments and MBH causal structure.

We use the ASTRID cosmological hydrodynamic simulation to investigate the properties and evolution of triple and quadruple massive black hole (MBH) systems at z = 2–3. Only a handful of MBH tuple systems have been detected to date. In ASTRID, we find 4 per cent of the $M_{\rm BH}\gt 10^7\, M_\odot$ are in tuples with $\Delta r_{\rm max} \lt 200\, {\rm kpc}$. The tuple systems span a range of separations with the majority of the observable AGN systems at Δr ∼ 50–100 kpc. They include some of the most massive BHs (up to $10^{10} \, M_\odot$) but with at least one of the components of $M_{\rm BH} \sim 10^7 \, {\rm M}_{\odot }$. Tuples’ host galaxies are typically massive with $M_* \sim 10^{10-11} \, M_\odot$. We find that $\gt 10~{{\ \rm per\ cent}}$ massive haloes with Mhalo > 1013 M⊙ host MBH tuples. Following the subsequent interactions between MBHs in tuples, we found that in $\sim 5~{{\ \rm per\ cent}}$ of the triplets all three MBHs merge within a Gyr, and 15 per cent go through one merger. As a by-product of the complex multigalaxy interaction of these systems, we also find that up to $\sim 5~{{\ \rm per\ cent}}$ of tuples lead to runaway MBHs. In ASTRID, virtually all of the ultramassive black holes ($\gt 10^{10} \, M_\odot$) have undergone a triple quasar phase, while for BHs with $M_{\rm BH} \sim 10^9 \, M_\odot$, this fraction drops to 50 per cent.

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.

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.

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.

We examine the dual [both black hole (BH) active] and offset (one BH active and in distinct galaxies) active galactic nucleus (AGN) population (comprising ∼ 2000 pairs at $0.5\, \text{kpc}\lesssim \Delta r\lt 30\, \text{kpc}$) at z = 2 ∼ 3 in the ASTRID simulation covering (360 cMpc)3. The dual (offset) AGN make up 3.0(0.5) per cent of all AGN at z = 2. The dual fraction is roughly constant while the offset fraction increases by a factor of 10 from z = 4 ∼ 2. Compared with the full AGN population, duals are characterized by low MBH/M* ratios, high specific star formation rates (sSFR) of $\sim 1\, \text{Gyr}^{-1}$, and high Eddington ratios (∼0.05, double that of single AGN). Dual AGNs are formed in major galaxy mergers (typically involving $M_\text{halo}\lt 10^{13}\, M_\odot$), with simular-mass BHs. At small separations (when host galaxies are in the late phase of the merger), duals become 2 ∼ 8 times brighter (albeit more obscured) than at larger separations. 80  per cent of the bright, close duals would merge within $\sim 500\, \text{Myr}$. Notably, the initially less-massive BHs in duals frequently become the brighter AGN during galaxy mergers. In offset AGN, the active BH is typically ≳ 10 times more massive than its non-active counterpart and than most BHs in duals. Offsets are predominantly formed in minor galaxy mergers with the active BH residing in the centre of massive haloes ($M_\text{ halo}\sim 10^{13-14}\, \mathrm{M}_\odot$). In these deep potentials, gas stripping is common and the secondary quickly deactivates. The stripping also leads to inefficient orbital decay amongst offsets, which stall at $\Delta r\sim 5\, \text{kpc}$ for a few hundred Myrs.