In this work, we present a methodology and a corresponding code-base for constructing mock integral field spectrograph (IFS) observations of simulated galaxies in a consistent and reproducible way. Such methods are necessary to improve the collaboration and comparison of observation and theory results, and accelerate our understanding of how the kinematics of galaxies evolve over time. This code, SimSpin, is an open-source package written in R, but also with an API interface such that the code can be interacted with in any coding language. Documentation and individual examples can be found at the open-source website connected to the online repository. SimSpin is already being utilised by international IFS collaborations, including SAMI and MAGPI, for generating comparable data sets from a diverse suite of cosmological hydrodynamical simulations.

The present work discusses the use of a weakly-supervised deep learning algorithm that reduces the cost of labelling pixel-level masks for complex radio galaxies with multiple components. The algorithm is trained on weak class-level labels of radio galaxies to get class activation maps (CAMs). The CAMs are further refined using an inter-pixel relations network (IRNet) to get instance segmentation masks over radio galaxies and the positions of their infrared hosts. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope, specifically the Evolutionary Map of the Universe (EMU) Pilot Survey, which covered a sky area of 270 square degrees with an RMS sensitivity of 25–35 $\mu$Jy beam$^{-1}$. We demonstrate that weakly-supervised deep learning algorithms can achieve high accuracy in predicting pixel-level information, including masks for the extended radio emission encapsulating all galaxy components and the positions of the infrared host galaxies. We evaluate the performance of our method using mean Average Precision (mAP) across multiple classes at a standard intersection over union (IoU) threshold of 0.5. We show that the model achieves a mAP$_{50}$ of 67.5% and 76.8% for radio masks and infrared host positions, respectively. The network architecture can be found at the following link: https://github.com/Nikhel1/Gal-CAM

The Australian SKA Pathfinder (ASKAP) radio telescope has carried out a survey of the entire Southern Sky at 887.5 MHz. The wide area, high angular resolution, and broad bandwidth provided by the low-band Rapid ASKAP Continuum Survey (RACS-low) allow the production of a next-generation rotation measure (RM) grid across the entire Southern Sky. Here we introduce this project as Spectral and Polarisation in Cutouts of Extragalactic sources from RACS (SPICE-RACS). In our first data release, we image 30 RACS-low fields in Stokes I, Q, U at 25$^{\prime\prime}$ angular resolution, across 744–1032 MHz with 1 MHz spectral resolution. Using a bespoke, highly parallelised, software pipeline we are able to rapidly process wide-area spectro-polarimetric ASKAP observations. Notably, we use ‘postage stamp’ cutouts to assess the polarisation properties of 105912 radio components detected in total intensity. We find that our Stokes Q and U images have an rms noise of $\sim$80 $\unicode{x03BC}$Jy PSF$^{-1}$, and our correction for instrumental polarisation leakage allows us to characterise components with $\gtrsim$1% polarisation fraction over most of the field of view. We produce a broadband polarised radio component catalogue that contains 5818 RM measurements over an area of $\sim$1300 deg$^{2}$ with an average error in RM of $1.6^{+1.1}_{-1.0}$ rad m$^{-2}$, and an average linear polarisation fraction $3.4^{+3.0}_{-1.6}$ %. We determine this subset of components using the conditions that the polarised signal-to-noise ratio is $>$8, the polarisation fraction is above our estimated polarised leakage, and the Stokes I spectrum has a reliable model. Our catalogue provides an areal density of $4\pm2$ RMs deg$^{-2}$; an increase of $\sim$4 times over the previous state-of-the-art (Taylor, Stil, Sunstrum 2009, ApJ, 702, 1230). Meaning that, having used just 3% of the RACS-low sky area, we have produced the 3rd largest RM catalogue to date. This catalogue has broad applications for studying astrophysical magnetic fields; notably revealing remarkable structure in the Galactic RM sky. We will explore this Galactic structure in a follow-up paper. We will also apply the techniques described here to produce an all-Southern-sky RM catalogue from RACS observations. Finally, we make our catalogue, spectra, images, and processing pipeline publicly available.

We present observations of the Mopra carbon monoxide (CO) survey of the Southern Galactic Plane, covering Galactic longitudes spanning $l = 250^{\circ}$ ($-110^{\circ}$) to $l = 355^{\circ}$ ($-5^{\circ}$), with a latitudinal coverage of at least $|b|<1^\circ$, totalling an area of $>$210 deg$^{2}$. These data have been taken at 0.6 arcmin spatial resolution and 0.1 km s$^{-1}$ spectral resolution, providing an unprecedented view of the molecular gas clouds of the Southern Galactic Plane in the 109–115 GHz $J = 1-0$ transitions of $^{12}$CO, $^{13}$CO, C$^{18}$O, and C$^{17}$O.

Asymmetric emission of gravitational waves during mergers of black holes (BHs) produces a recoil kick, which can set a newly formed BH on a bound orbit around the centre of its host galaxy, or even completely eject it. To study this population of recoiling BHs we extract properties of galaxies with merging BHs from Illustris TNG300 simulation and then employ both analytical and numerical techniques to model unresolved process of BH recoil. This comparative analysis between analytical and numerical models shows that, on cosmological scales, numerically modelled recoiling BHs have a higher escape probability and predict a greater number of offset active galactic nuclei (AGN). BH escaped probability $>$40% is expected in 25$\%$ of merger remnants in numerical models, compared to 8$\%$ in analytical models. At the same time, the predicted number of offset AGN at separations ${>}5$ kpc changes from 58$\%$ for numerical models to 3$\%$ for analytical models. Since BH ejections in major merger remnants occur in non-virialised systems, static analytical models cannot provide an accurate description. Thus we argue that numerical models should be used to estimate the expected number density of escaped BHs and offset AGN.

We study the correlation between the non-thermal velocity dispersion ($\sigma_{nth}$) and the length scale (L) in the neutral interstellar medium (ISM) using a large number of Hi gas components taken from various published Hi surveys and previous Hi studies. We notice that above the length-scale (L) of 0.40 pc, there is a power-law relationship between $\sigma_{nth}$ and L. However, below 0.40 pc, there is a break in the power law, where $\sigma_{nth}$ is not significantly correlated with L. It has been observed from the Markov chain Monte Carlo (MCMC) method that for the dataset of L $\gt$ 0.40 pc, the most probable values of intensity (A) and power-law index (p) are 1.14 and 0.55, respectively. Result of p suggests that the power law is steeper than the standard Kolmogorov law of turbulence. This is due to the dominance of clouds in the cold neutral medium. This is even more clear when we separate the clouds into two categories: one for L is $\gt$ 0.40 pc and the kinetic temperature ($T_{k}$) is $\lt$250 K, which are in the cold neutral medium (CNM) and for other one where L is $\gt$0.40 pc and $T_{k}$ is between 250 and 5 000 K, which are in the thermally unstable phase (UNM). Most probable values of A and p are 1.14 and 0.67, respectively, in the CNM phase and 1.01 and 0.52, respectively, in the UNM phase. A greater number of data points is effective for the UNM phase in constructing a more accurate estimate of A and p, since most of the clouds in the UNM phase lie below 500 K. However, from the value of p in the CNM phase, it appears that there is a significant difference from the Kolmogorov scaling, which can be attributed to a shock-dominated medium.

We present a method for identifying radio stellar sources using their proper-motion. We demonstrate this method using the FIRST, VLASS, RACS-low and RACS-mid radio surveys, and astrometric information from Gaia Data Release 3. We find eight stellar radio sources using this method, two of which have not previously been identified in the literature as radio stars. We determine that this method probes distances of $\sim$90pc when we use FIRST and RACS-mid, and $\sim$250pc when we use FIRST and VLASS. We investigate the time baselines required by current and future radio sky surveys to detect the eight sources we found, with the SKA (6.7 GHz) requiring $<$3 yr between observations to find all eight sources. We also identify nine previously known and 43 candidate variable radio stellar sources that are detected in FIRST (1.4 GHz) but are not detected in RACS-mid (1.37 GHz). This shows that many stellar radio sources are variable, and that surveys with multiple epochs can detect a more complete sample of stellar radio sources.

The advent of time-domain sky surveys has generated a vast amount of light variation data, enabling astronomers to investigate variable stars with large-scale samples. However, this also poses new opportunities and challenges for the time-domain research. In this paper, we focus on the classification of variable stars from the Catalina Surveys Data Release 2 and propose an imbalanced learning classifier based on Self-paced Ensemble (SPE) method. Compared with the work of Hosenie et al. (2020), our approach significantly enhances the classification Recall of Blazhko RR Lyrae stars from 12% to 85%, mixed-mode RR Lyrae variables from 29% to 64%, detached binaries from 68% to 97%, and LPV from 87% to 99%. SPE demonstrates a rather good performance on most of the variable classes except RRab, RRc, and contact and semi-detached binary. Moreover, the results suggest that SPE tends to target the minority classes of objects, while Random Forest is more effective in finding the majority classes. To balance the overall classification accuracy, we construct a Voting Classifier that combines the strengths of SPE and Random Forest. The results show that the Voting Classifier can achieve a balanced performance across all classes with minimal loss of accuracy. In summary, the SPE algorithm and Voting Classifier are superior to traditional machine learning methods and can be well applied to classify the periodic variable stars. This paper contributes to the current research on imbalanced learning in astronomy and can also be extended to the time-domain data of other larger sky survey projects (LSST, etc.).

To explore the role environment plays in influencing galaxy evolution at high redshifts, we study $2.0\leq z<4.2$ environments using the FourStar Galaxy Evolution (ZFOURGE) survey. Using galaxies from the COSMOS legacy field with ${\rm log(M_{*}/M_{\odot})}\geq9.5$, we use a seventh nearest neighbour density estimator to quantify galaxy environment, dividing this into bins of low-, intermediate-, and high-density. We discover new high-density environment candidates across $2.0\leq z<2.4$ and $3.1\leq z<4.2$. We analyse the quiescent fraction, stellar mass and specific star formation rate (sSFR) of our galaxies to understand how these vary with redshift and environment. Our results reveal that, across $2.0\leq z<2.4$, the high-density environments are the most significant regions, which consist of elevated quiescent fractions, ${\rm log(M_{*}/M_{\odot})}\geq10.2$ massive galaxies and suppressed star formation activity. At $3.1\leq z<4.2$, we find that high-density regions consist of elevated stellar masses but require more complete samples of quiescent and sSFR data to study the effects of environment in more detail at these higher redshifts. Overall, our results suggest that well-evolved, passive galaxies are already in place in high-density environments at $z\sim2.4$, and that the Butcher–Oemler effect and SFR-density relation may not reverse towards higher redshifts as previously thought.