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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 assemble the largest C iv absorption line catalogue to date, leveraging machine learning, specifically Gaussian processes, to remove the need for visual inspection for detecting C iv absorbers. The catalogue contains probabilities classifying the reliability of the absorption system within a quasar spectrum. Our training set was a sub-sample of DR7 spectra that had no detectable C iv absorption in a large visually inspected catalogue. We used Bayesian model selection to decide between our continuum model and our absorption-line models. Using a random hold-out sample of 1301 spectra from all of the 26 030 investigated spectra in DR7 C iv catalogue, we validated our pipeline and obtained an 87 per cent classification performance score. We found good purity and completeness values, both $\sim 80{{\ \rm per\ cent}}$, when a probability of $\sim 95{{\ \rm per\ cent}}$ is used as the threshold. Our pipeline obtained similar C iv redshifts and rest equivalent widths to our training set. Applying our algorithm to 185 425 selected quasar spectra from SDSS DR12, we produce a catalogue of 113 775 C iv doublets with at least 95 per cent confidence. Our catalogue provides maximum a posteriori values and credible intervals for C iv redshift, column density, and Doppler velocity dispersion. We detect C iv absorption systems with a redshift range of 1.37–5.1, including 33 systems with a redshift larger than 5 and 549 absorbers systems with a rest equivalent width greater than 2 Å at more than 95 per cent confidence. Our catalogue can be used to investigate the physical properties of the circumgalactic and intergalactic media.

We introduce MF-Box, an extended version of MFEmulator, designed as a fast surrogate for power spectra, trained using N-body simulation suites from various box sizes and particle loads. To demonstrate MF-Box’s effectiveness, we design simulation suites that include low-fidelity (LF) suites (L1 and L2) at 256 and $100 \, \rm {Mpc\, ~}h^{-1}$, each with 1283 particles, and a high-fidelity (HF) suite with 5123 particles at $256 \, \rm {Mpc\, ~}h^{-1}$, representing a higher particle load compared to the LF suites. MF-Box acts as a probabilistic resolution correction function, learning most of the cosmological dependencies from L1 and L2 simulations and rectifying resolution differences with just three HF simulations using a Gaussian process. MF-Box successfully emulates power spectra from our HF testing set with a relative error of $\lt 3~{{\ \rm per\ cent}}$ up to $k \simeq 7 \, h\rm {Mpc}{^{-1}}$ at z ∈ [0, 3], while maintaining a cost similar to our previous multifidelity approach, which was accurate only up to z = 1. The addition of an extra LF node in a smaller box significantly improves emulation accuracy for MF-Box at $k \gt 2 \, h\rm {Mpc}{^{-1}}$, increasing it by a factor of 10. We conduct an error analysis of MF-Box based on computational budget, providing guidance for optimizing budget allocation per fidelity node. Our proposed MF-Box enables future surveys to efficiently combine simulation suites of varying quality, effectively expanding the range of emulation capabilities while ensuring cost efficiency.

The connection between galaxies and dark matter halos is often quantified using the stellar mass–halo mass (SMHM) relation. Optical and near-infrared imaging surveys have led to a broadly consistent picture of the evolving SMHM relation based on measurements of galaxy abundances and angular correlation functions. Spectroscopic surveys atz≳ 2 can also constrain the SMHM relation via the galaxy autocorrelation function and through the cross-correlation between galaxies and Lyαabsorption measured in transverse sight lines; however, such studies are very few and have produced some unexpected or inconclusive results. We use ∼3000 spectra ofz∼ 2.5 galaxies from the LyαTomography IMACS Survey (LATIS) to measure the galaxy–galaxy and galaxy–Lyαcorrelation functions in four bins of stellar mass spanning 10^9.2≲M_*/M_⊙≲ 10^10.5. Parallel analyses of the MultiDarkN-body and ASTRID hydrodynamic cosmological simulations allow us to model the correlation functions, estimate covariance matrices, and infer halo masses. We find that results of the two methods are mutually consistent and broadly accord with standard SMHM relations. This consistency demonstrates that we are able to measure and model Lyαtransmission fluctuationsδ_Fin LATIS accurately. We also show that the galaxy–Lyαcross-correlation, a free by-product of optical spectroscopic galaxy surveys at these redshifts, can constrain halo masses with similar precision to galaxy–galaxy clustering.

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.

From the formation mechanisms of stars and compact objects to nuclear physics, modern astronomy frequently leverages surveys to understand populations of objects to answer fundamental questions. The population of dark and isolated compact objects in the Galaxy contains critical information related to many of these topics, but is only practically accessible via gravitational microlensing. However, photometric microlensing observables are degenerate for different types of lenses, and one can seldom classify an event as involving either a compact object or stellar lens on its own. To address this difficulty, we apply a Bayesian framework that treats lens type probabilistically and jointly with a lens population model. This method allows lens population characteristics to be inferred despite intrinsic uncertainty in the lens class of any single event. We investigate this method’s effectiveness on a simulated ground-based photometric survey in the context of characterizing a hypothetical population of primordial black holes (PBHs) with an average mass of 30M_⊙. On simulated data, our method outperforms current black hole (BH) lens identification pipelines and characterizes different subpopulations of lenses while jointly constraining the PBH contribution to dark matter to ≈25%. Key to robust inference, our method can marginalize over population model uncertainty. We find the lower mass cutoff for stellar origin BHs, a key observable in understanding the BH mass gap, particularly difficult to infer in our simulations. This work lays the foundation for cutting-edge PBH abundance constraints to be extracted from current photometric microlensing surveys.

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.

We consider the potential for line intensity mapping (LIM) of the rotational CO(1-0), CO(2-1), and CO(3-2) transitions to detect deviations from General Relativity from 0 < z < 3 within the framework of a very general class of modified gravity models, called Horndeski’s theories. Our forecast assumes a multitracer analysis separately obtaining information from the matter power spectrum and the first two multipoles of the redshift space distortion power spectrum. To achieve ±0.1 level constraints on the slope of the kinetic gravity braiding and Planck mass evolution parameters, a mm-wave LIM experiment would need to accumulate ≈108–109 spectrometre-hours, feasible with instruments that could be deployed in the 2030s. Such a measurement would constrain the parameters of Horndeski’s theory at a level at worst competitive to and at best an order of magnitude tighter than existing constraints from the CMB and LSS. Our modelling code is publicly available.

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.