We report the discovery of J0624–6948, a low-surface brightness radio ring, lying between the Galactic Plane and the Large Magellanic Cloud (LMC). It was first detected at 888 MHz with the Australian Square Kilometre Array Pathfinder (ASKAP), and with a diameter of ∼196 arcsec. This source has phenomenological similarities to Odd Radio Circles (ORCs). Significant differences to the known ORCs −− a flatter radio spectral index, the lack of a prominent central galaxy as a possible host, and larger apparent size −− suggest that J0624–6948 may be a different type of object. We argue that the most plausible explanation for J0624–6948 is an intergalactic supernova remnant due to a star that resided in the LMC outskirts that had undergone a single-degenerate type Ia supernova, and we are seeing its remnant expand into a rarefied, intergalactic environment. We also examine if a massive star or a white dwarf binary ejected from either galaxy could be the supernova progenitor. Finally, we consider several other hypotheses for the nature of the object, including the jets of an active galactic nucleus (AGN) or the remnant of a nearby stellar super-flare.

We present an analysis of a new 120 deg2 radio continuum image of the Large Magellanic Cloud (LMC) at 888 MHz with a bandwidth of 288 MHz and beam size of $13\rlap{.}^{\prime \prime }9\times 12\rlap{.}^{\prime \prime }1$, from the Australian Square Kilometre Array Pathfinder (ASKAP) processed as part of the Evolutionary Map of the Universe (EMU) survey. The median Root Mean Squared noise is 58 μJy beam−1. We present a catalogue of 54,612 sources, divided over a Gold list (30,866 sources) complete down to 0.5 mJy uniformly across the field, a Silver list (22,080 sources) reaching down to < 0.2 mJy and a Bronze list (1,666 sources) of visually inspected sources in areas of high noise and/or near bright complex emission. We discuss detections of planetary nebulæ and their radio luminosity function, young stellar objects showing a correlation between radio luminosity and gas temperature, novæ and X-ray binaries in the LMC, and active stars in the Galactic foreground that may become a significant population below this flux level. We present examples of diffuse emission in the LMC (H ii regions, supernova remnants, bubbles) and distant galaxies showcasing spectacular interaction between jets and intracluster medium. Among 14,333 infrared counterparts of the predominantly background radio source population we find that star-forming galaxies become more prominent below 3 mJy compared to active galactic nuclei. We combine the new 888 MHz data with archival Australia Telescope Compact Array data at 1.4 GHz to determine spectral indices; the vast majority display synchrotron emission but flatter spectra occur too. We argue that the most extreme spectral index values are due to variability.

We present two new radio continuum images from the Australian Square Kilometre Array Pathfinder (ASKAP) survey in the direction of the Small Magellanic Cloud (SMC). These images are part of the Evolutionary Map of the Universe (EMU) Early Science Project (ESP) survey of the Small and Large Magellanic Clouds. The two new source lists produced from these images contain radio continuum sources observed at 960 MHz (4489 sources) and 1320 MHz (5954 sources) with a bandwidth of 192 MHz and beam sizes of 30.0 ×30.0 and 16.3 ×15.1 , respectively. The median Root Mean Squared (RMS) noise values are 186 µJy beam −1 (960 MHz) and 165 µJy beam −1 (1320 MHz). To create point source catalogues, we use these two source lists, together with the previously published Molonglo Observatory Synthesis Telescope (MOST) and the Australia Telescope Compact Array (ATCA) point source catalogues to estimate spectral indices for the whole population of radio point sources found in the survey region. Combining our ASKAP catalogues with these radio continuum surveys, we found 7736 point-like sources in common over an area of 30 deg 2 . In addition, we report the detection of two new, low surface brightness supernova remnant candidates in the SMC. The high sensitivity of the new ASKAP ESP survey also enabled us to detect the bright end of the SMC planetary nebula sample, with 22 out of 102 optically known planetary nebulae showing point-like radio continuum emission. Lastly, we present several morphologically interesting background radio galaxies.

We present a study of six open clusters (Berkeley 67, King 2, NGC 2420, NGC 2477, NGC 2682 and NGC 6940) using the Ultra Violet Imaging Telescope (UVIT) aboard ASTROSAT and Gaia EDR3. We used combinations of astrometric, photometric and systematic parameters to train and supervise a machine learning algorithm along with a Gaussian mixture model for the determination of cluster membership. This technique is robust, reproducible and versatile in various cluster environments. In this study, the Gaia EDR3 membership catalogues are provided along with classification of the stars as members, candidates and field in the six clusters. We could detect 200–2500 additional members using our method with respect to previous studies, which helped estimate mean space velocities, distances, number of members and core radii. UVIT photometric catalogues, which include blue stragglers, main-sequence and red giants are also provided. From UV–Optical colour-magnitude diagrams, we found that majority of the sources in NGC 2682 and a few in NGC 2420, NGC 2477 and NGC 6940 showed excess UV flux. NGC 2682 images have ten white dwarf detection in far-UV. The far-UV and near-UV images of the massive cluster NGC 2477 have 92 and 576 members respectively, which will be useful to study the UV properties of stars in the extended turn-off and in various evolutionary stages from main-sequence to red clump. Future studies will carry out panchromatic and spectroscopic analysis of noteworthy members detected in this study.

We propose a method for the automated detection of strong galaxy-galaxy gravitational lenses in images, utilising a convolutional neural network (CNN) trained on 210 000 simulated galaxy-galaxy lens and non-lens images. The CNN, named LensFinder, was tested on a separate 210 000 simulated image catalogue, with 95% of images classied with at least 98.6% certainty. An accuracy of over 98% was achieved and an area under curve of 0.9975 was determined from the resulting receiver operating characteristic curve. A regional CNN, R-LensFinder, was trained to label lens positions in images, perfectly labelling 80% while partially labelling another 10% correctly.