This project will explore designs for a simple ExecutionPlanner prototype that is capable of executing caesar source extractor tasks and using this to enable the caesar-rest system to make use of remote compute platforms managed by other projects.

We report diffuse extended radio-continuum emission spatially coinciding with the IR source, WISEA J094409.17-751012.8, and a semi-variable star, V687 Carinae. We use 944 MHz radio data from the large-scale Evolutionary Map of the Universe (EMU) survey to analyse this diffuse emission (EMU J094412-751016), which we nickname 'Anglerfish'. We investigate if the spatially correlated infrared (IR) source, WISEA J094409.17-751012.8, is physically related to Anglerfish. The IR colours of WISEA J094409.17-751012.8 are indicative of an elliptical galaxy, raising the possibility that Anglerfish may belong to the newly discovered class of extragalactic radio sources known as Odd Radio Circles (ORCs) with WISEA J094409.17-751012.8 as the host galaxy. We also investigate the possibility that Anglerfish is physically related to the star, V687 Carinae, and whether it may be a remnant from a previous epoch of stellar mass-loss. We determine that a physical association between the radio emission and the star is unlikely due to the star's weak stellar winds compared to the theoretical expansion velocity of the 'shell'. It is possible that Anglerfish may be a Galactic high-latitude supernova remnant; however, we find that the observed size and luminosity are not consistent with this scenario. We also investigate the ORC scenario, which we deem the most likely scenario based on the Anglerfish's observed properties such as size, brightness, lack of other frequency detections, and possible host galaxy identification. We therefore propose Anglerfish as an ORC candidate, but note that additional radio and optical observations are vital to further constrain the properties and confirm this classification.

IntroductionGenerative Artificial Intelligence (GenAI) is rapidly transforming scientific workflows, yet empirical data on its adoption within national research infrastructures remain scarce.MethodsThis study presents the results of a comprehensive survey conducted at the Italian National Institute for Astrophysics (INAF) in spring 2025, collecting 371 responses (∼20% of the workforce). Data were analyzed quantitatively, including user segmentation through K-Means clustering, while qualitative feedback highlighted user attitudes and ethical concerns.ResultsOur analysis reveals widespread adoption, with 50% of staff using GenAI weekly, primarily for coding and linguistic refinement. We identify a critical shift in usage patterns: AI is valued more for speed (efficiency) than for quality improvement, acting as an accelerator rather than a quality enhancer. The clustering analysis identifies three user personas: Enthusiasts, Pragmatists, and Skeptics, highlighting a hidden digital divide where 18% of researchers invest personal funds to access premium tools (“Shadow IT”), creating equity concerns. Furthermore, we find a correlation between AI literacy and user satisfaction, suggesting that skepticism may stem from lack of training.DiscussionThe study concludes that while the community is predominantly eager to adopt these tools, the current absence of formal guidelines poses risks regarding data privacy and scientific integrity. We provide evidence-based recommendations for institutional governance, prioritizing specific technical training and centralized provision of suitable tools over too restrictive policies.

We present a study of ionised hydrogen ($\textrm{H}\scriptstyle\mathrm{II}$) regions in the Galactic Plane using data from the SARAO MeerKAT Galactic Plane Survey (SMGPS). The SMPGS is a wide-field, wide-band 1.3 GHz radio continuum survey ($251^\circ \leq l \leq 358^\circ$ and $2^\circ \leq l \leq 61^\circ$ at $\quad |b| \leq 1^\circ.5$) that has enabled us to trace the diffuse emission enveloping recently formed massive stars. Our multifrequency synthesis images reveal faint and extended emission that was previously overlooked by $\textrm{H}\scriptstyle\mathrm{II}$ region surveys. We report the distances and Lyman-photon flux ($N_{\mathrm{Ly}}$) measurements for 1,327 Galactic $\textrm{H}\scriptstyle\mathrm{II}$ regions from which we characterise the spectral types for candidate ionising stars. The spectral types range from B3 to O4. The typical stellar spectral type responsible for ionisation is the B0, which constitutes about $\text{40 %}$ of our catalogue, corresponding to a mean $\log(N_{\mathrm{Ly}}) = 47.5\ \mathrm{s}^{-1}$. Moreover, as a result of the lack of radio recombination line (RRL) velocity measurements for faint $\textrm{H}\scriptstyle\mathrm{II}$ regions, we identify the effective completeness limit at $\log(N_{\mathrm{Ly}}) \approx 46.8\ \mathrm{s}^{-1}$. The multiwavelength approach reveals that the physical radius at 1.3 GHz and in the mid-infrared are well correlated with a slope of $1.15 \pm 0.02$. We find clear power-law relations between $N_{\mathrm{Ly}}$ and physical radius, and an inverse correlation between electron density and radius ($n_{\rm e} \propto R^{-0.73}$). However, no significant correlation is observed between the $N_{\mathrm{Ly}}$ and Galactocentric distance, suggesting that the observed trends are governed primarily by local star-forming environments rather than large-scale Galactic gradients.

Evolved massive stars dominate stellar feedback and drive the chemodynamical evolution of their host galaxies. Therefore, the identification of new evolved massive stars is an essential task that will soon become impractical, as next-generation observing facilities deliver large-area photometric surveys at an unprecedented scale. The need for automated photometric classifiers that can efficiently exploit unlabelled data (which will be the most abundant in these new surveys) is thus evident. In this work, we explore the potential of semi-supervised learning techniques to improve the classification of evolved massive stars by leveraging unlabelled data. Our findings show that cluster-based semi-supervised methods outperform supervised classifiers when only a few labelled examples are available.

Next-generation radio astronomy surveys are producing millions of resolved sources, but robust morphology analysis remains difficult across heterogeneous telescopes and imaging pipelines. We present STRADAViT, a self-supervised Vision Transformer continued-pretraining framework for transferable radio astronomy image encoders. STRADAViT combines a mixed-survey pretraining dataset, radio astronomy-aware view generation, and controlled continued pretraining through reconstruction-only, contrastive-only, and two-stage branches. Pretraining uses 512x512 radio astronomy cutouts from MeerKAT, ASKAP, LOFAR/LoTSS, and SKA data. We evaluate transfer with linear probing and fine-tuning on three morphology benchmarks: MiraBest, LoTSS DR2, and Radio Galaxy Zoo. Relative to the initialization used for continued pretraining, the best two-stage STRADAViT models improve Macro-F1 in all reported linear-probe settings and in most fine-tuning settings, with the largest gain on RGZ DR1. Relative to strong DINOv2 baselines, gains are selective but remain positive on LoTSS DR2 and RGZ DR1 under linear probing, and on MiraBest and RGZ DR1 under fine-tuning. A targeted DINOv2-initialized HCL ablation further shows that the adaptation recipe is not specific to a single starting point. The released STRADAViT checkpoint remains the preferred model because it offers competitive transfer at lower token count and downstream cost than the DINOv2-based alternative. These results show that radio astronomy-aware view generation and staged continued pretraining provide a stronger starting point than out-of-the-box Vision Transformers for radio astronomy transfer.

ABSTRACT We present a study of ionized hydrogen ($m {H}\, \rm {{\small II}}$) regions in the Galactic Plane using data from the SARAO MeerKAT Galactic Plane Survey (SMGPS). The SMPGS is a wide-field, wide-band 1.3 GHz radio continuum survey ($251^\circ \le l \le 358^\circ$ and $2^\circ \le l \le 61^\circ$ at $|b| \le 1^\circ .5$) that has enabled us to trace the diffuse emission enveloping recently formed massive stars. Our multifrequency synthesis images reveal faint and extended emission that was previously overlooked by $m {H}\, \rm {{\small II}}$ region surveys. We report the distances and Lyman-photon flux ($N_{\mathrm{Ly}}$) measurements for 1327 Galactic $m {H}\, \rm {{\small II}}$ regions from which we characterize the spectral types for candidate ionizing stars. The spectral types range from B2 to O4. The typical stellar spectral type responsible for ionization is the B0, which constitutes about 16% of our catalogue, corresponding to a mean $\log (N_{\mathrm{Ly}}) = 47.5\ \mathrm{s}^{-1}$. Moreover, as a result of the lack of radio recombination line (RRL) velocity measurements for faint $m {H}\, \rm {{\small II}}$ regions, we identify the effective completeness limit at $\log (N_{\mathrm{Ly}}) \approx 47.6\ \mathrm{s}^{-1}$. The multiwavelength approach reveals that the physical radius at 1.3 GHz and in the mid-infrared are well correlated with a slope of $1.15 \pm 0.02$. We find clear power-law relations between $N_{\mathrm{Ly}}$ and physical radius, and an inverse correlation between electron density and radius ($n_{\rm e} \propto R^{-0.73}$). However, no significant correlation is observed between the $N_{\mathrm{Ly}}$ and Galactocentric distance, suggesting that the observed trends are governed primarily by local star-forming environments rather than large-scale Galactic gradients.

Massive early-type (B/A) stars and ultracool dwarfs (UCDs) represent two distinct regimes in which ordered, large-scale magnetospheres are observed. In rapidly rotating massive stars, incoherent radio emission is explained by the centrifugal breakout (CBO) mechanism: plasma confined within the rigidly rotating magnetosphere accumulates beyond the co-rotation radius, where centrifugal forces trigger breakout events and magnetic reconnection, generating non-thermal electrons that produce incoherent gyro-synchrotron emission. Empirically, the radio luminosity correlates with the power released by CBO events, establishing a clear link between stellar rotation, magnetic confinement, and radio output. In UCDs, persistent non-thermal radio emission exhibits similar luminosity trends to those of massive magnetic stars, despite the absence of strong stellar winds. This similarity suggests that a CBO-like process may also operate in these fully convective, low-mass objects, though the plasma source and acceleration mechanisms remain uncertain. In both classes, coherent electron cyclotron maser emission (ECME), characterized by strong polarization and rotational modulation, is observed, indicating common magnetospheric processes analogous to planetary auroral emission. The Square Kilometre Array (SKA) will be able to deeply observe about 70% of the sky. We expect to observe ∼ 1000 UCDs, enabling better statistical analysis of their emission and a test of the CBO hypothesis.

We present a catalogue of compact sources detected in the SARAO (South African Radio Astronomy Observatory) MeerKAT 1.3 GHz Galactic Plane Survey (SMGPS). We extract 510 599 compact sources, with areas less than five 8 arcsec beams, from the survey maps covering the regions 252 degrees < l < 358 degrees and 2 degrees < l < 61 degrees at | b| <= 1 . 5 degrees, which have an angular resolution of 8 arcsec and a sensitivity of similar to 10-30 mu Jy beam(-1). In this paper, we describe the source identification and characterization methods, present the quality assurance of the catalogue, explore the nature of the catalogue sources, and present initial science highlights. We limit our catalogue to sources with a signal-to-noise ratio >= 5, as the catalogue is similar to 90percent complete, and has a false positive rate of less than 1 per cent at this threshold. The bulk of the catalogue sources are previously unknown to the literature, with the majority of unknown sources at sub-mJy levels. Initial science highlights from the catalogue include the detection of 213 radio quiet Wide-field Infrared Survey Explorer (WISE) HII region candidates, previously undetected in radio continuum studies. We show images that compare the SMGPS compact sources to CORNISH (Coordinated Radio and Infrared Survey for High-Mass Star Formation) ultracompact HII regions, thus highlighting the sensitivity and unprecedented uv-coverage of the SMGPS, and the potential synergy of the SMGPS with other surveys.

One of the fundamental requirements for studying and understanding Galactic structure and massive star formation is accurate distances to H II regions. However, most distance assignments are hampered by kinematic distance ambiguity (KDA), sparse parallax measurements, and the large number of radio continuum sources lacking velocity and distance measurements. We present a kinematic distance determination method via cloud association, linking H II regions from the Wide-field Infrared Survey Explorer (WISE) catalogue and the South African Radio Astronomy Observatory (SARAO) MeerKAT Galactic Plane Survey (SMGPS) with molecular clouds from the Structure, Excitation and Dynamics of the Inner Galactic Interstellar Medium (SEDIGISM) 13CO (2-1) survey. The associations are established through spatial overlap and velocity coherence, and the molecular cloud velocity and distance are then assigned to the associated H II region. The method yields 741 H II regions with adopted CO-based systemic velocities, of which 640 have reliable kinematic distances based on the SEDIGISM distance reliability criteria. We validate the method using 329 H II regions with independent radio recombination line (RRL) velocities, finding excellent agreement with a median absolute velocity difference of 3.46km/s. Our analysis resolves ambiguous velocities for 40 H II regions with multiple WISE RRL velocity measurements. Compared to the unassociated clouds, the associated molecular clouds exhibit significantly higher masses, gas surface densities, linewidths, star formation efficiencies and dense gas fractions, and slightly lower virial parameters. This work provides a large, homogeneously-derived catalogue of H II region distances and establishes a framework for further studying massive star formation and Galactic structure in general.

B[e] supergiants represent a rare class of luminous, evolved massive stars surrounded by dusty circumstellar disks. Since their intense UV fields were long thought to sterilize their surroundings, molecular detections beyond carbon monoxide have remained elusive, leaving their chemical reservoirs largely unexplored. Whether these environments can sustain a complex molecular chemistry is a fundamental question with significant astrochemical implications. Here we report the detection of chemically rich molecular gas surrounding the B[e] supergiant HD 87643, using Atacama Large Millimeter/submillimeter Array observations. Our data reveal the presence of the sulfur oxides SO and SO2 and other sulfur-bearing species, marking the first detection of these molecules in an early-type evolved massive star. We find a high fractional abundance of SO2 relative to H2, which our chemical modeling can reproduce in timescales as short as similar to 104 yr in an oxygen-rich environment. These results indicate that the detected molecules trace a short-lived, rapidly evolving phase of out-of-equilibrium chemistry. Furthermore, we measure an anomalously low 32SO/33SO, which we attribute to mass-independent fractionation driven by intense photochemistry. This mechanism mirrors processes proposed to explain the 33S excesses in the atmosphere of the Archaean Earth. Our findings suggest that B[e] supergiants could serve as unique laboratories for studying sulfur chemistry under extreme radiation conditions, opening potential avenues to investigate the fractionation processes that shaped the isotopic signatures found in the early geological record.

The advent of next-generation radio telescopes is set to transform radio astronomy by producing massive data volumes that challenge traditional processing methods. Deep learning techniques have shown strong potential in automating radio analysis tasks, yet are often constrained by the limited availability of large annotated datasets. Recent progress in self-supervised learning has led to foundational radio vision models, but adapting them for new tasks typically requires coding expertise, limiting their accessibility to a broader astronomical community. Text-based AI interfaces offer a promising alternative by enabling task-specific queries and example-driven learning. In this context, large language models (LLMs), with their remarkable zero-shot capabilities, are increasingly used in scientific domains. However, deploying large-scale models remains resource-intensive, and there is a growing demand for AI systems that can reason over both visual and textual data in astronomical analysis. This study explores small-scale vision-language models (VLMs) as AI assistants for radio astronomy, combining LLM capabilities with vision transformers. We fine-tuned the LLaVA VLM on a dataset of 59k radio images from multiple surveys, enriched with 38k image-caption pairs from the literature. The fine-tuned models show clear improvements over base models in radio-specific tasks, achieving similar to 30% F1-score gains in extended source detection, but they underperform vision-only classifiers and exhibit similar to 20% drop on general multimodal tasks. Inclusion of caption data and LoRA fine-tuning enhances instruction following and helps recover similar to 10% accuracy on multimodal benchmarks (e.g., ChartQA/DocVQA). This work lays the foundation for future advancements in radio VLMs, highlighting their potential and limitations, such as the need for better multimodal alignment, higher-quality datasets, and mitigation of catastrophic forgetting.

We present a catalogue of extended radio sources from the SARAO MeerKAT Galactic Plane Survey (SMGPS). Compiled from 56 survey tiles and covering approximately 500 deg^2 across the first, third, and fourth Galactic quadrants, the catalogue includes 16534 extended and diffuse sources with areas larger than 5 synthesised beams. Of them, 3891 (24% of the total) are confidently associated with known Galactic radio-emitting objects in the literature, such as HII regions, supernova remnants, planetary nebulae, luminous blue variables, and Wolf-Rayet stars. A significant fraction of the remaining sources, 5462 (33%), are candidate extragalactic sources, while 7181 (43%) remain unclassified. Isolated radio filaments are excluded from the catalogue. The diversity of extended sources underscores MeerKAT's contribution to the completeness of censuses of Galactic radio emitters, and its potential for new scientific discoveries. For the catalogued sources, we derived basic positional and morphological parameters, as well as flux density estimates, using standard aperture photometry. This paper describes the methods followed to generate the catalogue from the original SMGPS tiles, detailing the source extraction, characterisation, and crossmatching procedures. Additionally, we analyse the statistical properties of the catalogued populations

We present Evolutionary Map of the Universe Search Engine (EMUSE), a tool designed for searching specific radio sources within the extensive datasets of the Evolutionary Map of the Universe (EMU) survey, with potential applications to other Big Data challenges in astronomy. Built on a multimodal approach to radio source classification and retrieval, EMUSE fine-tunes the OpenCLIP model on curated radio galaxy datasets. Leveraging the power of foundation models, our work integrates visual and textual embeddings to enable efficient and flexible searches within large radio astronomical datasets. We fine-tune OpenCLIP using a dataset of 2 900 radio galaxies, encompassing various morphological classes, including FR-I, FR-II, FR-x, R-type, and other rare and peculiar sources. The model is optimised using adapter-based fine-tuning, ensuring computational efficiency while capturing the unique characteristics of radio sources. The fine-tuned model is then deployed in the EMUSE, allowing for seamless image and text-based queries over the EMU survey dataset. Our results demonstrate the model's effectiveness in retrieving and classifying radio sources, particularly in recognising distinct morphological features. However, challenges remain in identifying rare or previously unseen radio sources, highlighting the need for expanded datasets and continuous refinement. This study showcases the potential of multimodal machine learning in radio astronomy, paving the way for more scalable and accurate search tools in the field. The search engine is accessible at https://askap-emuse.streamlit.app/ and can be used locally by cloning the repository at https://github.com/Nikhel1/EMUSE.

We present the serendipitous radio-continuum discovery of a likely Galactic supernova remnant (SNR) G305.4–2.2. This object displays a remarkable circular symmetry in shape, making it one of the most circular Galactic SNRs known. Nicknamed Teleios due to its symmetry, it was detected in the new Australian Square Kilometre Array Pathfinder (ASKAP) Evolutionary Map of the Universe (EMU) radio–continuum images with an angular size of 1 320 $^{\prime\prime}$ $\times$ 1 260 $^{\prime\prime}$ and PA = 0 $^\circ$ . While there is a hint of possible H $\alpha$ and gamma-ray emission, Teleios is exclusively seen at radio–continuum frequencies. Interestingly, Teleios is not only almost perfectly symmetric, but it also has one of the lowest surface brightnesses discovered among Galactic SNRs and a steep spectral index of $\alpha$ =–0.6 $\pm$ 0.3. Our best estimates from H i studies and the $\Sigma$ –D relation place Teleios as a type Ia SNR at a distance of either $\sim$ 2.2 kpc (near-side) or $\sim$ 7.7 kpc (far-side). This indicates two possible scenarios, either a young (under 1 000 yr) or a somewhat older SNR (over 10 000 yr). With a corresponding diameter of 14/48 pc, our evolutionary studies place Teleios at the either early or late Sedov phase, depending on the distance/diameter estimate. However, our modelling also predicts X-ray emission, which we do not see in the present generation of eROSITA images. We also explored a type Iax explosion scenario that would point to a much closer distance of $\lt$ 1 kpc and Teleios size of only $\sim$ 3.3 pc, which would be similar to the only known type Iax remnant SN1181. Unfortunately, all examined scenarios have their challenges, and no definitive Supernova (SN) origin type can be established at this stage. Remarkably, Teleios has retained its symmetrical shape as it aged even to such a diameter, suggesting expansion into a rarefied and isotropic ambient medium. The low radio surface brightness and the lack of pronounced polarisation can be explained by a high level of ambient rotation measure (RM), with the largest RM being observed at Teleios’s centre.

We present the Evolutionary Map of the Universe (EMU) survey conducted with the Australian Square Kilometre Array Pathfinder (ASKAP). EMU aims to deliver the touchstone radio atlas of the southern hemisphere. We introduce EMU and review its science drivers and key science goals, updated and tailored to the current ASKAP five-year survey plan. The development of the survey strategy and planned sky coverage is presented, along with the operational aspects of the survey and associated data analysis, together with a selection of diagnostics demonstrating the imaging quality and data characteristics. We give a general description of the value-added data pipeline and data products before concluding with a discussion of links to other surveys and projects and an outline of EMU's legacy value.

Context. Sensitive radio continuum data could bring the number of known supernova remnants (SNRs) in the Galaxy more in line with what is expected. Due to confusion in the Galactic plane, however, faint SNRs can be challenging to distinguish from brighter H II regions and filamentary radio emission. Aims. We exploited new 1.3 GHz SARAO MeerKAT Galactic Plane Survey (SMGPS) radio continuum data, which cover 251 degrees <= & ell; <= 358 degrees and 2 degrees <= & ell; <= 61 degrees at | b | <= 1.5 degrees, to search for SNR candidates in the Milky Way disk. Methods. We also used mid-infrared data from the Spitzer GLIMPSE, Spitzer MIPSGAL, and WISE surveys to help identify SNR candidates. These candidates are sources of extended radio continuum emission that lack mid-infrared counterparts, are not known as H II regions in the WISE Catalog of Galactic H II Regions, and have not been previously identified as SNRs. Results. We locate 237 new Galactic SNR candidates in the SMGPS data. We also identify and confirm the expected radio morphology for 201 objects classified in the literature as SNRs and 130 previously identified SNR candidates. The known and candidate SNRs have similar spatial distributions and angular sizes. Conclusions. The SMGPS data allowed us to identify a large population of SNR candidates that can be confirmed as true SNRs using radio polarization measurements or by deriving radio spectral indices. If the 237 candidates are confirmed as true SNRs, it would approximately double the number of known Galactic SNRs in the survey area, alleviating much of the discrepancy between the known and expected populations.

The upcoming Square Kilometer Array (SKA) telescope marks a significant step forward in radio astronomy, presenting new opportunities and challenges for data analysis. Traditional visual models pretrained on optical photography images may not perform optimally on radio interferometry images, which have distinct visual characteristics. Self-Supervised Learning (SSL) offers a promising approach to address this issue, leveraging the abundant unlabeled data in radio astronomy to train neural networks that learn useful representations from radio images. This study explores the application of SSL to radio astronomy, comparing the performance of SSL-trained models with that of traditional models pretrained on natural images, evaluating the importance of data curation for SSL, and assessing the potential benefits of selfsupervision to different domain-specific radio astronomy datasets. Our results indicate that, SSL-trained models achieve significant improvements over the baseline in several downstream tasks, especially in the linear evaluation setting; when the entire backbone is fine-tuned, the benefits of SSL are less evident but still outperform pretraining. These findings suggest that SSL can play a valuable role in efficiently enhancing the analysis of radio astronomical data. The trained models and code is available at: https://github.com/dr4thmos/solo- learn-radio.

The study of transient phenomena in a multimessenger context is expected to remain a major pillar of astrophysical discovery in the decades ahead. Supernovae, Kilonovae, Black-Hole formation, Novae, GRBs, and tidal disruption events are prime examples, as their earliest phases link electromagnetic radiation to gravitational waves, neutrinos, and high-energy emission. Yet, the physics connecting these messengers unfolds within minutes to hours, while traditional surveys revisit the same region of the sky on the scale of days/weeks, missing when the event begins. Current survey facilities excel at answering what happened and how often, but essentially fail in addressing how it happened and how it couples to gravitational waves, neutrinos, or high-energy emission. Continuous wide-area optical monitoring, as proposed here, removes this limitation. The traditional approach, where a GW or neutrino alert triggers electromagnetic follow-up, is now complemented, and sometimes reversed: early electromagnetic discoveries can prompt searches for weaker gravitational waves or neutrino signals that would otherwise be missed. In the Einstein Telescope era, wide-field optical monitoring will allow us to find the optical counterparts of gravitational-wave events and understand their physics. At the same time, a telescope capable of continuous monitoring provides immediate scientific value for planetary defense, space-debris tracking, stellar variability, exoplanets transit monitoring, accretion-driven activity, and when we step into a new observational territory, the true discoveries are often the ones we did not expect. In this vision, continuous time-domain astronomy does not replace classical surveys: it completes them by supplying the missing temporal dimension. Follow-up observations remain essential, but they now begin at the physical onset of the event rather than after its evolution is underway.