Astronomy and Computing最新文献

{"title":"Classification of spiral galaxies by spiral arm number using convolutional neural network","authors":"Ming Wei Lee,&nbsp;John Y.H. Soo,&nbsp;Syarawi M.H. Sharoni","doi":"10.1016/j.ascom.2025.100965","DOIUrl":"10.1016/j.ascom.2025.100965","url":null,"abstract":"<div><div>The structural information of spiral galaxies such as the spiral arm number, offer valuable insights into the formation processes of spirals and their physical roles in galaxy evolution. We developed classifiers based on convolutional neural networks (CNNs) using variants of the EfficientNet architecture with different transfer learning techniques and pre-trained weights to categorise spiral galaxies by their number of spiral arms. A selected dataset from Galaxy Zoo 2, comprising 11<!--> <!-->718 images filtered based on appropriate criteria is used for training and evaluation. Both the V2M model (EfficientNetV2M architecture fine-tuned on ImageNet) and the B0 model (EfficientNetB0 architecture with Zoobot pre-trained weights) achieved high accuracy on the down-sampled dataset, with most performance metrics exceeding 0.8 across all classes, except for galaxies with 4 arms due to the limited number of samples in this category. Merging higher-arm-number classes (more than 4 arms) improved the V2M model’s accuracy significantly for 4-arm galaxies, as this approach allowed the model to focus on more distinct features within fewer, broader categories with a more balanced class distribution. GradCAM++ and SmoothGrad highlight the networks’ effectiveness in classifying galaxies, through the distinction of the galaxy structures and the extraction of the spiral arms, with the V2M model showing better capabilities in both tasks. Lower-arm galaxies tend to be misclassified as “can’t tell” when their spiral arms are not clearly visible, while higher-arm galaxies tend to be misclassified as having fewer arms when their features are only partially detected. The study also found that galaxies with 3 arms tend to have lower stellar masses, and this tendency is reduced in the model predictions. The models’ mispredictions between 2-arm and 1/3-arm are likely resulting from external interference and dynamic nature of spiral arms. The V2M model prediction also shows a slight tendency towards higher stellar mass in <strong>many-arm</strong> galaxies.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100965"},"PeriodicalIF":1.9,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869175","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":"A data-driven approach for extracting exoplanetary atmospheric features","authors":"Massimiliano Giordano Orsini ,&nbsp;Alessio Ferone ,&nbsp;Laura Inno ,&nbsp;Paolo Giacobbe ,&nbsp;Antonio Maratea ,&nbsp;Angelo Ciaramella ,&nbsp;Aldo Stefano Bonomo ,&nbsp;Alessandra Rotundi","doi":"10.1016/j.ascom.2025.100964","DOIUrl":"10.1016/j.ascom.2025.100964","url":null,"abstract":"<div><div>Ground-based high-resolution transmission spectroscopy has become a critical tool for probing the chemical compositions of transiting exoplanetary atmospheres. A well-known challenge in this scope lies in the <em>detrending</em> process, which consists in effectively removing contaminating stellar and telluric absorption features obscuring the planetary spectrum. Principal Component Analysis (PCA) is the current state-of-the-art method, but its effectiveness depends on selecting the correct number of components—a subjective choice that impacts how much of the planetary signal is preserved or lost, and the features to be removed are well represented by the linear combination of the principal components. Additionally, there is no quantitative framework for distinguishing between residuals from incomplete subtraction and those containing the true planetary signal.</div><div>In this work, we introduce a novel, computer vision-inspired approach to the task of detrending using Deep Convolutional Generative Adversarial Networks (DCGANs), combined with semantic image inpainting, able to overcome the limitations of PCA. In contrast to PCA, our proposed detrending method operates in a non-linear fashion, allowing for a scalable and robust separation of planetary atmospheric features from interfering signals and eliminating reliance on the manual selection of principal components. As a case study, we consider observations of the ultra-hot Jupiter KELT-9 b acquired by the HARPS-N spectrograph at the Telescopio Nazionale Galileo. Although further refinement is needed for full competitiveness with PCA, our method successfully produces realistic transit-free nights and promising residuals, paving the way for future machine learning-driven detrending methods.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100964"},"PeriodicalIF":1.9,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851336","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 II. QuartiCal: Calibrating radio interferometer data at scale using Numba and Dask","authors":"J.S. Kenyon ,&nbsp;S.J. Perkins ,&nbsp;H.L. Bester ,&nbsp;O.M. Smirnov ,&nbsp;C. Russeeawon ,&nbsp;B.V. Hugo","doi":"10.1016/j.ascom.2025.100962","DOIUrl":"10.1016/j.ascom.2025.100962","url":null,"abstract":"<div><div>Calibration is, and will remain, an integral component of radio interferometric data reduction. However, as larger, more sensitive radio interferometers are conceived and built, the calibration problem grows in both size and difficulty.</div><div>The increasing size can be attributed to the fact that the data volume scales quadratically with the number of antennas in an array. Additionally, new instruments may have up to two orders of magnitude more channels than their predecessors. Simultaneously, increasing sensitivity is making calibration more challenging: low-level RFI and calibration artefacts (in the resulting images) which would previously have been subsumed by the noise may now limit dynamic range and, ultimately, the derived science.</div><div>It is against this backdrop that we introduce <span>QuartiCal</span>: a new Python package implementing radio interferometric calibration routines. <span>QuartiCal</span> improves upon its predecessor, <span>CubiCal</span>, in terms of both flexibility and performance. Whilst the same mathematical framework – complex optimization using Wirtinger derivatives – is in use, the approach has been refined to support arbitrary length chains of parameterized gain terms.</div><div><span>QuartiCal</span> utilizes <span>Dask</span>, a library for parallel computing in Python, to express calibration as an embarrassingly parallel task graph. These task graphs can (with some constraints) be mapped onto a number of different hardware configurations, allowing <span>QuartiCal</span> to scale from running locally on consumer hardware to a distributed, cloud-based cluster.</div><div><span>QuartiCal</span>’s qualitative behaviour is demonstrated using MeerKAT observations of PSR J2009-2026. These qualitative results are followed by an analysis of <span>QuartiCal</span>’s performance in terms of wall time and memory footprint for a number of calibration scenarios and hardware configurations.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100962"},"PeriodicalIF":1.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873958","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":"The GRBSN webtool: An open-source repository for gamma-ray burst-supernova associations","authors":"Gabriel Finneran,&nbsp;Laura Cotter,&nbsp;Antonio Martin-Carrillo","doi":"10.1016/j.ascom.2025.100954","DOIUrl":"10.1016/j.ascom.2025.100954","url":null,"abstract":"<div><div>This paper presents the GRBSN webtool, an open-source data repository coupled to a web interface that hosts the most complete dataset of GRB-SN associations to date. In contrast to repositories of supernova (SN) or gamma-ray burst (GRB) data, this tool provides a multi-wavelength view of each GRB-SN association. GRBSN allows users to view and interact with plots of the data; search and filter the whole database; and download radio, X-ray, optical/NIR photometric and spectroscopic data related to a GRB-SN association. The web interface code and GRB-SN data are hosted on a public GitHub repository, allowing users to upload their own data, flag missing data and suggest improvements. The GRBSN webtool will be maintained by the Space Science group at University College Dublin, Ireland. As the number of confirmed GRB-SN associations increases in the coming years, the GRBSN webtool will provide a robust framework in which to catalogue these associations and their associated data. The web interface is available at: <span><span>https://grbsn.watchertelescope.ie</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100954"},"PeriodicalIF":1.9,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839106","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":"Galaxy morphological classification with manifold learning","authors":"Vasyl Semenov ,&nbsp;Vitalii Tymchyshyn ,&nbsp;Volodymyr Bezguba ,&nbsp;Maksym Tsizh ,&nbsp;Andrii Khlevniuk","doi":"10.1016/j.ascom.2025.100963","DOIUrl":"10.1016/j.ascom.2025.100963","url":null,"abstract":"<div><div>We address the problem of morphological classification of galaxies from the Galaxy Zoo DECaLS dataset using classical machine learning techniques. Our approach employs a dimensionality reduction method followed by a classical classifier to categorize galaxies based on shape (cigar/in-between/ round; edge-on/face-on) and texture (smooth/featured). We evaluate various dimensionality reduction methods, including Locally Linear Embedding (LLE), Isomap, Uniform Manifold Approximation and Projection (UMAP), t-SNE, and Principal Component Analysis (PCA). Our results demonstrate that most classical classifiers achieve their highest performance when combined with LLE, attaining accuracy comparable to that of simple neural networks. Moreover, in the case of shape classification, the three-dimensional representation remains interpretable, in contrast to the commonly observed loss of interpretability following nonlinear transformations. Additionally, we explore dimensionality reduction followed by k-means clustering to assess whether the data exhibits a natural tendency toward a specific number of clusters. We evaluate clustering performance using silhouette, elbow, Dunn, and Davies–Bouldin scores. While the Davies–Bouldin score indicates a slight preference for four clusters — closely aligning with classifications made by human astronomers — the other metrics do not support a distinct clustering structure.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100963"},"PeriodicalIF":1.9,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847794","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":"3D radio data visualisation in open science platforms for next-generation observatories","authors":"I. Labadie-García,&nbsp;J. Garrido,&nbsp;L. Verdes-Montenegro,&nbsp;M.Á. Mendoza,&nbsp;M. Parra-Royón,&nbsp;S. Sánchez-Expósito,&nbsp;R. Ianjamasimanana","doi":"10.1016/j.ascom.2025.100949","DOIUrl":"10.1016/j.ascom.2025.100949","url":null,"abstract":"<div><div>Next-generation telescopes will bring groundbreaking discoveries but they will also present new technological challenges. The Square Kilometre Array Observatory (SKAO) will be one of the most demanding scientific infrastructures, with a projected data output of 700 PB per year to be distributed to a network of SKA Regional Centres. Current tools are not fully suited to manage such massive data volumes, therefore, new research is required to transform science archives from data providers into service providers. In this paper we examine how a science archive can deliver advanced visualisation capabilities for the SKA science archive. In particular, we have conducted a thorough exploration of existing visualisation software for astronomy and other fields to identify tools capable of addressing Big Data requirements. Using selected technologies, we have developed a prototype archive that provides access to interactive visualisations of 3D radio data through web-based interfaces, adhering to International Virtual Observatory Alliance (IVOA) recommendations to favour interoperability and Open Science practices. In addition, we discuss how current IVOA recommendations support these visualisation capabilities and how they could be expanded. Our prototype archive includes a service to generate 3D models on the fly as a server operation, enabling remote visualisations in a flexible manner; for instance, a set of parameters can be used to customise the models and their visualisation. We have used SKA precursor and pathfinder data to test its usability and scalability, concluding that remote visualisation is a viable solution for handling high-volume data. However, our prototype is constrained by memory limitations, requiring techniques to reduce memory usage.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100949"},"PeriodicalIF":1.9,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829237","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":"LRS Bianchi type-V cosmological model in f(Q,T) theory of gravity with cold matter and holographic dark energy","authors":"Y.S. Solanke ,&nbsp;S. Mhaske ,&nbsp;V.J. Dagwal ,&nbsp;D.D. Pawar","doi":"10.1016/j.ascom.2025.100961","DOIUrl":"10.1016/j.ascom.2025.100961","url":null,"abstract":"<div><div>In the present work Bianchi type <span><math><mi>V</mi></math></span> cosmological model with cold dark matter and holographic dark energy with <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>Q</mi><mo>,</mo><mi>T</mi><mo>)</mo></mrow></mrow></math></span> theory of gravity is investigated, with <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>Q</mi><mo>,</mo><mi>T</mi><mo>)</mo></mrow><mo>=</mo><mi>α</mi><mi>Q</mi><mo>+</mo><mi>β</mi><mi>T</mi></mrow></math></span>, where <span><math><mi>α</mi></math></span> and <span><math><mi>β</mi></math></span> are constants. To find the solution of the field equation we have used the anisotropic relation. Various physical and geometrical properties of the model have been investigated graphically.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100961"},"PeriodicalIF":1.9,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847798","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":"Coniferest: A complete active anomaly detection framework","authors":"M.V. Kornilov ,&nbsp;V.S. Korolev ,&nbsp;K.L. Malanchev ,&nbsp;A.D. Lavrukhina ,&nbsp;E. Russeil ,&nbsp;T.A. Semenikhin ,&nbsp;E. Gangler ,&nbsp;E.E.O. Ishida ,&nbsp;M.V. Pruzhinskaya ,&nbsp;A.A. Volnova ,&nbsp;S. Sreejith ,&nbsp;SNAD team","doi":"10.1016/j.ascom.2025.100960","DOIUrl":"10.1016/j.ascom.2025.100960","url":null,"abstract":"<div><div>We present <span>coniferest</span>, an open source generic purpose active anomaly detection framework written in Python. The package design and implemented algorithms are described. Currently, static outlier detection analysis is supported via the Isolation forest algorithm. Moreover, Active Anomaly Discovery (AAD) and Pineforest algorithms are available to tackle active anomaly detection problems. The algorithms and package performance are evaluated on a series of synthetic datasets. We also describe a few success cases which resulted from applying the package to real astronomical data in active anomaly detection tasks within the SNAD project.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100960"},"PeriodicalIF":1.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817370","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 IV. The Stimela2 framework: Scalable and repeatable workflows, from local to cloud compute","authors":"O.M. Smirnov ,&nbsp;S. Makhathini ,&nbsp;J.S. Kenyon ,&nbsp;H.L. Bester ,&nbsp;S.J. Perkins ,&nbsp;A.J.T. Ramaila ,&nbsp;B.V. Hugo","doi":"10.1016/j.ascom.2025.100959","DOIUrl":"10.1016/j.ascom.2025.100959","url":null,"abstract":"<div><div><span>Stimela2</span> is a new-generation framework for developing data reduction workflows. It is designed for radio astronomy data but can be adapted for other data processing applications. <span>Stimela2</span> aims at the middle ground between ease of development, human readability, and enabling robust, scalable and repeatable workflows. <span>Stimela2</span> defines a YAML-based domain specific language (DSL), which represents workflows by linear, concise and intuitive YAML-format <em>recipes</em>. Atomic data reduction tasks (binary executables, Python functions and code, and CASA tasks) are described by YAML-format <em>cab definitions</em> detailing each task’s <em>schema</em> (inputs and outputs). The <span>Stimela2</span> DSL provides a rich syntax for chaining tasks together, and encourages a high degree of modularity: recipes may be nested into other recipes, and configuration is cleanly separated from recipe logic. Tasks can be executed natively or in isolated environments using containerization technologies such as Apptainer. The container images are open-source and maintained through a companion package called <span>cult-cargo</span>. This enables the development of system-agnostic and repeatable workflows. <span>Stimela2</span> facilitates the deployment of scalable, distributed workflows by interfacing with the <span>Slurm</span> scheduler and the <span>Kubernetes</span> API. The latter allows workflows to be readily deployed in the cloud. Previous papers in this series used <span>Stimela2</span> as the underlying technology to run workflows on the AWS cloud.</div><div>This paper presents an overview of <span>Stimela2</span>’s design, architecture and use in the radio astronomy context.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100959"},"PeriodicalIF":1.9,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of ghost dark energy on cosmic evolution in f(Q, L m) theory","authors":"M. Zeeshan Gul ,&nbsp;M. Sharif ,&nbsp;S.A. Qureshi","doi":"10.1016/j.ascom.2025.100956","DOIUrl":"10.1016/j.ascom.2025.100956","url":null,"abstract":"<div><div>The primary aim of this research is to explore the ghost dark energy model in the framework of <span><math><mi>f</mi></math></span>(<span>Q</span>, <span>L</span> _m) gravity, where <span>Q</span> represents the non-metricity scalar and <span>L</span> _m denotes the matter-Lagrangian density. To achieve this objective, we investigate the homogeneous and isotropic universe with an ideal matter distribution. We examine a scenario with interacting fluids that encompass both dark energy and dark matter in this context. Further, we reconstruct <span><math><mi>f</mi></math></span>(<span>Q</span>, <span>L</span> _m) model to examine the effects of this extended gravitational framework on the cosmic evolution. We explore the behavior of numerous cosmic parameters corresponding to distinct parametric values. The viability of the ghost dark energy model is evaluated by the matter contents, revealing that it supports the fast expansion of the cosmos. Furthermore, the statefinder <span><math><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>s</mi><mo>)</mo></mrow></math></span> and standard diagnostic pairs <span><math><mrow><mo>(</mo><msub><mrow><mi>ω</mi></mrow><mrow><mi>D</mi></mrow></msub><mo>−</mo><msubsup><mrow><mi>ω</mi></mrow><mrow><mi>D</mi></mrow><mrow><mo>′</mo></mrow></msubsup><mo>)</mo></mrow></math></span> are used to study the various cosmic eras. This study offers novel perspectives on the correlation between dark energy models and modified gravity theories, thereby enhancing our comprehension of cosmic evolution. Our results align with recent observational evidence, indicating that the <span><math><mi>f</mi></math></span>(<span>Q</span>, <span>L</span> _m) model effectively characterizes dark energy and cosmic evolution.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"52 ","pages":"Article 100956"},"PeriodicalIF":1.9,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680660","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}