Breakthrough Listen

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ABSTRACT Radio haloes are diffuse synchrotron sources that trace the turbulent intracluster medium (ICM) of galaxy clusters. However, their origin remains unknown. Two main formation models have been proposed: the hadronic model, in which relativistic electrons are continuously injected by cosmic-ray protons; and the leptonic turbulent re-acceleration model, where cluster mergers re-energize electrons in situ. A key discriminant between the two models would be the existence of ultra-steep spectrum radio haloes (USSRHs), which can only be produced through turbulent re-acceleration. Here, we report the discovery of an USSRH in the galaxy cluster SPT-CLJ2337–5942 at redshift $z = 0.78$ in the MeerKAT-South Pole Telescope 100 deg$^2$ UHF (0.58–1.09 GHz) survey. This discovery is noteworthy for two primary reasons: it is the highest redshift USSRH system to date; and the close correspondence of the radio emission with the thermal ICM as traced by Chandra X-ray observations, further supporting the leptonic re-acceleration model. The halo is underluminous for its mass, consistent with a minor merger origin, which produces steep-spectrum, lower luminosity haloes. This result demonstrates the power of wide-field, high-fidelity, low-frequency ($\lesssim 1$ GHz) surveys like the MeerKAT-SPT 100 deg$^2$ programme to probe the origin and evolution of radio haloes over cosmic time, ahead of the Square Kilometre Array.

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Context. Jets from accreting black-hole (BH) X-ray binary (XRB) systems are powerful outflows that release a large fraction of the accretion energy to the surrounding environment, providing a feedback mechanism that may alter the properties of the interstellar medium (ISM). Studying accretion processes alongside their feedback on the environment may enable one to estimate the matter and energy input and output around accreting BHs. Aims. We aim to study the extended jet structures around the BH XRB GRS 1758–258. First observed in VLA data, these parsec-scale jet structures originate from jet-ISM interaction, and are characterised by a peculiar Z-shape morphology. Methods. Using the MeerKAT radio telescope we observed GRS 1758–258 in the L band for a total exposure of 7 hr. Following a calorimetry-based method originally proposed for active galactic nuclei (AGN) and later applied to X-ray binaries, we estimated the properties of the jets and of the surrounding ISM. Results. We detect a jet and a counter-jet terminating in bow-shock structures induced by their interaction with the ISM. We identified both synchrotron and bremsstrahlung emitting regions within the northern lobe, while the southern lobe is dominated by thermal emission. We measured an ISM particle density of between 10 and 40 cm −3 across both the northern and southern jets, slightly lower in the northern region. The estimated ages of the two jet sides range from 6 to 51 kyr, with the northern jet seemingly younger than the southern one. The time-averaged transferred jet energy for both jets falls between 4.4 × 10 33 and 3.3 × 10 36 erg s −1 , with slight differences between the northern and southern jets ascribed to different local environmental conditions. Comparing the new MeerKAT with archival VLA observations, we measured a proper motion of a portion of the northern jet of ∼130 mas/year. Conclusions. Jet-ISM interaction structures on both sides of GRS 1758–258 reveal different local ISM properties. The comparison between the morphology of these structures and those from other XRBs indicates that the lobes in GRS 1758–258 may be younger and may result from a number of jet activity phases. The estimated time-averaged energy transferred to the environment is slightly lower than, but comparable to, that observed in other XRBs, consistent with the younger age of the lobes in GRS 1758–258 relative to those of other systems.

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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.

Abstract:

A key science goal of large sky surveys such as those conducted by the Vera C. Rubin Observatory and precursors to the Square Kilometre Array is the identification of variable and transient objects. One approach is analyzing time series of the changing brightness of sources, namely, light curves. However, finding adequate statistical representations of light curves is challenging because of the sparsity of observations, irregular sampling, and nuisance factors inherent in astronomical data collection. The wide diversity of objects that a large-scale survey will observe also means that making parametric assumptions about the shape of light curves is problematic. We present a Gaussian process (GP) regression approach for characterizing light-curve variability that addresses these challenges. Our approach makes no assumptions about the shape of a light curve and, therefore, is general enough to detect a range of variable and transient source types. In particular, we propose using the joint distribution of GP amplitude hyperparameters to distinguish variable and transient candidates from nominally stable ones and apply this approach to 6394 radio light curves from the ThunderKAT survey. We compare our results with two variability metrics commonly used in radio astronomy, namely ην and Vν, and show that our approach has better discriminatory power and interpretability. Finally, we conduct a rudimentary search for transient sources in the ThunderKAT data set to demonstrate how our approach might be used as an initial screening tool. Computational notebooks in Python and R are available to help deploy this framework to other surveys.