All very massive early-type galaxies contain supermassive blackholes, but are these blackholes all sufficiently active to produce detectable radio continuum sources? We have used the 887.5 MHz Rapid ASKAP Continuum Survey DR1 to measure the radio emission from morphological early-type galaxies brighter than $K_S=9.5$ selected from the 2MASS Redshift Survey, HyperLEDA, and RC3. In line with previous studies, we find median radio power increases with infrared luminosity, with $P_{1.4} \propto L_K^{2.2}$, although the scatter about this relation spans several orders of magnitude. All 40 of the $M_K<-25.7$ early-type galaxies in our sample have measured radio flux densities that are more than $2\sigma$ above the background noise, with $1.4\,{\rm GHz}$ radio powers spanning ${\sim} 3 \times 10^{20}$ to ${\sim} 3\times 10^{25}\,{\rm W/Hz^{-1}}$. Cross-matching our sample with integral field spectroscopy of early-type galaxies reveals that the most powerful radio sources preferentially reside in galaxies with relatively low angular momentum (i.e. slow rotators). While the infrared colours of most galaxies in our early-type sample are consistent with passive galaxies with negligible star formation and the radio emission produced by active galactic nuclei or AGN remnants, very low levels of star formation could power the weakest radio sources with little effect on many other star formation rate tracers.

The Australian SKA Pathfinder (ASKAP) has surveyed the sky at multiple frequencies as part of the Rapid ASKAP Continuum Survey (RACS). The first two RACS observing epochs, at 887.5 (RACS-low) and 1 367.5 (RACS-mid) MHz, have been released (McConnell, et al. 2020, PASA, 37, e048; Duchesne, et al. 2023, PASA, 40, e034). A catalogue of radio sources from RACS-low has also been released, covering the sky south of declination $+30^{\circ}$ (Hale, et al., 2021, PASA, 38, e058). With this paper, we describe and release the first set of catalogues from RACS-mid, covering the sky below declination $+49^{\circ}$. The catalogues are created in a similar manner to the RACS-low catalogue, and we discuss this process and highlight additional changes. The general purpose primary catalogue covering 36 200 deg$^2$ features a variable angular resolution to maximise sensitivity and sky coverage across the catalogued area, with a median angular resolution of $11.2^{\prime\prime} \times 9.3^{\prime\prime}$. The primary catalogue comprises 3 105 668 radio sources, including those in the Galactic Plane (2 861 923 excluding Galactic latitudes of $|b|<5^{\circ}$), and we estimate the catalogue to be 95% complete for sources above 2 mJy. With the primary catalogue, we also provide two auxiliary catalogues. The first is a fixed-resolution, 25-arcsec catalogue approximately matching the sky coverage of the RACS-low catalogue. This 25-arcsec catalogue is constructed identically to the primary catalogue, except images are convolved to a less-sensitive 25-arcsec angular resolution. The second auxiliary catalogue is designed for time-domain science and is the concatenation of source lists from the original RACS-mid images with no additional convolution, mosaicking, or de-duplication of source entries to avoid losing time-variable signals. All three RACS-mid catalogues, and all RACS data products, are available through the CSIRO ASKAP Science Data Archive (https://research.csiro.au/casda/).

Very metal-poor (VMP, [Fe/H]<-2.0) stars serve as invaluable repositories of insights into the nature and evolution of the first-generation stars formed in the early galaxy. The upcoming China Space Station Telescope (CSST) will provide us with a large amount of spectral data that may contain plenty of VMP stars, and thus it is crucial to determine the stellar atmospheric parameters ($T_{\textrm{eff}}$, $\log$ g, and [Fe/H]) for low-resolution spectra similar to the CSST spectra ($R\sim 200$). This study introduces a novel two-dimensional Convolutional Neural Network (CNN) model, comprised of three convolutional layers and two fully connected layers. The model’s proficiency is assessed in estimating stellar parameters, particularly metallicity, from low-resolution spectra ($R \sim 200$), with a specific focus on enhancing the search for VMP stars within the CSST spectral data. We mainly use 10 008 spectra of VMP stars from LAMOST DR3, and 16 638 spectra of non-VMP stars ([Fe/H]>-2.0) from LAMOST DR8 for the experiments and apply random forest and support vector machine methods to make comparisons. The resolution of all spectra is reduced to $R\sim200$ to match the resolution of the CSST, followed by pre-processing and transformation into two-dimensional spectra for input into the CNN model. The validation and practicality of this model are also tested on the MARCS synthetic spectra. The results show that using the CNN model constructed in this paper, we obtain Mean Absolute Error (MAE) values of 99.40 K for $T_{\textrm{eff}}$, 0.22 dex for $\log$ g, 0.14 dex for [Fe/H], and 0.26 dex for [C/Fe] on the test set. Besides, the CNN model can efficiently identify VMP stars with a precision rate of 94.77%, a recall rate of 93.73%, and an accuracy of 95.70%. This paper powerfully demonstrates the effectiveness of the proposed CNN model in estimating stellar parameters for low-resolution spectra ($R\sim200$) and recognizing VMP stars that are of interest for stellar population and galactic evolution work.

Creating radio galaxy catalogues from next-generation deep surveys requires automated identification of associated components of extended sources and their corresponding infrared hosts. In this paper, we introduce RadioGalaxyNET, a multimodal dataset, and a suite of novel computer vision algorithms designed to automate the detection and localization of multi-component extended radio galaxies and their corresponding infrared hosts. The dataset comprises 4 155 instances of galaxies in 2 800 images with both radio and infrared channels. Each instance provides information about the extended radio galaxy class, its corresponding bounding box encompassing all components, the pixel-level segmentation mask, and the keypoint position of its corresponding infrared host galaxy. RadioGalaxyNET is the first dataset to include images from the highly sensitive Australian Square Kilometre Array Pathfinder (ASKAP) radio telescope, corresponding infrared images, and instance-level annotations for galaxy detection. We benchmark several object detection algorithms on the dataset and propose a novel multimodal approach to simultaneously detect radio galaxies and the positions of infrared hosts.