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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.

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.

Abstract:

Stars in galactic centres are occasionally scattered so close to the central supermassive black hole that they are completely disrupted by tidal forces, initiating a transient accretion event. The aftermath of such a tidal disruption event (TDE) produces a bright-and-blue accretion flow that is known to persist for at least a decade (observationally) and can in principle produce ionizing radiation for hundreds of years. TDEs are known (observationally) to be overrepresented in galaxies that show extended emission-line regions (EELRs), with no pre-TDE classical active galactic nucleus activity, and to produce transient ‘coronal lines’, such as [Fe x] and [Fe xiv]. Using coupled cloudy-TDE disc simulations we show that TDE discs produce a sufficient ionizing radiation flux over their lifetimes to power both EELR of radial extents of light years, and coronal lines. EELRs are produced when the ionizing radiation interacts with low-density () clouds on galactic scales, while coronal lines are produced by high-density () clouds near the galactic centre. High-density gas in galactic centres will also result in the rapid switching on of narrow-line features in post-TDE galaxies, and also various high-ionization lines, which may be observed throughout the infrared with James Webb Space Telescope. Galaxies with a higher intrinsic rate of TDEs will be more likely to show macroscopic EELRs, which can be traced to originate from the previous TDE in that galaxy.

Abstract:

We present the detection of the binary system 2MASS J02132062+3648506 AB using the Karl G. Jansky Very Large Array archive data observed at 4–8 GHz. The system is a triple consisting of a tight binary () of two M dwarfs of spectral types M4.5 and M6.5 and a wide T3 brown dwarf companion (16.4 arcsec). The binary displays coronal and chromospheric activity as traced by previously measured X-ray flux and H emission. We detect the unresolved binary at a peak flux density of at a signal-to-noise ratio (SNR) of and determine a radio luminosity of . The radio emission is quiescent, polarized at a mean circular polarization fraction % and exhibits a spectral index . We probe the binary using the Enhanced Multi-Element Remotely Linked Interferometer Network (e-MERLIN) with an angular resolution of mas at 5 GHz and detect a component at a peak flux density of Jy at a SNR . We propose a gyrosynchrotron origin for the radio emission and estimate a magnetic field strength G, an emitting region of size times the radius of the M4.5 primary and a plasma number density . The brown dwarf companion is not detected. Additionally, we have analysed observations of 2MASS J04183483+213127, a chromospherically active L5 brown dwarf which is also not detected and can only place flux density upper limits at Jy and Jy for Stokes I and V, respectively.