Astronomy and Computing最新文献

Polarization based direction of arrival estimation using a radio interferometric array 基于偏振的无线电干涉阵列到达方向估计

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2025-12-22 DOI: 10.1016/j.ascom.2025.101052

Sarod Yatawatta

引用次数: 0

AstroFlow: A customizable workflow management system for astronomical data production and case study of EP-WXT AstroFlow：用于天文数据生产和EP-WXT案例研究的可定制工作流管理系统

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2026-02-04 DOI: 10.1016/j.ascom.2026.101073

Zhen Zhang , Yunfei Xu , Yuan Liu , Chenzhou Cui , Dongwei Fan

{"title":"AstroFlow: A customizable workflow management system for astronomical data production and case study of EP-WXT","authors":"Zhen Zhang , Yunfei Xu , Yuan Liu , Chenzhou Cui , Dongwei Fan","doi":"10.1016/j.ascom.2026.101073","DOIUrl":"10.1016/j.ascom.2026.101073","url":null,"abstract":"<div><div>In the era of time-domain astronomy, automated and efficient data processing is a crucial factor in ensuring astronomical scientific output. With the international collaboration trend in large-scale sky survey projects, data processing algorithms developed through multi-institutional collaboration face challenges in system integration, scheduling capability, extensibility, and traceability. In this work, we employ containerized execution modules to resolve dependency issues in multi-institutional software integration, implement complex workflow orchestration through message queue control modules, including cyclic dependencies, dynamic parallel relationships, and resource/priority-based scheduling. The system also non-intrusively implements the International Virtual Observatory Alliance (IVOA) provenance data model through aspect injection mechanisms, ensuring traceability and reproducibility of data products. In Einstein Probe (EP) data processing, the system has processed over 20,000 general observations, 170,000 FXT observations, and 21,000 VHF and Beidou alerts, supported over 300 agile algorithm updates, and detected more than 17,000 sources and nearly 200 transient events. The system demonstrates excellent generality and portability, smoothly migrating from the single-module LEIA to the 12-module EP, and supporting data fusion for new payload FXT and multiple data transmission links. This workflow management system provides a flexible, extensible, and standardized data processing framework for future large-scale astronomical observation projects, effectively addressing key issues such as multi-institutional collaboration, complex workflow orchestration, and scientific data reproducibility.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101073"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173398","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

Reproducible star–galaxy separation in DES DR2 18≤i<24: A minimal machine-learning baseline with slice-wise metrics, calibration diagnostics, and visualization mosaics DES DR2 18≤i<24中可重复的恒星-星系分离：具有切片指标，校准诊断和可视化马赛克的最小机器学习基线

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2026-01-15 DOI: 10.1016/j.ascom.2026.101059

Qingchuan Zhao

{"title":"Reproducible star–galaxy separation in DES DR2 18≤i<24: A minimal machine-learning baseline with slice-wise metrics, calibration diagnostics, and visualization mosaics","authors":"Qingchuan Zhao","doi":"10.1016/j.ascom.2026.101059","DOIUrl":"10.1016/j.ascom.2026.101059","url":null,"abstract":"<div><div>We present a compact and fully reproducible workflow for star–galaxy separation in the Dark Energy Survey Data Release 2 (DES DR2) over <span><math><mrow><mn>18</mn><mo>≤</mo><mi>MAG_AUTO_I</mi><mo><</mo><mn>24</mn></mrow></math></span>. Using only two widely available catalog attributes—<span>MAG_AUTO_I</span> (Kron-like magnitude) and <span>SPREAD_MODEL_I</span> [a point spread function (PSF)–extended morphology discriminant]—we train slice-wise logistic-regression models against the survey’s internal morphology summary <span>EXTENDED_CLASS_COADD</span>. Performance is reported as a function of magnitude, and slice-wise class fractions are quantified, showing smooth variation from bright to faint regimes without severe class imbalance in most slices (imbalance increases toward the faint edge). Across most slices the baseline reproduces the internal label with high discriminative metrics (precision, recall, <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>, and AUC), while Brier scores and reliability curves highlight calibration challenges toward the faint end. We also examine the impact of seeing and cross-validation strategy (random vs. tile-wise), and test a minimal feature-augmentation experiment. All SQL, derived catalogs, metrics tables, and figure notebooks are released under a citable dataset on Zenodo (DOI: <span><span>10.5281/zenodo.17688656</span><svg><path></path></svg></span>) to enable reproduction. The main contribution is a transparent, pedagogical baseline that can serve as a reproducible sanity check and a starting point for more sophisticated classifiers in DES-like surveys.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101059"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022495","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

Solar flare forecasting with foundational transformer models across image, video, and time-series modalities 太阳耀斑预报与基础变压器模型跨图像，视频，和时间序列模式

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2025-12-04 DOI: 10.1016/j.ascom.2025.101042

S. Riggi , P. Romano , A. Pilzer , U. Becciani

{"title":"Solar flare forecasting with foundational transformer models across image, video, and time-series modalities","authors":"S. Riggi , P. Romano , A. Pilzer , U. Becciani","doi":"10.1016/j.ascom.2025.101042","DOIUrl":"10.1016/j.ascom.2025.101042","url":null,"abstract":"<div><div>We present a comparative study of transformer-based architectures for solar flare forecasting using heterogeneous data modalities, including images, video sequences, and time-series observations. Our analysis evaluates three recent foundational models <span><math><mo>−</mo></math></span> <em>SigLIP2</em> for image encoding, <em>VideoMAE</em> for spatio-temporal video representation, and <em>Moirai2</em> for multivariate time-series forecasting <span><math><mo>−</mo></math></span> applied to publicly available datasets of solar magnetograms from the SDO/HMI mission and soft X-ray fluxes acquired by GOES satellites. All models are trained and validated under consistent data splits and evaluation criteria, with the goal of assessing the strengths and limitations of transformer backbones across spatial and temporal representations of solar activity. We investigate multiple loss formulations (weighted BCE, focal, and score-oriented) and training balance strategies to mitigate class imbalance typical of flare datasets. Results show that while both SigLIP2 and VideoMAE achieve typical performance on image and video data (True Skill Statistic TSS <span><math><mo>∼</mo></math></span> 0.60–0.65), the time-series model Moirai2 reaches superior forecasting skill (TSS <span><math><mo>∼</mo></math></span> 0.74) using irradiance-based temporal evolution alone. These findings highlight the potential of pretrained transformer architectures and cross-modal learning for advancing operational space weather forecasting, paving the way toward unified multimodal models that integrate visual and temporal information.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101042"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684926","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

Accelerating cosmological simulations on GPUs: A step towards sustainability and green-awareness 加速gpu上的宇宙模拟：迈向可持续发展和绿色意识的一步

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2026-01-15 DOI: 10.1016/j.ascom.2026.101060

G. Lacopo , M.D. Lepinzan , D. Goz , G. Taffoni , L. Tornatore , P. Monaco , P.J. Elahi , U. Varetto , M. Cytowski , L. Riha

{"title":"Accelerating cosmological simulations on GPUs: A step towards sustainability and green-awareness","authors":"G. Lacopo , M.D. Lepinzan , D. Goz , G. Taffoni , L. Tornatore , P. Monaco , P.J. Elahi , U. Varetto , M. Cytowski , L. Riha","doi":"10.1016/j.ascom.2026.101060","DOIUrl":"10.1016/j.ascom.2026.101060","url":null,"abstract":"<div><div>The increasing complexity and scale of cosmological N-body simulations, driven by astronomical surveys like Euclid, call for a paradigm shift towards more sustainable and energy-efficient high-performance computing (HPC). The rising energy consumption of supercomputing facilities poses a significant environmental and financial challenge.</div><div>In this work, we build upon a recently developed GPU implementation of <span>PINOCCHIO</span>, a widely-used tool for the fast generation of dark matter (DM) halo catalogs, to investigate energy consumption. Using a different resource configuration, we confirmed the time-to-solution behavior observed in a companion study, and we use these runs to compare time-to-solution with energy-to-solution.</div><div>By profiling the code on various HPC platforms with a newly developed implementation of the Power Measurement Toolkit (PMT), we demonstrate an <span><math><mrow><mn>8</mn><mo>×</mo></mrow></math></span> reduction in energy-to-solution and <span><math><mrow><mn>8</mn><mo>×</mo></mrow></math></span> speed-up in time-to-solution compared to the CPU-only version. Taken together, these gains translate into an overall efficiency improvement of up to <span><math><mrow><mn>64</mn><mo>×</mo></mrow></math></span>. Our results show that the GPU-accelerated <span>PINOCCHIO</span> not only achieves substantial speed-up, making the generation of large-scale mock catalogs more tractable, but also significantly reduces the energy footprint of the simulations. This work represents an step towards “green-aware” scientific computing in cosmology, proving that performance and sustainability can be simultaneously achieved.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101060"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976729","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

Improving Bayesian inference in PTA data analysis: Importance nested sampling with Normalizing Flows 改进PTA数据分析中的贝叶斯推断：使用归一化流的重要性嵌套抽样

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.2026.101061

Eleonora Villa , Golam Mohiuddin Shaifullah , Andrea Possenti , Carmelita Carbone

引用次数: 0

Tracing correlations between galaxy properties across the Cosmic Web: An IllustrisTNG-based study 通过宇宙网追踪星系属性之间的相关性：一项基于illustristng的研究

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2025-11-21 DOI: 10.1016/j.ascom.2025.101023

Anindita Nandi , Biswajit Pandey , Prakash Sarkar

引用次数: 0

ArXSP: A python-based modular application for the reduction of digitized archival spectra ArXSP：一个基于python的模块化应用程序，用于减少数字化档案光谱

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2025-12-18 DOI: 10.1016/j.ascom.2025.101050

I.M. Izmailova , A.Zh. Umirbayeva , M.K. Khassanov , L. Aktay , S.A. Shomshekova

{"title":"ArXSP: A python-based modular application for the reduction of digitized archival spectra","authors":"I.M. Izmailova , A.Zh. Umirbayeva , M.K. Khassanov , L. Aktay , S.A. Shomshekova","doi":"10.1016/j.ascom.2025.101050","DOIUrl":"10.1016/j.ascom.2025.101050","url":null,"abstract":"<div><div>We present a methodology for the reduction of archival spectral data together with the description of a newly developed Python-based software package featuring an interactive graphical interface. The work is primarily aimed at processing spectra obtained with electron–optical converters (EOCs), which are characterized by geometric distortions induced by the magnetic field of the registration system. Such data are preserved, in particular, in the archive of the Fesenkov Astrophysical Institute (FAI), which contains about 10,000 photographic plates. These distortions, along with the need to transform the optical density of the photographic material into relative intensity, cannot be corrected by standard astronomical packages such as <span>IRAF</span> and therefore require a dedicated approach. Historically, reductions at FAI were performed using a program written in the <span>Microsoft QuickC</span> language for computing platforms of the 1990s, rendering it incompatible with modern operating systems. The new package is implemented with the <span>PyQt5</span> framework, retaining the logic of the original code while extending its functionality. The implemented algorithms include image rotation and cropping, geometric distortion correction, construction of the characteristic curve linking optical density and intensity, and direct conversion of pixel values in object spectra. The developed software ensures reproducible reduction of archival spectra and provides a cross-platform environment with potential for further extensions.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101050"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840026","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

Modified homotopy perturbation technique for solving third-order nonlinear Lane–Emden equations 求解三阶非线性Lane-Emden方程的修正同伦摄动技术

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2025-12-02 DOI: 10.1016/j.ascom.2025.101039

Vikash Kumar Sinha

引用次数: 0

NeuroStarMap: Neural Network encoding of Gaia’s distance ladder 神经星图：盖亚距离阶梯的神经网络编码

IF 1.8 4区物理与天体物理

Astronomy and Computing Pub Date : 2026-04-01 Epub Date: 2025-12-30 DOI: 10.1016/j.ascom.2025.101056

L. Brolli , C. Fruncillo , S. Zimotti , S. Tortora , L. Maina , A. Petrone , M. Gai , D. Busonero

引用次数: 0