Galaxy formation and evolution

Abstract:

Abstract Despite the vast amount of energy released by active galactic nuclei (AGN), their role in early galaxy formation and in regulating the growth of supermassive black holes (SMBHs) remains poorly understood. Through new high-resolution zoom-in cosmological simulations, we follow the co-evolution of 105 M⊙ black hole seeds with their host dwarf galaxy. We model ionizing feedback from a Pop III.1 progenitor, applicable to a wide range of internally or externally irradiated SMBH formation scenarios. The simulated suite progressively spans physics ranging from no AGN feedback to more complex setups including thermal, kinetic and radiative feedback – explored for both low and enhanced AGN power. Across all our models, we find that black hole seeds efficiently reach masses of ∼107 M⊙ within a ∼1010 M⊙ halo by z = 8. Although they exhibit notably different mass growth histories, these latter seem unimpeded by the presence of AGN feedback. The simulation including radiative feedback is the most distinct, with super-Eddington episodes driving fast and mass-loaded gas outflows (exceeding 2500 km s−1) up to ∼50 kpc, along with minor stellar mass suppression in the host galaxy. Our measurements are in broad agreement with moderate luminosity quasars recently observed by JWST, producing overmassive black holes (SMBH-to-galaxy mass ratios 0.01 − 1), dynamical masses of ∼109.5 M⊙, stellar masses of ∼108.5 M⊙, and high, though short-lived, Eddington fraction accretion rates. These results advocate for a scenario where AGN feedback allows for rapid SMBH growth during the reionisation era, while driving winds that extend deep into the intergalactic medium – shaping host galaxies as well as more distant surroundings.

Abstract:

<jats:title>Abstract</jats:title> <jats:p>We have carried out a detailed analysis of the 3.4 μm spectral feature arising from Polycyclic Aromatic Hydrocarbons (PAH), using JWST archival data. For the first time in an external galaxy (NGC 6240), we have identified two distinct spectral components of the PAH 3.4 μm feature: a shorter wavelength component at 3.395 μm, which we attribute to short aliphatic chains tightly attached to the aromatic rings of the PAH molecules; and a longer wavelength feature at 3.405 μm that arises from longer, more fragile, aliphatic chains that are weakly attached to the parent PAH molecule. These longer chains are more easily destroyed by far-ultraviolet photons (&amp;gt;5eV) and PAH thermal emission only occurs where PAH molecules are shielded from more energetic photons by dense molecular gas. We see a very strong correlation in the morphology of the PAH 3.395 μm feature with the PAH 3.3 μm emission, the latter arising from robust aromatic PAH molecules. We also see an equally strong correlation between the PAH 3.405 μm morphology and the warm molecular gas, as traced by H2 vibrational lines. We show that the flux ratio PAH 3.395/PAH 3.405 &amp;lt; 0.3 corresponds strongly to regions where the PAH molecules are shielded by dense molecular gas, so that only modestly energetic UV photons penetrate to excite the PAHs. Our work shows that PAH 3.405 μm and PAH 3.395 μm emission features can provide robust diagnostics of the physical conditions of the interstellar medium in external galaxies, and can be used to quantify the energies of the photon field penetrating molecular clouds.</jats:p>

Abstract:

Abstract JWST has revealed a large population of active galactic nuclei (AGN) in the distant universe, which are challenging our understanding of early massive black hole (BH) seeding and growth. We expand the exploration of this population to lower luminosities by stacking ∼600 NIRSpec grating spectra from the JWST Advanced Deep Extragalactic Survey (JADES) at 3 < z < 7, in bins of redshift, [O iii]5007 luminosity and equivalent width, UV luminosity and stellar mass. In various stacks, we detect a broad component of Hα without a counterpart in [O iii], implying that it is not due to outflows but traces the Broad Line Region of a large population of low-luminosity AGN not detected in individual spectra. The detection, in some stacks, of high [O iii]4363/Hγ, typical of AGN, further confirms the detection of a large population of AGN. We infer that the stacks probe BHs with masses of a few times 106M⊙ accreting at rates L/LEdd ∼ 0.02–0.1, i.e. a low mass and dormant parameter space poorly explored by previous studies on individual targets. We identify populations of BHs that fall within the scatter of the local MBH − M* scaling relation, indicating that there is a population of high-z BHs that are not overmassive relative to their host galaxies. Yet, on average, the stacks are still overmassive relative the local relation, with some of them 1–2 dex above it. We infer that the BH mass function at 3 < z < 5 is consistent with models in which BHs evolve through short bursts of super-Eddington accretion.

Abstract:

Abstract The peculiar velocity field of the local Universe provides direct insights into its matter distribution and the underlying theory of gravity, and is essential in cosmological analyses for modelling deviations from the Hubble flow. Numerous methods have been developed to reconstruct the density and velocity fields at z ≲ 0.05, typically constrained by redshift-space galaxy positions or by direct distance tracers such as the Tully–Fisher relation, the fundamental plane, or Type Ia supernovae. We introduce a validation framework to evaluate the accuracy of these reconstructions against catalogues of direct distance tracers. Our framework assesses the goodness-of-fit of each reconstruction using Bayesian evidence, residual redshift discrepancies, velocity scaling, and the need for external bulk flows. Applying this framework to a suite of reconstructions—including those derived from the Bayesian Origin Reconstruction from Galaxies (BORG) algorithm and from linear theory—we find that the non-linear BORG reconstruction consistently outperforms others. We highlight the utility of such a comparative approach for supernova or gravitational wave cosmological studies, where selecting an optimal peculiar velocity model is essential. Additionally, we present calibrated bulk flow curves predicted by the reconstructions and perform a density–velocity cross-correlation using a linear theory reconstruction to constrain the growth factor, yielding S8 = 0.793 ± 0.035. The result is in good agreement with both weak lensing and Planck, but is in strong disagreement with some peculiar velocity studies.

Abstract:

In this study, we examine over 14 000 radio galaxies finely selected from Radio Galaxy Zoo (RGZ) project and provide classifications for approximately 5900 FRIs and 8100 FRIIs. We present an analysis of these predicted radio galaxy morphologies for the RGZ catalogue, classified using a pre-trained radio galaxy foundation model that has been fine-tuned to predict Fanaroff–Riley (FR) morphology. As seen in previous studies, our results show overlap between morphologically classified FRI and FRII luminosity–size distributions and we find that the model’s confidence in its predictions is lowest in this overlap region, suggesting that source morphologies are more ambiguous. We identify the presence of low-luminosity FRII sources, the proportion of which, with respect to the total number of FRIIs, is consistent with previous studies. However, a comparison of the low-luminosity FRII sources found in this work with those identified by previous studies reveals differences that may indicate their selection is influenced by the choice of classification methodology. We investigate the impacts of both pre-training and fine-tuning data selection on model performance for the downstream classification task, and show that while different pre-training data choices affect model confidence they do not appear to cause systematic generalization biases for the range of physical and observational characteristics considered in this work; however, we note that the same is not necessarily true for fine-tuning. As automated approaches to astronomical source identification and classification become increasingly prevalent, we highlight training data choices that can affect the model outputs and propagate into downstream analyses.