Multi-messenger observations of the transient sky to detect cosmic explosions and counterparts of gravitational wave mergers critically rely on orbiting wide-FoV telescopes to cover the wide range of wavelengths where atmospheric absorption and emission limit the use of ground facilities. Thanks to continuing technological improvements, miniaturised space instruments operating as distributed-aperture constellations are offering new capabilities for the study of high-energy transients to complement ageing existing satellites. In this paper we characterise the performance of the upcoming joint SpIRIT and HERMES-TP/SP constellation for the localisation of high-energy transients through triangulation of signal arrival times. SpIRIT is an Australian technology and science demonstrator satellite designed to operate in a low-Earth Sun-synchronous Polar orbit that will augment the science operations for the equatorial HERMES-TP/SP constellation. In this work we simulate the improvement to the localisation capabilities of the HERMES-TP/SP constellation when SpIRIT is included in an orbital plane nearly perpendicular (inclination = 97.6°) to the HERMES-TP/SP orbits. For the fraction of GRBs detected by three of the HERMES satellites plus SpIRIT, we find that the combined constellation is capable of localising 60% of long GRBs to within ${\sim}30\,\textrm{deg}^{2}$ on the sky, and 60% of short GRBs within ${\sim}1850\,\textrm{deg}^{2}$ ($1\sigma$ confidence regions), though it is beyond the scope of this work to characterise or rule out systematic uncertainty of the same order of magnitude. Based purely on statistical GRB localisation capabilities (i.e., excluding systematic uncertainties and sky coverage), these figures for long GRBs are comparable to those reported by the Fermi Gamma Burst Monitor instrument. These localisation statistics represents a reduction of the uncertainty for the burst localisation region for both long and short GRBs by a factor of ${\sim}5$ compared to the HERMES-TP/SP alone. Further improvements by an additional factor of 2 (or 4) can be achieved by launching an additional 4 (or 6) SpIRIT-like satellites into a Polar orbit, respectively, which would both increase the fraction of sky covered by multiple satellite elements, and also enable localisation of ${\geq} 60\%$ of long GRBs to within a radius of ${\sim}1.5^{\circ}$ (statistical uncertainty) on the sky, clearly demonstrating the value of a distributed all-sky high-energy transient monitor composed of nano-satellites.

Pulsars have been studied extensively over the last few decades and have proven instrumental in exploring a wide variety of physics. Discovering more pulsars emitting at low radio frequencies is crucial to further our understanding of spectral properties and emission mechanisms. The Murchison Widefield Array Voltage Capture System (MWA VCS) has been routinely used to study pulsars at low frequencies and discover new pulsars. The MWA VCS offers the unique opportunity of recording complex voltages from all individual antennas (tiles), which can be off-line beamformed or correlated/imaged at millisecond time resolution. Devising imaged-based methods for finding pulsar candidates, which can be verified in beamformed data, can accelerate the complete process and lead to more pulsar detections. Image-based searches for pulsar candidates can reduce the number of tied-array beams required, increasing compute resource efficiency. Despite a factor of $\sim$4 loss in sensitivity, searching for pulsar candidates in images from the MWA VCS, we can explore a larger parameter space, potentially leading to discoveries of pulsars missed by high-frequency surveys such as steep spectrum pulsars, exotic binary systems, or pulsars obscured in high-time resolution time series data by propagation effects. Image-based searches are also essential to probing parts of parameter space inaccessible to traditional beamformed searches with the MWA (e.g. at high dispersion measures). In this paper we describe the innovative approach and capability of dual-processing MWA VCS data, that is forming 1-s visibilities and sky images, finding pulsar candidates in these images, and verifying by forming tied-array beam. We developed and tested image-based methods of finding pulsar candidates, which are based on pulsar properties such as steep spectral index, polarisation and variability. The efficiency of these methodologies has been verified on known pulsars, and the main limitations explained in terms of sensitivity and low-frequency spectral turnover of some pulsars. No candidates were confirmed to be a new pulsar, but this new capability will now be applied to a larger subset of observations to accelerate pulsar discoveries with the MWA and potentially speed up future searches with the SKA-Low.

We use the MaNGA integral field spectroscopic survey of low-redshift galaxies to compare the stellar populations of the bulge and disc components, identified from their Sérsic profiles, for various samples of galaxies. Bulge-dominated regions tend to be more metal-rich and have slightly older stellar ages than their associated disc-dominated regions. The metallicity difference is consistent with the deeper gravitational potential in bulges relative to discs, which allows bulges to retain more of the metals produced by stars. The age difference is due to star formation persisting longer in discs relative to bulges. Relative to galaxies with lower stellar masses, galaxies with higher stellar masses tend to have bulge-dominated regions that are more metal-rich and older (in light-weighted measurements) than their disc-dominated regions. This suggests high-mass galaxies quench from the inside out, while lower-mass galaxies quench across the whole galaxy simultaneously. Early-type galaxies tend to have bulge-dominated regions the same age as their disc-dominated regions, while late-type galaxies tend to have disc-dominated regions significantly younger than their bulge-dominated regions. Central galaxies tend to have a greater metallicity difference between their bulge-dominated regions and disc-dominated regions than satellite galaxies at similar stellar mass. This difference may be explained by central galaxies being subject to mergers or extended gas accretion bringing new, lower-metallicity gas to the disc, thereby reducing the average metallicity and age of the stars; quenching of satellite discs may also play a role.

The amount and complexity of data delivered by modern galaxy surveys has been steadily increasing over the past years. New facilities will soon provide imaging and spectra of hundreds of millions of galaxies. Extracting coherent scientific information from these large and multi-modal data sets remains an open issue for the community and data-driven approaches such as deep learning have rapidly emerged as a potentially powerful solution to some long lasting challenges. This enthusiasm is reflected in an unprecedented exponential growth of publications using neural networks, which have gone from a handful of works in 2015 to an average of one paper per week in 2021 in the area of galaxy surveys. Half a decade after the first published work in astronomy mentioning deep learning, and shortly before new big data sets such as Euclid and LSST start becoming available, we believe it is timely to review what has been the real impact of this new technology in the field and its potential to solve key challenges raised by the size and complexity of the new datasets. The purpose of this review is thus two-fold. We first aim at summarising, in a common document, the main applications of deep learning for galaxy surveys that have emerged so far. We then extract the major achievements and lessons learned and highlight key open questions and limitations, which in our opinion, will require particular attention in the coming years. Overall, state-of-the-art deep learning methods are rapidly adopted by the astronomical community, reflecting a democratisation of these methods. This review shows that the majority of works using deep learning up to date are oriented to computer vision tasks (e.g. classification, segmentation). This is also the domain of application where deep learning has brought the most important breakthroughs so far. However, we also report that the applications are becoming more diverse and deep learning is used for estimating galaxy properties, identifying outliers or constraining the cosmological model. Most of these works remain at the exploratory level though which could partially explain the limited impact in terms of citations. Some common challenges will most likely need to be addressed before moving to the next phase of massive deployment of deep learning in the processing of future surveys; for example, uncertainty quantification, interpretability, data labelling and domain shift issues from training with simulations, which constitutes a common practice in astronomy.

We present a catalogue of over 7000 sources from the GLEAM survey which have significant structure on sub-arcsecond scales at 162 MHz. The compact nature of these sources was detected and quantified via their Interplanetary Scintillation (IPS) signature, measured in interferometric images from the Murchison Widefield Array. The advantage of this approach is that all sufficiently compact sources across the survey area are included down to a well-defined flux density limit. The survey is based on ${\sim}250\times 10\hbox{-}\mathrm{min}$ observations, and the area covered is somewhat irregular, but the area within $1\,\mathrm{h}<\mathrm{RA}<11\,\mathrm{h}$ ; $-10^\circ<\mathrm{Decl.}<+20^\circ$ is covered entirely, and over 85% of this area has a detection limit for compact structure below 0.2 Jy. 7839 sources clearly showing IPS were detected ( ${>}5\sigma$ confidence), with a further 5550 tentative ( ${>}2\sigma$ confidence) detections. Normalised Scintillation Indices (NSI; a measure of the fraction of flux density coming from a compact component) are reported for these sources. Robust and informative upper limits on the NSI are reported for a further 31081 sources. This represents the largest survey of compact sources at radio frequencies ever undertaken.