Astronomy and Computing最新文献_第4页

Ginnungagap — A massively parallel cosmological initial conditions generator Ginnungagap -一个大规模平行宇宙初始条件发生器

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2026-02-06 DOI: 10.1016/j.ascom.2026.101082

Sergey Pilipenko , Gustavo Yepes , Stefan Gottlöber , Steffen Knollmann

引用次数: 0

Enabling scalable astrophysical visualization through HPC: The VisIVO experience 通过HPC实现可扩展的天体物理可视化：VisIVO体验

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2026-01-23 DOI: 10.1016/j.ascom.2026.101068

Giuseppe Tudisco , Fabio Vitello , Ugo Becciani , Eva Sciacca , Bruno Montalto , Simone Olmi , Fernando Carello , Matteo Farci , Marco Di Francesco

{"title":"Enabling scalable astrophysical visualization through HPC: The VisIVO experience","authors":"Giuseppe Tudisco , Fabio Vitello , Ugo Becciani , Eva Sciacca , Bruno Montalto , Simone Olmi , Fernando Carello , Matteo Farci , Marco Di Francesco","doi":"10.1016/j.ascom.2026.101068","DOIUrl":"10.1016/j.ascom.2026.101068","url":null,"abstract":"<div><div>As the Square Kilometre Array (SKA) radio telescopes approach full operation, they are set to generate hundreds of petabytes of data annually, offering unprecedented resolution and detail in astrophysical observations. This massive influx of data presents significant challenges in terms of storage, processing, analysis, and visualization. Scientific visualization becomes essential in this context, enabling researchers to interpret complex, high-dimensional datasets and extract valuable insights.</div><div>This paper presents the latest developments of VisIVO Visual Analytics, an interactive visualization tool evolving to meet the needs of the SKA and similar large-scale astrophysical projects. Originally designed as a desktop application, VisIVO has transitioned through different approaches to address the challenges of modern astrophysical datasets.</div><div>The first approach integrates VisIVO with a Science Platform, providing containerized solutions that enhance accessibility, reproducibility, and interoperability for researchers. The second approach leverages a ParaView-based client–server architecture, enabling distributed and parallel visualization pipelines and allowing computational workloads to be efficiently managed across multiple nodes in high-performance computing (HPC) clusters. The third approach, implemented within the AstroTool project, provides an alternative strategy to exploit HPC resources for interactive visualization and analysis of large-scale datasets.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101068"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173468","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}

引用次数: 0

VisIVO interactive high performance visualization VisIVO交互式高性能可视化

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ascom.2026.101069

E. Sciacca , N. Tuccari , U. Arshad , F. Pitari , G. Muscianisi , C. Carbone , F. Vitello , B. Montalto

{"title":"VisIVO interactive high performance visualization","authors":"E. Sciacca , N. Tuccari , U. Arshad , F. Pitari , G. Muscianisi , C. Carbone , F. Vitello , B. Montalto","doi":"10.1016/j.ascom.2026.101069","DOIUrl":"10.1016/j.ascom.2026.101069","url":null,"abstract":"<div><div>The exponential growth of data in Astrophysics and Cosmology demands scalable computational infrastructures coupled with interactive and reproducible visualization tools. This paper presents the integration of the VisIVO scientific visualization framework within the InterActive Computing (IAC) service at Cineca, providing real-time, GPU-accelerated visualization directly on HPC resources through a browser-based Jupyter interface. By developing dedicated Python wrappers and a custom Jupyter kernel, VisIVO can now be executed seamlessly within interactive notebooks, eliminating traditional command-line complexities and enabling immediate visualization on HPC compute nodes. Additionally, the incorporation of RESTful APIs based on the Flask framework enables the remote execution of VisIVO operations through lightweight web services, further abstracting backend complexity and enhancing interoperability with external applications. The framework improves user accessibility, fosters reproducibility, and supports high-throughput data exploration across large-scale astrophysical simulations. The system has been validated through representative use cases, including visual analysis of cosmological simulations produced with the GADGET code, demonstrating its scalability, robustness, and capacity to support interactive scientific discovery on HPC infrastructures.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101069"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173477","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}

引用次数: 0

Analysis of Galactic cirrus filaments in HSC-SSP high-resolution deep images using artificial neural networks 利用人工神经网络分析HSC-SSP高分辨率深度图像中的银河卷云细丝

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2026-01-28 DOI: 10.1016/j.ascom.2026.101075

Denis M. Poliakov , Anton A. Smirnov , Sergey S. Savchenko , Alexander A. Marchuk , Aleksandr V. Mosenkov , Vladimir B. Il’in , George A. Gontcharov , Daria G. Turichina , Andrey D. Panasyuk

{"title":"Analysis of Galactic cirrus filaments in HSC-SSP high-resolution deep images using artificial neural networks","authors":"Denis M. Poliakov , Anton A. Smirnov , Sergey S. Savchenko , Alexander A. Marchuk , Aleksandr V. Mosenkov , Vladimir B. Il’in , George A. Gontcharov , Daria G. Turichina , Andrey D. Panasyuk","doi":"10.1016/j.ascom.2026.101075","DOIUrl":"10.1016/j.ascom.2026.101075","url":null,"abstract":"<div><div>The existence of Galactic optical cirrus poses a challenge for observing faint objects within our Galaxy and dim extragalactic structures. To investigate individual cirrus filaments in the Hyper Suprime-Cam Subaru Strategic Program public data release 3 (HSC-SSP DR3) we use a technique based on convolutional neural networks and ensemble learning. This approach allows us to distinguish cirrus filaments from foreground and background objects across the entire HSC-SSP, using optical images in the <span><math><mi>g</mi></math></span>, <span><math><mi>r</mi></math></span>, and <span><math><mi>i</mi></math></span> wavebands. A comparison with previous work using deep Sloan Digital Sky Survey Stripe 82 (SDSS Stripe 82) data reveals that the cirrus clouds identified in this study are highly consistent in location within the overlapping survey region. However, in the deeper HSC-SSP dataset, we were able to detect 4.5 times more cirrus clouds. Our study indicates that the sky background in HSC-SSP coadd images is over-subtracted, as evidenced by the surface brightness distribution in cirrus filaments and surrounding regions. Objects with surface brightness of <span><math><mrow><mi>m</mi><mo>=</mo><mn>29</mn><mspace></mspace><msup><mrow><mtext>mag arcsec</mtext></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup></mrow></math></span> near large filaments can be dimmed by over-subtraction of 0.5 magnitude in the <span><math><mi>r</mi></math></span> band. This suggests that cirrus clouds should be taken into account in algorithms for estimating the sky background. For practical use, we provide a catalog of filaments and a framework that allows one to train neural network models for segmenting cirri in HSC-SSP coadd images.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101075"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173480","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}

引用次数: 0

PRESOL: A web-based computational setting for feature-based flare forecasting PRESOL：基于网络的基于特征的耀斑预测计算设置

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2025-12-12 DOI: 10.1016/j.ascom.2025.101046

Chiara Curletto , Paolo Massa , Valeria Tagliafico , Cristina Campi , Federico Benvenuto , Michele Piana , Andrea Tacchino

引用次数: 0

Application of Artificial Intelligence techniques to tracking systems in space experiments 人工智能技术在空间实验跟踪系统中的应用

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2025-11-28 DOI: 10.1016/j.ascom.2025.101037

Federica Cuna , Maria Bossa , Fabio Gargano , Mario Nicola Mazziotta

{"title":"Application of Artificial Intelligence techniques to tracking systems in space experiments","authors":"Federica Cuna , Maria Bossa , Fabio Gargano , Mario Nicola Mazziotta","doi":"10.1016/j.ascom.2025.101037","DOIUrl":"10.1016/j.ascom.2025.101037","url":null,"abstract":"<div><div>The application of advanced Artificial Intelligence (AI) techniques in astroparticle experiments represents a major advancement in both data analysis and experimental design. As space missions become increasingly complex, integrating AI tools is essential for optimizing system performance and maximizing scientific return. This study explores the use of Graph Neural Networks (GNNs) within the tracking systems of space-based experiments. A key challenge in track reconstruction is the high level of noise, primarily due to backscattering tracks, which can obscure the identification of primary particle trajectories. We propose a novel GNN-based approach for node-level classification tasks, specifically designed to distinguish primary tracks from backscattered ones within the tracker. In this framework, AI is employed as a powerful tool for pattern recognition, enabling the system to identify meaningful structures within complex tracking data and to discriminate signal from backscattering with higher precision. By addressing these challenges, our work aims to enhance the accuracy and reliability of data interpretation in astroparticle physics through the deployment of state-of-the-art AI methodologies.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101037"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617389","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}

引用次数: 0

Standard candle based distance estimation with learning algorithms 基于学习算法的标准蜡烛距离估计

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2025-12-17 DOI: 10.1016/j.ascom.2025.101044

Virginia Ajani , Martina Giovalli , Paolo Viviani , Beatrice Bucciarelli , Sibilla Perina , Deborah Busonero , Andrea Lessio , Vanina Fissore , Olivier Terzo

{"title":"Standard candle based distance estimation with learning algorithms","authors":"Virginia Ajani , Martina Giovalli , Paolo Viviani , Beatrice Bucciarelli , Sibilla Perina , Deborah Busonero , Andrea Lessio , Vanina Fissore , Olivier Terzo","doi":"10.1016/j.ascom.2025.101044","DOIUrl":"10.1016/j.ascom.2025.101044","url":null,"abstract":"<div><div>Measuring distances to celestial objects, such as stars and galaxies, is essential to characterizing their physical properties, formation, and evolution, and provides fundamental constraints on the expansion rate of the Universe. In this work, we present a methodological study comparing several machine learning and deep learning approaches for predicting astrophysical parameters — such as parallax, astrometry-based luminosity, and distance — using Cepheids and RR Lyrae samples from Gaia DR3 catalogues as input. In parallel, we introduce a framework to exploit the historical archive of photographic plates from INAF-OATo. In this context we extract a catalogue of objects from the plates, then cross-match the output sources with the Gaia dataset, with the goal of extending light curves that could serve as additional input for the models. Preliminary results identify the Gaussian Process regressor as the best-performing model among those tested, and the Multi-Layer Perceptron (MLP) as the most promising deep learning approach. We further study the propagation of uncertainties, enabling us to incorporate them both into the models and the predictions. For the plate analysis, we chose an image of the LMC field with 43 cepheids in common with the Gaia catalogue as a first case study to validate our methodology.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101044"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840025","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}

引用次数: 0

Hunting the outliers: Machine learning for anomalous time series detection 寻找异常值：异常时间序列检测的机器学习

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2025-12-23 DOI: 10.1016/j.ascom.2025.101049

N. De Bonis , S. Vaccaro , Y. Maruccia , G. Riccio , R. Crupi , S. Rubini , D. De Cicco , M. Brescia , S. Cavuoti

{"title":"Hunting the outliers: Machine learning for anomalous time series detection","authors":"N. De Bonis , S. Vaccaro , Y. Maruccia , G. Riccio , R. Crupi , S. Rubini , D. De Cicco , M. Brescia , S. Cavuoti","doi":"10.1016/j.ascom.2025.101049","DOIUrl":"10.1016/j.ascom.2025.101049","url":null,"abstract":"<div><div>The increasing availability of large-scale time series datasets from modern astronomical surveys, such as those provided by Gaia and the forthcoming Legacy Survey of Space and Time (LSST) to be conducted with the Simonyi Survey Telescope at the Vera C. Rubin Observatory, is transforming time-domain astrophysics, enabling the systematic study of variable and transient phenomena across billions of sources. However, the sheer volume and heterogeneity of these data present significant challenges for traditional analysis techniques. Feature-based representations have emerged as a powerful solution, allowing the application of machine learning methods for efficient characterization and classification of astrophysical sources, including the reliable identification of Active Galactic Nuclei (AGNs). In this work, we introduce a general-purpose methodology for anomaly detection in time series data that transfers this feature engineering framework to the financial domain, where time series display complexities, such as noise and irregular patterns, closely resembling those in astrophysics. By combining domain-informed features with unsupervised algorithms (specifically Isolation Forests and Autoencoders), our approach effectively detects anomalous time series relative to the sample studied, demonstrating strong performance and highlighting its cross-domain transferability. Moreover, we propose an extension based on adaptive temporal windows to localize anomalies at a finer temporal resolution, further enhancing detection capabilities. Finally, we discuss the potential for reapplying this adaptive strategy to astrophysical time series, aiming to improve the identification of rare or unexpected behaviors in future studies.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101049"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840024","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}

引用次数: 0

The Origins of Planets for ArieL (OPAL) Key Science Project: The end-to-end planet formation campaign for the ESA space mission Ariel 阿里尔行星的起源（OPAL）关键科学项目：欧空局阿里尔太空任务的端到端行星形成运动

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2026-02-06 DOI: 10.1016/j.ascom.2026.101063

Danae Polychroni , Diego Turrini , Romolo Politi , Sergio Fonte , Eugenio Schisano , Elenia Pacetti , Paolo Matteo Simonetti , Michele Zusi , Sergio Molinari , Stavro Ivanovski

{"title":"The Origins of Planets for ArieL (OPAL) Key Science Project: The end-to-end planet formation campaign for the ESA space mission Ariel","authors":"Danae Polychroni , Diego Turrini , Romolo Politi , Sergio Fonte , Eugenio Schisano , Elenia Pacetti , Paolo Matteo Simonetti , Michele Zusi , Sergio Molinari , Stavro Ivanovski","doi":"10.1016/j.ascom.2026.101063","DOIUrl":"10.1016/j.ascom.2026.101063","url":null,"abstract":"<div><div>The growing body of atmospheric observations of exoplanets from space and ground-based facilities showcases how the great diversity of the planetary population is not limited to their physical properties but extends to their compositions. The ESA space mission Ariel will observe and characterise hundreds of exoplanetary atmospheres to explore and understand the roots of this compositional diversity. To lay the foundations for the Ariel mission, the OPAL Key Science Project is tasked with creating an unprecedented library of realistic synthetic atmospheres spanning tens of elements and hundreds of molecules on which the Ariel consortium will test and validate its codes and pipelines ahead of launch. In this work we describe the aims and the pipeline of codes of the OPAL project, as well as the process through which we trace the genetic link connecting planets to their native protoplanetary discs and host stars. We present the early results of this complex and unprecedented endeavour and discuss how they highlight the great diversity of outcomes that emerge from the large degeneracy in the parameter space of possible initial conditions to the planet formation process. This, in turn, illustrates the growing importance of interdisciplinary modelling studies supported by high-performance computing methods and infrastructures to properly investigate this class of high-dimensionality problems.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101063"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173400","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}

引用次数: 0

Mercury-Arχes: A high-performance n-body code for planet formation studies Mercury-Arχes：用于行星形成研究的高性能n体代码

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2026-01-24 DOI: 10.1016/j.ascom.2026.101064

Diego Turrini , Sergio Fonte , Romolo Politi , Danae Polychroni , Scigé J. Liu , Paolo Matteo Simonetti , Simona Pirani

{"title":"Mercury-Arχes: A high-performance n-body code for planet formation studies","authors":"Diego Turrini , Sergio Fonte , Romolo Politi , Danae Polychroni , Scigé J. Liu , Paolo Matteo Simonetti , Simona Pirani","doi":"10.1016/j.ascom.2026.101064","DOIUrl":"10.1016/j.ascom.2026.101064","url":null,"abstract":"<div><div>Forming planetary systems are populated by large numbers of gravitationally interacting planetary bodies, spanning from massive giant planets to small planetesimals akin to present-day asteroids and comets. All these planetary bodies are embedded in the gaseous embrace of their native protoplanetary disks, and their interactions with the disk gas play a central role in shaping their dynamical evolution and the outcomes of planet formation. These factors make realistic planet formation simulations extremely computationally demanding, which in turn means that accurately modeling the formation of planetary systems requires the use of high-performance methods. The planet formation code <span>Mercury-Ar</span> <span><math><mi>χ</mi></math></span> <span>es</span> was developed to address these challenges and, since its first implementation, has been used in multiple exoplanetary and Solar System studies. <span>Mercury-Ar</span> <span><math><mi>χ</mi></math></span> <span>es</span> is a parallel n-body code that builds on the widely used <span>Mercury</span> code and is capable of modeling the growth and migration of forming planets, the interactions between planetary bodies and the disk gas, as well as the evolving impact flux of planetesimals on forming planets across the different stages of their formation process. In this work we provide the up-to-date overview of its physical modeling capabilities and the first detailed description of its high-performance implementation based on the OpenMP directive-based parallelism for shared memory environments, to harness the multi-thread and vectorization features of modern processor architectures.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101064"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173475","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}

引用次数: 0