We investigated the kinematics and dynamics of gas structures on galaxy-cloud scales in two spiral galaxies NGC5236 (M83) and NGC4321 (M100) using CO (2$-$1) line. We utilised the FILFINDER algorithm on integrated intensity maps for the identification of filaments in two galaxies. Clear fluctuations in velocity and density were observed along these filaments, enabling the fitting of velocity gradients around intensity peaks. The variations in velocity gradient across different scales suggest a gradual and consistent increase in velocity gradient from large to small scales, indicative of gravitational collapse, something also revealed by the correlation between velocity dispersion and column density of gas structures. Gas structures at different scales in the galaxy may be organised into hierarchical systems through gravitational coupling. All the features of gas kinematics on galaxy-cloud scale are very similar to that on cloud-clump and clump-core scales studied in previous works. Thus, the interstellar medium from galaxy to dense core scales presents multi-scale/hierarchical hub-filament structures. Like dense core as the hub in clump, clump as the hub in molecular cloud, now we verify that cloud or cloud complex can be the hub in spiral galaxies. Although the scaling relations and the measured velocity gradients support the gravitational collapse of gas structures on galaxy-cloud scales, the collapse is much slower than a pure free-fall gravitational collapse.

In large-scale galaxy surveys, particularly deep ground-based photometric studies, galaxy blending was inevitable. Such blending posed a potential primary systematic uncertainty for upcoming surveys. Current deblenders predominantly depended on analytical modelling of galaxy profiles, facing limitations due to inflexible and imprecise models. We presented a novel approach, using a U-net structured transformer-based network for deblending astronomical images, which we term the CAT-deblender. It was trained using both RGB and the grz-band images, spanning two distinct data formats present in the Dark Energy Camera Legacy Survey (DECaLS) database, including galaxies with diverse morphologies in the training dataset. Our method necessitated only the approximate central coordinates of each target galaxy, sourced from galaxy detection, bypassing assumptions on neighbouring source counts. Post-deblending, our RGB images retained a high signal-to-noise peak, consistently showing superior structural similarity against ground truth. For multi-band images, the ellipticity of central galaxies and median reconstruction error for r-band consistently lie within $\pm$0.025 to $\pm$0.25, revealing minimal pixel residuals. In our comparison of deblending capabilities focused on flux recovery, our model showed a mere 1% error in magnitude recovery for quadruply blended galaxies, significantly outperforming SExtractor’s higher error rate of 4.8%. Furthermore, by cross-matching with the publicly accessible overlapping galaxy catalogs from the DECaLS database, we successfully deblended 433 overlapping galaxies. Moreover, we have demonstrated effective deblending of 63 733 blended galaxy images, randomly chosen from the DECaLS database.

Typical radio interferometer observations are performed assuming the source of radiation to be in the far-field of the instrument, resulting in a two-dimensional Fourier relationship between the observed visibilities in the aperture plane and the sky brightness distribution (over a small field of view). When near-field objects are present in an observation, the standard approach applies far-field delays during correlation, resulting in loss of signal coherence for the signal from the near-field object. In this paper, we demonstrate near-field aperture synthesis techniques using a Murchison Widefield Array observation of the International Space Station (ISS), as it appears as a bright near-field object. We perform visibility phase corrections to restore coherence across the array for the near-field object (however not restoring coherence losses due to time and frequency averaging at the correlator). We illustrate the impact of the near-field corrections in the aperture plane and the sky plane. The aperture plane curves to match the curvature of the near-field wavefront, and in the sky plane near-field corrections manifest as fringe rotations at different rates as we bring the focal point of the array from infinity to the desired near-field distance. We also demonstrate the inverse scenario of inferring the line-of-sight range of the ISS by inverting the apparent curvature of the wavefront seen by the aperture. We conclude the paper by briefly discussing the limitations of the methods developed and the near-field science cases where our approach can be exploited.

We present a comparison between the performance of a selection of source finders (SFs) using a new software tool called Hydra. The companion paper, Paper I, introduced the Hydra tool and demonstrated its performance using simulated data. Here we apply Hydra to assess the performance of different source finders by analysing real observational data taken from the Evolutionary Map of the Universe (EMU) Pilot Survey. EMU is a wide-field radio continuum survey whose primary goal is to make a deep ($20\mu$Jy/beam RMS noise), intermediate angular resolution ($15^{\prime\prime}$), 1 GHz survey of the entire sky south of $+30^{\circ}$ declination, and expecting to detect and catalogue up to 40 million sources. With the main EMU survey it is highly desirable to understand the performance of radio image SF software and to identify an approach that optimises source detection capabilities. Hydra has been developed to refine this process, as well as to deliver a range of metrics and source finding data products from multiple SFs. We present the performance of the five SFs tested here in terms of their completeness and reliability statistics, their flux density and source size measurements, and an exploration of case studies to highlight finder-specific limitations.

The latest generation of radio surveys are now producing sky survey images containing many millions of radio sources. In this context it is highly desirable to understand the performance of radio image source finder (SF) software and to identify an approach that optimises source detection capabilities. We have created Hydra to be an extensible multi-SF and cataloguing tool that can be used to compare and evaluate different SFs. Hydra, which currently includes the SFs Aegean, Caesar, ProFound, PyBDSF, and Selavy, provides for the addition of new SFs through containerisation and configuration files. The SF input RMS noise and island parameters are optimised to a 90% ‘percentage real detections’ threshold (calculated from the difference between detections in the real and inverted images), to enable comparison between SFs. Hydra provides completeness and reliability diagnostics through observed-deep ($\mathcal{D}$) and generated-shallow ($\mathcal{S}$) images, as well as other statistics. In addition, it has a visual inspection tool for comparing residual images through various selection filters, such as S/N bins in completeness or reliability. The tool allows the user to easily compare and evaluate different SFs in order to choose their desired SF, or a combination thereof. This paper is part one of a two part series. In this paper we introduce the Hydra software suite and validate its $\mathcal{D/S}$ metrics using simulated data. The companion paper demonstrates the utility of Hydra by comparing the performance of SFs using both simulated and real images.

We develop an adaptive method to automatically identify ARs from radial synoptic maps observed by SOHO/MDI and SDO/HMI, calibrate the detections between HMI and MDI data based on identified ARs flux and area and further derive a homogeneous dataset including ARs’ area and flux over the last two solar cycles. The data are compared with sunspot number, USAF/NOAA sunspot area, SMARPs and SHARPs and BARD area and flux, which show reasonable agreement. The identified ARs during the overlap period of MDI and HMI have the same areas as a whole while the AR flux based on MDI maps is about 1.36 times as large as that of HMI maps. Based on our dataset, we find strong ARs (|flux| > 1022Mx) contribute most to the difference between cycles 23 and 24 while other ARs (|flux| < 1022Mx) are similar in the two cycles in both area and flux.

In this review, we introduce our recent applications of deep learning to solar and space weather data. We have successfully applied novel deep learning methods to the following applications: (1) generation of solar farside/backside magnetograms and global field extrapolation based on them, (2) generation of solar UV/EUV images from other UV/EUV images and magnetograms, (3) denoising solar magnetograms using supervised learning, (4) generation of UV/EUV images and magnetograms from Galileo sunspot drawings, (5) improvement of global IRI TEC maps using IGS TEC ones, (6) one-day forecasting of global TEC maps through image translation, (7) generation of high-resolution magnetograms from Ca II K images, (8) super-resolution of solar magnetograms, (9) flare classification by CNN and visual explanation by attribution methods, and (10) forecasting GOES solar X-ray profiles. We present major results and discuss them. We also present future plans for integrated space weather models based on deep learning.

The signatures of small-scale features in the solar atmosphere are severely degraded by limited angular resolution of the observations. The Deep Solar ALMA Neural Network Estimator (Deep-SANNE) is trained towards synthetic observables from 3D magnetohydrodynamic simulations to recognize the small-scale dynamic features in data at limited observational resolution, and provide maps of correction factors across the field of view. The correction factors can be used to acquire deconvolved refined images with significantly improved brightness temperature contrasts, where the strength of brightening events are reproduced to an accuracy of 94.0% instead of the 43.7% at observational resolution. Deep-SANNE can also provide masks of the most probable locations with large accuracies, and estimations on the radiation formation heights in connection to the small-scale features. The Deep-SANNE refined images and estimations of radiation formation heights allow for larger accuracy and meaningful analysis of solar ALMA data.

One of the major priorities of international radio astronomy is to study the early universe through the detection of the 21 cm HI line from the epoch of reionisation (EoR). Due to the weak nature of the 21 cm signal, an important part in the detection of the EoR is removing contaminating foregrounds from our observations as they are multiple orders of magnitude brighter. In order to achieve this, sky maps spanning a wide range of frequencies and angular scales are required for calibration and foreground subtraction. Complementing the existing low-frequency sky maps, we have constructed a Southern Sky map through spherical harmonic transit interferometry utilising the Engineering Development Array 2 (EDA2), a Square Kilometre Array (SKA) low-frequency array prototype system. We use the m-mode formalism to create an all-sky map at 159 MHz with an angular resolution of 3 degrees, with data from the EDA2 providing information over +60 degrees to –90 degrees in declination. We also introduce a new method for visualising and quantifying how the baseline distribution of an interferometer maps to the spherical harmonics and discuss how prior information can be used to constrain spherical harmonic components that the interferometer is not sensitive to.

We present a detailed discussion of the implementation strategies for a recently developed w-stacking w-projection hybrid algorithm used to reconstruct wide-field interferometric images. In particular, we discuss the methodology used to deploy the algorithm efficiently on a supercomputer via use of a Message Passing Interface (MPI) k-means clustering technique to achieve efficient construction and application of non-coplanar effects. Additionally, we show that the use of conjugate symmetry can increase the w-stacking efficiency, decrease the time required to construction, and apply w-projection kernels for large data sets. We then demonstrate this implementation by imaging an interferometric observation of Fornax A from the Murchison Widefield Array (MWA). We perform an exact non-coplanar wide-field correction for 126.6 million visibilities using 50 nodes of a computing cluster. The w-projection kernel construction takes only 15 min prior to reconstruction, demonstrating that the implementation is both fast and efficient.

The increase in the amount of scientific information in heliophysics is related to both quantitative – increasing the number of high-power telescopes and the size of light receivers coupled to them, and qualitative reasons – new modes of observation, large-scale and multiple studies of the solar corona in different ranges, large-scale numerical experiments to simulate the evolution of various processes and formations, etc. The paper discusses the role and importance of methods for processing images of the solar corona, the store of obtained “raw” data and the need to access high-performance computing systems in order to obtain scientific results from the observational experiments, the need of international collaboration and access to the data in the era of increase in the amount of scientific information in heliophysics.

Traditional visual aids used in astronomy outreach (such as 2D telescope images) can fail to effectively convey complex and abstract ideas to lay audiences. With the advent of impressive CGI images widely available in film and other media, these aids may also not meet their expectations or visually engage people. To address this, we have been employing 3D holograms in lieu of 2D images for astronomy-based outreach activities both in-person (pre-pandemic), and virtually since the start of the pandemic. Here we demonstrate how the reader can make and incorporate holograms in their own virtual talks (no budget required) and present the feedback we’ve received so far.

We present the developed methods of digitization, image processing, reduction, and scientific data mining with the latest reference catalogs, which allowed us to obtain a good positional and photometric accuracy in B-band of 6,500 asteroids down to 17.5 m from the Ukrainian Virtual Observatory database of astroplates. The archive includes FON-Kyiv, FON-Kitab, FON-Dushanbe sky surveys (1981–1996) and astroplates of the Baldone and Tautenburg observatories. For some of asteroids, observations are either completely absent or not enough over the certain time interval to the moments of their official discoveries (about 300 such objects were found). Positional observations during these time scales are highly useful for a more detailed study of the dynamics and orbital parameters of asteroids as well as the obtained photometric parameters are very complementary with present-day data for studying changes in brightness and light curves.

We present an overview of Guitarra, a simulator for the Near Infrared Camera that creates scenes from catalogues of mock or real sources using the current best estimates of the instrument characteristics and the pattern on the sky of the observations.

Observations of dense stellar systems such as globular clusters (GCs) are limited in resolution by the optical aberrations induced by atmospheric turbulence (atmospheric seeing). At the example of holographic speckle imaging, we now study, to which degree image reconstruction algorithms are able to remove residual aberrations from a partial adaptive optics (AO) correction, such as delivered from ground-layer AO (GLAO) systems. Simultaneously, we study, how such algorithms benefit from being applied to pre-corrected instead of natural point-spread functions (PSFs). We find that using partial AO corrections already lowers the demands on the holography reference star by ∼3 mag, what makes more fields accessible for this technique, and also that the discrete integration times may be chosen about 2–3× longer, since the effective wavefront evolution is slowed down by removing the perturbation power.

We present independent optical spectroscopic follow-up of WGD2038-4008/GRAL2038-4008, a background quasar strongly lensed by a foreground elliptical galaxy into four images, recently discovered independently by Agnello et al. [2018, MNRAS, 479, 4345] and Krone-Martins et al. [2018, A&A, 616, L11] thanks to the exquisite spatial resolution of Gaia. The quasar images are bright (i ∼ 19 mag), thus enabling us to reach S/N > 20 for the continuum within 30 min of exposure time with the Andalucia Faint Object Spectrograph and Camera spectrograph mounted on the 2.56-m Nordic Optical Telescope. The flexible scheduling and high sensitivity delivered by Andalucia Faint Object Spectrograph and Camera provide timely redshifts and reveal the nature of the quasar images; both are essential for lensing modelling and cosmography. Our analysis shows a strong emission feature in a data gap in Agnello et al. [2018, MNRAS, 479, 4345], which can be attributed to as an interloper emission line feature from the foreground lensing galaxy, or hinting to a higher redshift of the background quasar. We discuss these two scenarios and outline possible tests to verify these scenarios.

The GaLactic and Extragalactic All-sky Murchison Widefield Array survey is a radio continuum survey at 72–231 MHz of the whole sky south of declination +30º, carried out with the Murchison Widefield Array. In this paper, we derive source counts from the GaLactic and Extragalactic All-sky Murchison data at 200, 154, 118, and 88 MHz, to a flux density limit of 50, 80, 120, and 290 mJy respectively, correcting for ionospheric smearing, incompleteness and source blending. These counts are more accurate than other counts in the literature at similar frequencies as a result of the large area of sky covered and this survey’s sensitivity to extended emission missed by other surveys. At S154 MHz > 0.5 Jy, there is no evidence of flattening in the average spectral index (α ≈ −0.8 where S ∝ vα) towards the lower frequencies. We demonstrate that the Square Kilometre Array Design Study model by Wilman et al. significantly underpredicts the observed 154-MHz GaLactic and Extragalactic All-sky Murchison counts, particularly at the bright end. Using deeper Low-Frequency Array counts and the Square Kilometre Array Design Study model, we find that sidelobe confusion dominates the thermal noise and classical confusion at v ≳ 100 MHz due to both the limited CLEANing depth and the undeconvolved sources outside the field-of-view. We show that we can approach the theoretical noise limit using a more efficient and automated CLEAN algorithm.

he Chromospheric Telescope (ChroTel) observes the entire solar disk since 2011 in three different chromospheric wavelengths: Hα, Ca ii K, and He i. The instrument records full-disk images of the Sun every three minutes in these different spectral ranges. The ChroTel observations cover the rising and decaying phase of solar cycle 24. We started analyzing the ChroTel time-series and created synoptic maps of the entire observational period in all three wavelength bands. The maps will be used to analyze the poleward migration of quiet-Sun filaments in solar cycle 24.

The UVIT ultraviolet and visual band detectors and electronics for the ASTROSAT observatory were calibrated in the vacuum laboratory at the University of Calgary. This work was supported by the Canadian Space Agengy and carried out prior to integration with the UVIT optical assembly and the ASTROSAT spacecraft. The multiband (X-ray, ultraviolet and optical) ASTROSAT observatory was successfully launched by the Indian Space Research Organization on Sept. 28, 2015, with subsequent in-orbit verification and ongoing calibration activities. Here we discuss the current issues of calibrating the UVIT data, such as distortion corrections, and how the laboratory data is being used to address these issues.

With the rapid development of telescopes, both temporal cadence and the spatial resolution of observations are increasing. This in turn generates vast amount of data, which can be efficiently searched only with automated detections in order to derive the features of interest in the observations. A number of automated detection methods and algorithms have been developed for solar activities, based on the image processing and machine learning techniques. In this paper, after briefly reviewing some automated detection methods, we describe our efficient and versatile automated detection method for solar filaments. It is able not only to recognize filaments, determine the features such as the position, area, spine, and other relevant parameters, but also to trace the daily evolution of the filaments. It is applied to process the full disk Hα data observed in nearly three solar cycles, and some statistic results are presented.