Astronomy and Computing最新文献

{"title":"Experiences of commercial supercomputing in radio astronomy data processing","authors":"I.P. Kemp ,&nbsp;S.J. Tingay ,&nbsp;S.D. Midgely ,&nbsp;D.A. Mitchell","doi":"10.1016/j.ascom.2025.101013","DOIUrl":"10.1016/j.ascom.2025.101013","url":null,"abstract":"<div><div>The ongoing exponential growth of computational power, and the growth of the commercial High Performance Computing (HPC) industry, has led to a point where ten commercial systems currently exceed the performance of the highest-used HPC system in radio astronomy in Australia, and one of these exceeds the expected requirements of the Square Kilometre Array (SKA) Science Data Processors.</div><div>In order to explore implications of this emerging change in the HPC landscape for radio astronomy, we report results from a survey conducted via semi-structured interviews with 14 Australian scientists and providers with experience of commercial HPC in astronomy and similar data intensive fields. We supplement these data with learnings from two earlier studies in which we investigated the application of commercial HPC to radio astronomy data processing, using cases with very different data and processing considerations.</div><div>We use the established qualitative research approach of thematic analysis to extract key messages from our interviews. We find that commercial HPC can provide major advantages in accessibility and availability, and may contribute to increasing researchers’ career productivity. Significant barriers exist, however, including the need for access to increased expertise in systems programming and parallelization, and a need for recognition in research funding. We comment on potential solutions to these issues.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101013"},"PeriodicalIF":1.8,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Encapsulating textual contents into a MOC data structure for advanced applications","authors":"Giuseppe Greco ,&nbsp;Thomas Boch ,&nbsp;Pierre Fernique ,&nbsp;Manon Marchand ,&nbsp;Mark Allen ,&nbsp;Francois-Xavier Pineau ,&nbsp;Matthieu Baumann ,&nbsp;Marco Molinaro ,&nbsp;Roberto De Pietri ,&nbsp;Marica Branchesi ,&nbsp;Steven Schramm ,&nbsp;Gergely Dálya ,&nbsp;Elahe Khalouei ,&nbsp;Barbara Patricelli ,&nbsp;Giulia Stratta","doi":"10.1016/j.ascom.2025.101014","DOIUrl":"10.1016/j.ascom.2025.101014","url":null,"abstract":"<div><h3>Context:</h3><div>The Multi-Order Coverage map (MOC) is a widely adopted standard promoted by the International Virtual Observatory Alliance (IVOA) to support data sharing and interoperability within the Virtual Observatory (VO) ecosystem. This hierarchical data structure efficiently encodes and visualizes irregularly shaped regions of the sky, enabling applications such as cross-matching large astronomical catalogs, visualizing multi-wavelength and multi-messenger surveys, and facilitating collaborative research through seamless interoperability in big-data-driven exploration.</div></div><div><h3>Aims:</h3><div>This study aims to explore potential enhancements to the MOC data structure by encapsulating textual descriptions and semantic embeddings into sky regions. Specifically, we introduce “Textual MOCs”, in which textual content is encapsulated, and “Semantic MOCs” that transform textual content into semantic embeddings. These enhancements are designed to enable advanced operations such as similarity searches and complex queries and to integrate with generative artificial intelligence (GenAI) tools to improve context-aware interactions and response accuracy in astronomical data analysis, and support agent-based applications.</div></div><div><h3>Method:</h3><div>We experimented with Textual MOCs by annotating detailed descriptions directly into the MOC sky regions, enriching the maps with contextual information suitable for interactive learning tools. For Semantic MOCs, we converted the textual content into semantic embeddings, numerical representations capturing textual meanings in multidimensional spaces, and stored them in high-dimensional vector databases optimized for efficient retrieval.</div></div><div><h3>Results:</h3><div>The implementation of Textual MOCs enhances user engagement by providing meaningful descriptions within sky regions, facilitating the development of effective game-based learning. Semantic MOCs enable sophisticated query capabilities, such as similarity-based searches and context-aware data retrieval, enhancing astronomical data analyses. Integration with multimodal generative AI systems allows for more accurate and contextually relevant interactions supporting both spatial, semantic and visual operations for advancing astronomical data analysis capabilities. Through straightforward examples, we discuss the fundamentals of this new experimental implementation.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101014"},"PeriodicalIF":1.8,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"That pesky A-term: Efficiently correcting for direction-, time-, and baseline-dependent effects in radio interferometric imaging","authors":"Torrance Hodgson,&nbsp;Melanie Johnston-Hollitt","doi":"10.1016/j.ascom.2025.101012","DOIUrl":"10.1016/j.ascom.2025.101012","url":null,"abstract":"<div><div>Radio interferometers must grapple with apparent fields of view that distort the true radio sky. These so-called ‘<span><math><mi>A</mi></math></span>-term’ distortions may be direction-, time- and baseline-dependent, and include effects like the primary beam and the ionosphere. Traditionally, properly handling these effects has been computationally expensive and, instead, less accurate, ad-hoc methods have been employed. Image domain gridding (<span>idg</span>; van der Tol et al., 2018) is a recently developed algorithm that promises to account for these <span><math><mi>A</mi></math></span>-terms both accurately and efficiently. Here we describe a new implementation of <span>idg</span> known as the Parallel Interferometric <span>gpu</span> Imager (Pigi). Pigi is capable of imaging at rates of almost half a billion visibilities per second on modest hardware, making it well suited for the projected data rates of the Square Kilometre Array, and is compatible with both <span>nvidia</span> and <span>amd</span> <span>gpu</span> hardware. Its accuracy is principally limited only by the degree to which <span><math><mi>A</mi></math></span>-terms are spatially sampled. Using data from the Murchison Widefield Array, we demonstrate the effectiveness of Pigi in correcting for simulated ionospheric effects and point to future work that would enable these results on real-world data.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101012"},"PeriodicalIF":1.8,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nessie: A rust-powered, fast, flexible, and generalized friends-of-friends galaxy-group finder in R and Python","authors":"Trystan S. Lambert ,&nbsp;A.S.G. Robotham ,&nbsp;M. Bravo ,&nbsp;C. del P. Lagos ,&nbsp;R. Tobar ,&nbsp;S. Driver ,&nbsp;A. Aufan Stoffels d’Hautefort","doi":"10.1016/j.ascom.2025.101011","DOIUrl":"10.1016/j.ascom.2025.101011","url":null,"abstract":"<div><div>We introduce <span>Nessie</span>, a galaxy group finder implemented in <span>Rust</span> and distributed as both a <span>Python</span> and <span>R</span> package. <span>Nessie</span> employs the friends-of-friends (FoF) algorithm and requires only on-sky position and redshift as input, making it immediately applicable to surveys that lack a well-defined luminosity function. We implement several algorithmic optimizations – including binary search and k-d tree pre-selection – that significantly improve performance by reducing unnecessary galaxy pair checks. To validate the accuracy of <span>Nessie</span>, we tune its parameters using a suite of GALFORM mock lightcones and achieve a strong Figure of Merit. We further demonstrate its reliability by applying it to both the GAMA and SDSS surveys, where it produces group catalogues consistent with those in the literature. Additional functionality is included for comparison with simulations and mock catalogues. Benchmarking on a standard MacBook Pro (M3 chip with 11 cores) shows that version 1 of <span>Nessie</span> can process <span><math><mo>∼</mo></math></span>1 million galaxies in <span><math><mrow><mo>∼</mo><mn>10</mn></mrow></math></span> s, highlighting its speed and suitability for next-generation redshift surveys.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101011"},"PeriodicalIF":1.8,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards automating initial conditions for GALFIT models of spiral galaxies using SpArcFiRe’s outputs","authors":"Matthew E. Portman ,&nbsp;Wayne B. Hayes","doi":"10.1016/j.ascom.2025.100994","DOIUrl":"10.1016/j.ascom.2025.100994","url":null,"abstract":"<div><div>Spiral galaxies constitute a significant fraction of galaxies observed in the local universe yet their characteristic structure is not well understood. Current methods of analysis rely on manual intervention and expertise, both of which present a significant barrier to the investigation of spiral structure at the scale of modern observational surveys. We present an automated pipeline that uses the simple, one-dimensional arc analysis from <span>SpArcFiRe</span> to generate an initial guess for <span>GALFIT</span> to produce two-dimensional photometric decompositions of spiral galaxies. Using this pipeline, we produce two and three component decompositions of several samples of spiral galaxies from the SDSS DR7 data release, as selected by the Galaxy Zoo team. We then assess the performance of our method, validating our results by eye, and analyze the resultant parameterization of these in bulk. Our largest sample is 28912 galaxies, of which we estimate 54% (about 15,700) of the models accurately map the visible structure of the original observations. We identify trends in the Sérsic indices, magnitudes, and arm-to-total flux ratios, and compare these trends to previous decomposition studies, finding general agreement in the arm-to-total flux ratio. Of the other parameters, there is evidence that our models overfit the observations, causing disagreement. Finally, we present an extension to our method that evaluates the model’s pitch angle as it varies along the length of the arm.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 100994"},"PeriodicalIF":1.8,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Karabo: A versatile SKA observation simulation framework","authors":"Rohit Sharma ,&nbsp;Simon Felix ,&nbsp;Luis Fernando Machado Poletti Valle ,&nbsp;Vincenzo Timmel ,&nbsp;Lukas Gehrig ,&nbsp;Andreas Wassmer ,&nbsp;Jennifer Studer ,&nbsp;Pascal Hitz ,&nbsp;Filip Schramka ,&nbsp;Michele Bianco ,&nbsp;Devin Crichton ,&nbsp;Marta Spinelli ,&nbsp;André Csillaghy ,&nbsp;Stefan Kögel ,&nbsp;Alexandre Réfrégier","doi":"10.1016/j.ascom.2025.101004","DOIUrl":"10.1016/j.ascom.2025.101004","url":null,"abstract":"<div><div>Karabo is a versatile Python-based software framework simplifying research with radio astronomy data. It bundles existing software packages into a coherent whole to improve the ease of use of its components. Karabo includes useful abstractions, like strategies to scale and parallelize typical workloads or science-specific Python modules. The framework includes functionality to access datasets and mock observations to study the Square Kilometre Array (SKA) instruments and their expected accuracy. SKA will address problems in a wide range of fields of astronomy. We demonstrate the application of Karabo relevant to some of the SKA science cases from HI intensity mapping, simulation of the radio surveys, radio source detection, the epoch of re-ionization and heliophysics. We discuss the capabilities, scalabilities and challenges of simulating large radio datasets in the context of SKA.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101004"},"PeriodicalIF":1.8,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural networks in the search for fast radio bursts with RATAN-600","authors":"D.O. Kudryavtsev ,&nbsp;S.A. Trushkin ,&nbsp;P.G. Tsybulev ,&nbsp;V.A. Stolyarov","doi":"10.1016/j.ascom.2025.101002","DOIUrl":"10.1016/j.ascom.2025.101002","url":null,"abstract":"<div><div>We present a technique to search for fast radio bursts in records obtained with broadband radiometers having few radio channels. The technique is applied to the RATAN-600 surveys carried out at its Western Sector since the year 2017. A 1D convolutional neural network for multichannel time series classification is developed based on the EfficientNet family of models. The procedure to generate synthetic FRB signals needed for the training dataset is described. We implement a two-stage cascade scheme to effectively suppress the rate of false positive detections. Evaluation of the trained model is provided based on the synthetic events and the giant pulse of the Crab Pulsar.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101002"},"PeriodicalIF":1.8,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced Data Products for radio observatories","authors":"Villard E. ,&nbsp;Ahmadi A. ,&nbsp;Bolton R. ,&nbsp;Breen S. ,&nbsp;Castro S. ,&nbsp;De Breuck C. ,&nbsp;Deg N. ,&nbsp;Delli Veneri M. ,&nbsp;Emsellem E. ,&nbsp;Fleming S. ,&nbsp;Guglielmetti F. ,&nbsp;Hess K.M. ,&nbsp;Hibbard J. ,&nbsp;Holties H. ,&nbsp;Humphreys L. ,&nbsp;Iacobelli M. ,&nbsp;Jachym P. ,&nbsp;Jones B. ,&nbsp;Kimball A. ,&nbsp;Koch E. ,&nbsp;Zwaan M.","doi":"10.1016/j.ascom.2025.101003","DOIUrl":"10.1016/j.ascom.2025.101003","url":null,"abstract":"<div><div>Advanced Data Products (ADPs) are increasingly central to enhancing the efficiency and scientific output of radio observatories. Designed to bridge the gap between raw observational data and science-ready results, ADPs reduce processing overhead, improve reproducibility, and enable a wider range of researchers to engage with complex datasets. Their benefits include accelerated research, interoperability across archives, and reduced duplication of computational effort, with direct implications for sustainability. This paper summarizes the outcomes of the ADP2024 workshop, which reviewed current practices at major facilities including ALMA, SKAO, LOFAR, VLA, and ESO. We highlight lessons learned from ongoing initiatives. Critical issues identified include standardization, provenance tracking, quality assurance, and long-term maintenance. We conclude that ADPs represent a key step toward sustainable, accessible, and scientifically optimized data ecosystems in radio astronomy.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101003"},"PeriodicalIF":1.8,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed Lane-Emden solvers using Lynx-Net: Implementing radial basis functions in Kolmogorov representation","authors":"Elmira Mirzabeigi ,&nbsp;Maryam Babaei ,&nbsp;Amir Hossein Karami ,&nbsp;Sepehr Rezaee ,&nbsp;Rezvan Salehi ,&nbsp;Kourosh Parand","doi":"10.1016/j.ascom.2025.100997","DOIUrl":"10.1016/j.ascom.2025.100997","url":null,"abstract":"<div><div>This paper introduces a novel approach for solving Lane-Emden equations using Lynx-Net, a Physics-Informed Neural Network that integrates Radial Basis Functions (RBFs) within the Kolmogorov representation framework. Lynx-Net addresses these challenges by combining RBF-enhanced function approximation with physics-informed constraints: differential-equation residuals are enforced during training, ensuring stability and rapid convergence. Across a spectrum of polytropic indices, our experiments show that Lynx-Net consistently outperforms prior machine-learning approaches, achieving lower errors without incurring excessive computational cost. The proposed model leverages the function approximation capabilities of RBFs and physics-informed constraints to enhance solution stability and convergence. By incorporating differential equation residuals into the learning process, Lynx-Net minimizes errors while maintaining computational efficiency. Experimental results across multiple test cases demonstrate its superiority over conventional solvers and existing machine learning-based approaches. This research highlights the potential of integrating RBFs with PINNs for solving nonlinear differential equations, providing a scalable and efficient framework applicable to broader problems in astrophysics and engineering.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 100997"},"PeriodicalIF":1.8,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Africanus III. pfb-imaging–A flexible radio interferometric imaging suite","authors":"H.L. Bester ,&nbsp;J.S. Kenyon ,&nbsp;A. Repetti ,&nbsp;S.J. Perkins ,&nbsp;O.M. Smirnov ,&nbsp;T. Blecher ,&nbsp;Y. Mhiri ,&nbsp;J. Roth ,&nbsp;I. Heywood ,&nbsp;Y. Wiaux ,&nbsp;B.V. Hugo","doi":"10.1016/j.ascom.2025.100996","DOIUrl":"10.1016/j.ascom.2025.100996","url":null,"abstract":"<div><div>The popularity of the CLEAN algorithm in radio interferometric imaging stems from its maturity, speed, and robustness. While many alternatives have been proposed in the literature, none have achieved mainstream adoption by astronomers working with data from interferometric arrays operating in the big data regime. This lack of adoption is largely due to increased computational complexity, absence of mature implementations, and the need for astronomers to tune obscure algorithmic parameters. This work introduces <span>pfb-imaging</span>: a flexible library that implements the scaffolding required to develop and accelerate general radio interferometric imaging algorithms. We demonstrate how the framework can be used to implement a sparsity-based image reconstruction technique known as (unconstrained) SARA in a way that scales with image size rather than data volume and features interpretable algorithmic parameters. The implementation is validated on terabyte-sized data from the MeerKAT telescope, using both a single compute node and Amazon Web Services computing instances.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 100996"},"PeriodicalIF":1.8,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}