Astronomy and Computing最新文献

{"title":"A new approach to web-programming: Binary star DataBase (BDB) engine","authors":"Pavel Kaygorodov ,&nbsp;Ekaterina Malik ,&nbsp;Dana Kovaleva ,&nbsp;Oleg Malkov ,&nbsp;Bernard Debray","doi":"10.1016/j.ascom.2025.101025","DOIUrl":"10.1016/j.ascom.2025.101025","url":null,"abstract":"<div><div>Binary star DataBase BDB (<span><span>http://bdb.inasan.ru</span><svg><path></path></svg></span>) has a very long history and its internal design was changed twice during its lifetime. The first version was written in mid 90’s as CGI (Common Gateway Interface) shell scripts and used text files for data storage. Later it was rewritten in stackless Python with Nagare library. The next major update was performed during last year. The Nagare and other libraries were developing more and more compatibility issues, so we have decided to rewrite the BDB code using a completely new approach. In this paper we are presenting a brief introduction of this new approach to the distributed programming paradigm, which allows to significantly speedup the development. Here we employ the switch from the traditional Model-View-Controller approach to the distributed application, where the server is a “primary node” which controls many web-clients as “subordinate nodes”, delegating all User-Interface-related tasks to them.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101025"},"PeriodicalIF":1.8,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614862","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":"RFI detection based on semi-supervised learning with improved Unet","authors":"J. Li,&nbsp;B. Liang,&nbsp;S. Feng,&nbsp;W. Dai,&nbsp;S. Wei","doi":"10.1016/j.ascom.2025.101020","DOIUrl":"10.1016/j.ascom.2025.101020","url":null,"abstract":"<div><div>Radio Frequency Interference (RFI) suppression is a crucial component of radio astronomical data processing. Accurate elimination of interference maintains the maximum observation purity for astronomical signals. Existing machine learning-based detection methods are overly reliant on fully labeled data, often requiring thousands of annotated samples to achieve satisfactory performance. Aiming at this limitation, we propose the Allspark-Unet model in this paper. It is a semi-supervised semantic segmentation network that incorporates a dedicated feature enhancement mechanism to reconstruct the feature representation of RFI signals. While achieving superior performance, the proposed architecture introduces a computational overhead compared to simpler baselines, representing a meaningful trade-off between performance gains and resource consumption. Experiments are conducted using a real dataset from the 40-meter radio telescope at Yunnan Observatory. Results demonstrate an accuracy of 0.98 with only 272 labeled data samples. Compared to the baseline method, an improvement of 1.52% in the F1 score (to 0.90) is achieved along with a 2.18% gain in the mean Intersection over Union (mIoU). Quantitative analysis reveals that Allspark-Unet effectively reduces the dependence on labeled data for RFI detection. The proposed feature reconstruction mechanism enables reliable interference detection even in small-sample scenarios. The detailed analysis of this performance-computational cost trade-off is presented and discussed in the study.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101020"},"PeriodicalIF":1.8,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466462","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":"CHEOPS ground segment: Systems and automation for mission and science operations","authors":"Alexis Heitzmann ,&nbsp;María J. González Bonilla ,&nbsp;Anja Bekkelien ,&nbsp;Babatunde Akinsanmi ,&nbsp;Mathias O.W. Beck ,&nbsp;Nicolas Billot ,&nbsp;Christopher Broeg ,&nbsp;Adrien Deline ,&nbsp;David Ehrenreich ,&nbsp;Andrea Fortier ,&nbsp;Marcus G.F. Kirsch ,&nbsp;Monika Lendl ,&nbsp;Nuria Alfaro Llorente ,&nbsp;Naiara Fernández de Bobadilla Vallano ,&nbsp;María Fuentes Tabas ,&nbsp;Anthony G. Maldonado ,&nbsp;Eva M. Vega Carrasco ,&nbsp;David Modrego Contreras","doi":"10.1016/j.ascom.2025.101016","DOIUrl":"10.1016/j.ascom.2025.101016","url":null,"abstract":"<div><div>The CHaracterising ExOPlanet Satellite (CHEOPS) is the first European Space Agency (ESA) small-class mission. It has been performing photometric astronomical observations with a particular emphasis on exoplanetary science for the past five years. A distinctive feature of CHEOPS is that the responsibility for all operational aspects of the mission lies with the CHEOPS consortium rather than ESA. As a result, all subsystems, their architecture, and operational processes have been independently developed and tailored specifically to CHEOPS. This paper offers an overview of the CHEOPS operational subsystems, the design, and the automation framework that compose the two main components of the CHEOPS ground segment: the Mission Operations Center (MOC) and the Science Operations Center (SOC). This comprehensive description of the CHEOPS workflow aims to serve as a reference and potential source of inspiration for future small and/or independent space missions.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101016"},"PeriodicalIF":1.8,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466461","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":"Co-SOM: Co-training for photometric redshift estimation using Self-Organizing Maps","authors":"A. Callejas-Tavera ,&nbsp;E. Molino-Minero-Re ,&nbsp;O. Valenzuela","doi":"10.1016/j.ascom.2025.101019","DOIUrl":"10.1016/j.ascom.2025.101019","url":null,"abstract":"<div><div>The upcoming galaxy large-scale surveys, such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), will generate photometry for billions of galaxies. The interpretation of large-scale weak lensing maps, as well as the estimation of galaxy clustering, requires reliable redshifts with high precision for multi-band photometry. However, obtaining spectroscopy for billions of galaxies is impractical and complex; therefore, having a sufficiently large number of galaxies with spectroscopic observations to train supervised algorithms for accurate redshift estimation is a significant challenge and an open research area. We propose a novel methodology called Co-SOM based on Co-training and Self-Organizing Maps (SOM), integrating labeled (sources with spectroscopic redshifts) and unlabeled (sources with photometric observations only) data during the training process, through a selection method based on map topology (connectivity structure of the SOM lattice) to leverage the limited spectroscopy available for photo-z estimation. We utilized the magnitudes and colors of Sloan Digital Sky Survey data release 18 (SDSS-DR18) to analyze and evaluate the performance, varying the proportion of labeled data and adjusting the training parameters. For training sets of 1% of labeled data (<span><math><mrow><mo>≈</mo><mn>20</mn><mo>,</mo><mn>000</mn></mrow></math></span> galaxies) we achieved a performance of bias <span><math><mrow><mi>Δ</mi><mi>z</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>00007</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>00022</mn></mrow></math></span>, precision <span><math><mrow><msub><mrow><mi>σ</mi></mrow><mrow><mi>z</mi><mi>p</mi></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>00063</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>00032</mn></mrow></math></span> and outlier fraction <span><math><mrow><mi>o</mi><mi>u</mi><mi>t</mi><mtext>_</mtext><mi>f</mi><mi>r</mi><mi>a</mi><mi>c</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>02083</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>00027</mn></mrow></math></span>. Additionally, we conducted experiments varying the volume of labeled data, and the bias remains below <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></math></span>, regardless of the size of the spectroscopic or photometric data. These low-redshift results demonstrate the potential of semi-supervised learning to address spectroscopic limitations in future photometric surveys.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101019"},"PeriodicalIF":1.8,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417557","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":"Exploring the effects of Hawking evaporation on accretion disk, greybody factors and scalar perturbations of AdS black hole in f(Q) cosmologies","authors":"Shahid Chaudhary ,&nbsp;Muhammad Danish Sultan ,&nbsp;Asifa Ashraf ,&nbsp;Ali M. Mubaraki ,&nbsp;Saad Althobaiti ,&nbsp;Farruh Atamurotov ,&nbsp;Asif Mahmood","doi":"10.1016/j.ascom.2025.101017","DOIUrl":"10.1016/j.ascom.2025.101017","url":null,"abstract":"<div><div>We consider recently developed AdS black hole in <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>Q</mi><mo>)</mo></mrow></mrow></math></span> cosmologies to ascertain how accretion, graybody factors and scalar perturbations are effected by Hawking evaporation. We utilize Stefan–Boltzmann law to construct numerical plots exhibiting various evaporation patterns through distinct models. Our findings provide a realistic but distinct rate of mass loss across the models revealing the substantial impact of parameters as well as sensitivity of evaporation process to the underlying gravitational theory. We employ Novikov–Thorne model to investigate the thin accretion disks onto AdS black hole in <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>Q</mi><mo>)</mo></mrow></mrow></math></span> cosmologies. We compute direct and secondary images of the black hole’s accretion disk at different observational angles. We observe that the considered model significantly effects the structure of accretion disks and gravitational lensing. Moreover, we explore time evolution of black hole under the influence of physical parameters. We infer the pattern of both the gradual and quick decay precipitated by varying geometric configuration in <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>Q</mi><mo>)</mo></mrow></mrow></math></span> gravity. We observe that higher values of <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>Q</mi><mo>)</mo></mrow></mrow></math></span> gravity parameters lower the greybody factor bound across all frequencies. This suggest that higher values of the parameters suppress the escape of radiation from the black hole.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101017"},"PeriodicalIF":1.8,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363559","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":"Decoding the Radio Sky: Bayesian ensemble learning and SVD-based feature extraction for automated radio galaxy classification","authors":"Theophilus Ansah-Narh ,&nbsp;Jordan Lontsi Tedongmo ,&nbsp;Joseph Bremang Tandoh ,&nbsp;Nia Imara ,&nbsp;Ezekiel Nii Noye Nortey","doi":"10.1016/j.ascom.2025.101018","DOIUrl":"10.1016/j.ascom.2025.101018","url":null,"abstract":"<div><div>The classification of radio galaxies is central to understanding galaxy evolution, active galactic nuclei dynamics, and the large-scale structure of the universe. However, traditional manual techniques are inadequate for processing the massive, heterogeneous datasets generated by modern radio surveys. In this study, we present a probabilistic machine learning framework that integrates Singular Value Decomposition (SVD) for feature extraction with Bayesian ensemble learning to achieve robust, scalable radio galaxy classification. The SVD approach effectively reduces dimensionality while preserving key morphological structures, enabling efficient representation of galaxy features. To mitigate class imbalance and avoid the introduction of artefacts, we incorporate a Local Neighbourhood Encoding strategy tailored to the astrophysical distribution of galaxy types. The resulting features are used to train and optimise several baseline classifiers: Logistic Regression, Support Vector Machines, LightGBM, and Multi-Layer Perceptrons within bagging, boosting, and stacking ensembles governed by a Bayesian weighting scheme. Our results demonstrate that Bayesian ensembles outperform their traditional counterparts across all metrics, with the Bayesian stacking model achieving a classification accuracy of 99.0% and an F1-score of 0.99 across Compact, Bent, Fanaroff–Riley Type I (FR-I), and Type II (FR-II) sources. Interpretability is enhanced through SHAP analysis, which highlights the principal components most associated with morphological distinctions. Beyond improving classification performance, our framework facilitates uncertainty quantification, paving the way for more reliable integration into next-generation survey pipelines. This work contributes a reproducible and interpretable methodology for automated galaxy classification in the era of data-intensive radio astronomy.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101018"},"PeriodicalIF":1.8,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417556","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":"First single-star scidar measurements at Oukaimeden Observatory, Morocco","authors":"Y. Errazzouki ,&nbsp;A. Habib ,&nbsp;A. Jabiri ,&nbsp;M. Sabil ,&nbsp;Z. Benkhaldoun ,&nbsp;J. Chafi ,&nbsp;M. El bahraoui ,&nbsp;Y. El jariri","doi":"10.1016/j.ascom.2025.101015","DOIUrl":"10.1016/j.ascom.2025.101015","url":null,"abstract":"<div><div>This study aims to validate the initial performance of the Single Star SCIDAR (SSS) instrument recently deployed at the Oukaimeden Observatory in Morocco. The primary objectives are twofold: (i) to assess its capability to retrieve real-time vertical profiles of the refractive index structure constant, <span><math><mrow><msubsup><mrow><mi>C</mi></mrow><mrow><mi>n</mi></mrow><mrow><mn>2</mn></mrow></msubsup><mrow><mo>(</mo><mi>h</mi><mo>)</mo></mrow></mrow></math></span>, up to an altitude of 22 km, and (ii) to establish the reliability of the SSS system for real-time turbulence profiling, thereby confirming its suitability for prospective applications in adaptive optics. The (SSS) retrieves these profiles by analyzing single-star scintillation through a modified power spectrum of atmospheric speckles. An objective function is derived and minimized using the Active-Set optimization algorithm, enabling accurate and real-time reconstruction of <span><math><mrow><msubsup><mrow><mi>C</mi></mrow><mrow><mi>n</mi></mrow><mrow><mn>2</mn></mrow></msubsup><mrow><mo>(</mo><mi>h</mi><mo>)</mo></mrow></mrow></math></span> profiles.</div><div>A total of 34 h of data were collected from August 25 to 29, 2024. The reconstructed profiles and integrated seeing values are in good agreement with independent measurements from the Cyclope seeing monitor, confirming the reliability of the (SSS) system. Beyond site characterization, these results highlight the practical significance of real-time turbulence profile extraction, which allows adaptive-optics systems of next-generation 4 m-class telescopes to be adjusted more effectively to the prevailing atmospheric conditions. In addition, the compact and autonomous design of the (SSS) makes it particularly suitable for deployment at remote observatories with limited infrastructure.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101015"},"PeriodicalIF":1.8,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325032","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":"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}