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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.

 

We quantify the cosmological spread of baryons relative to their initial neighbouring dark matter distribution using thousands of state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project. We show that dark matter particles spread relative to their initial neighbouring distribution owing to chaotic gravitational dynamics on spatial scales comparable to their host dark matter halo. In contrast, gas in hydrodynamic simulations spreads much further from the initial neighbouring dark matter owing to feedback from supernovae (SNe) and active galactic nuclei (AGN). We show that large-scale baryon spread is very sensitive to model implementation details, with the fiducial simba model spreading ∼40 per cent of baryons >1 Mpc away compared to ∼10 per cent for the IllustrisTNG and astrid models. Increasing the efficiency of AGN-driven outflows greatly increases baryon spread while increasing the strength of SNe-driven winds can decrease spreading due to non-linear coupling of stellar and AGN feedback. We compare total matter power spectra between hydrodynamic and paired N-body simulations and demonstrate that the baryonic spread metric broadly captures the global impact of feedback on matter clustering over variations of cosmological and astrophysical parameters, initial conditions, and (to a lesser extent) galaxy formation models. Using symbolic regression, we find a function that reproduces the suppression of power by feedback as a function of wave number (k) and baryonic spread up to $k \sim 10\, h$ Mpc−1 in SIMBA while highlighting the challenge of developing models robust to variations in galaxy formation physics implementation.

 

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.

 

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.

 

Abstract Recent work has pointed out the potential existence of a tight relation between the cosmological parameter Ω m , at fixed Ω b , and the properties of individual galaxies in state-of-the-art cosmological hydrodynamic simulations. In this paper, we investigate whether such a relation also holds for galaxies from simulations run with a different code that makes use of a distinct subgrid physics: Astrid. We also find that in this case, neural networks are able to infer the value of Ω m with a ∼10% precision from the properties of individual galaxies, while accounting for astrophysics uncertainties, as modeled in Cosmology and Astrophysics with MachinE Learning (CAMELS). This tight relationship is present at all considered redshifts, z ≤ 3, and the stellar mass, the stellar metallicity, and the maximum circular velocity are among the most important galaxy properties behind the relation. In order to use this method with real galaxies, one needs to quantify its robustness: the accuracy of the model when tested on galaxies generated by codes different from the one used for training. We quantify the robustness of the models by testing them on galaxies from four different codes: IllustrisTNG, SIMBA, Astrid, and Magneticum. We show that the models perform well on a large fraction of the galaxies, but fail dramatically on a small fraction of them. Removing these outliers significantly improves the accuracy of the models across simulation codes. 

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.