MeerKAT

Abstract:

Context. The H I mass function (HIMF) is a crucial tool for understanding the evolution of the H I content in galaxies over cosmic time and, hence, to constraining both the baryon cycle in galaxy evolution and the reionization history of the Universe. Aims. We aim to derive semiempirical constraints at z  ∼ 0.37 by combining literature results on the stellar mass function from optical surveys with recent findings on the M HI  −  M ⋆ scaling relation derived via spectral stacking analysis applied to 21 cm line interferometric data from the MIGHTEE and CHILES surveys, conducted with the MeerKAT and VLA radio telescopes, respectively. Methods. We drew synthetic stellar mass samples directly from the publicly available results underlying the analysis of the COSMOS2020 galaxy photometric sample. We then converted M ⋆ into M HI using analytical fitting functions to the data points from H I stacking. We next fit a Schechter function to the median HIMF from all the samples via Monte Carlo Markov chains. We finally derived the posterior distribution for Ω HI by integrating the models for the HIMF built from the posteriors samples of the Schechter parameters. Results. We find a deviation of the HIMF at z  ∼ 0.37 from the results at z  ∼ 0 from the ALFALFA survey and at z  ∼ 1 from uGMRT data. Our results for Ω HI are in broad agreement with other literature results and follow the overall trend on Ω HI as a function of redshift. The derived value Ω HI = (7.02 +0.59 −0.52 ) × 10 −4 at z  ∼ 0.37 from the combined analysis deviates by ∼2.9 σ from the ALFALFA result at z  ∼ 0. Conclusions. Our findings regarding the HIMF and Ω HI derived from deep, state-of-the-art interferometric surveys differ from previous literature results at z  ∼ 0 and z  ∼ 1. We are unable to confirm at this stage whether these differences are due to cosmic evolution consistent with a smooth transition of the H I content of galaxies over the last 8 Gyr or due to selection biases and systematics.

Abstract:

Abstract Jets from stellar-mass and supermassive black holes provide the unique opportunity to study similar processes in two very different mass regimes. Historically, the apparent speeds of black hole x-ray binary (BHXRBs) jets have been observed to be lower than jet speeds from active galactic nuclei (AGN) and specifically blazars. In this work, we show that selection effects could be the primary cause of the observed population differences. For the first time, it is possible to perform a statistical analysis of the underlying BHXRB jet Lorentz factor distribution. We use both the Anderson-Darling test and apply nested sampling to this problem. With Bayes factors, we confirm that the Lorentz factor distribution of BHXRBs is best described with a power law, the same model that has been applied to AGN jets. For a Lorentz factor distribution following $\rm N(\Gamma ) \propto \Gamma ^b$ we find a value for the exponent of $b=-2.64_{-0.55}^{+0.46}$. This exponent is consistent with values found in AGN population studies, within 1σ for Swift-BAT and Fermi-LAT selected AGN. The best-fit exponent for the radio selected MOJAVE sample is just above our 2σ limit. This is a remarkable agreement given the different scales at which the jets are observed. The observed slower apparent speeds in BHXRBs are largely due to the much larger inclinations in this sample. Furthermore, nested sampling confirms that Γmax is completely unconstrained using this method. Therefore, based on kinematics alone, BHXRB jets are broadly consistent with being just as relativistic as those from supermassive black holes.

Abstract:

Abstract Understanding black hole–galaxy co-evolution and the role of AGN feedback requires complete AGN samples, including heavily obscured systems. Such sources are key to constraining the black hole accretion rate density over cosmic time, yet they are challenging to identify and characterise across most wavelengths. In this work, we present the first UV line–selected ([Ne v]3426 and C iv1549) sample of obscured AGN with full X-ray-to-radio coverage, assembled by combining data from the Chandra COSMOS Legacy survey, the COSMOS2020 UV–NIR catalogue, mid- and far-IR photometry from XID+, and radio observations from the VLA and MIGHTEE surveys. Using CIGALE to perform spectral energy distribution (SED) fitting, we analyse 184 obscured AGN at 0.6 < z < 1.2 and 1.5 < z < 3.1, enabling detailed measurements of AGN and host galaxy properties, and direct comparison with Simba hydrodynamical simulations. We find that X-ray and radio data are essential for accurate SED fits, with the radio band proving critical when X-ray detections are missing or in cases of poor IR coverage. Comparisons with matched non-active galaxies and simulations suggest that the [Ne v]-selected sources are in a pre-quenching stage, while the C iv-selected ones are likely quenched by AGN activity. Our results indicate that [Ne v] and C iv selections target galaxies in a transient phase of their co-evolution, characterised by intense, obscured accretion, and pave the way for future extensions with upcoming large area high-z spectroscopic surveys.

Abstract:

ABSTRACT Understanding the cold atomic hydrogen gas (H i) within cosmic filaments has the potential to pin down the relationship between the low density gas in the cosmic web and how the galaxies that lie within it grow using this material. We report the discovery of a cosmic filament using 14 H i-selected galaxies that form a very thin elongated structure of 1.7 Mpc. These galaxies are embedded within a much larger cosmic web filament, traced by optical galaxies, that spans at least $\sim 15$ Mpc. We find that the spin axes of the H i galaxies are significantly more strongly aligned with the cosmic web filament ($\langle \vert \cos \psi \vert \rangle = 0.64 \pm 0.05$) than cosmological simulations predict, with the optically selected galaxies showing alignment to a lesser degree ($\langle \vert \cos \psi \vert \rangle = 0.55 \pm 0.05$). This structure demonstrates that within the cosmic filament, the angular momentum of galaxies is closely connected to the large-scale filamentary structure. We also find strong evidence that the galaxies are orbiting around the spine of the filament, making this one of the largest rotating structures discovered thus far, and from which we can infer that there is transfer of angular momentum from the filament to the individual galaxies. The abundance of H i galaxies along the filament and the low dynamical temperature of the galaxies within the filament indicates that this filament is at an early evolutionary stage where the imprint of cosmic matter flow on galaxies has been preserved over cosmic time.

Abstract:

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.