Astronomy and Computing最新文献_第8页

Morphological classification of eclipsing binary stars using computer vision methods 用计算机视觉方法对食双星进行形态分类

IF 1.8 4区物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS

Astronomy and Computing

Pub Date : 2025-08-23 DOI: 10.1016/j.ascom.2025.100998

Š. Parimucha , M. Gabdeev , Y. Markus , M. Vaňko , P. Gajdoš

We present an application of computer vision methods to classify the light curves of eclipsing binaries (EB). We have used pre-trained models based on convolutional neural networks (ResNet50) and vision transformers (vit_base_patch16_224), which were fine-tuned on images created from synthetic datasets. To improve model generalisation and reduce overfitting, we developed a novel image representation by transforming phase-folded light curves into polar coordinates combined with hexbin visualisation. Our hierarchical approach in the first stage classifies systems into detached and overcontact types, and in the second stage identifies the presence or absence of spots. The binary classification models achieved high accuracy (

> 96 %

) on validation data across multiple passbands (Gaia

G

,

I

, and

T E S S

) and demonstrated strong performance (

> 94 %

, up to 100% for

T E S S

) when tested on extensive observational data from the OGLE, DEBCat, and WUMaCat catalogues. While the primary binary classification was highly successful, the secondary task of automated spot detection performed poorly, revealing a significant limitation of our models for identifying subtle photometric features. This study highlights the potential of computer vision for EB morphological classification in large-scale surveys, but underscores the need for further research into robust, automated spot detection.

提出了一种应用计算机视觉方法对食双星（EB）光曲线进行分类的方法。我们使用了基于卷积神经网络（ResNet50）和视觉转换器（vit_base_patch16_224）的预训练模型，这些模型对从合成数据集创建的图像进行了微调。为了提高模型的泛化和减少过拟合，我们开发了一种新的图像表示方法，将相位折叠光曲线转换为极坐标并结合hexbin可视化。我们的分层方法在第一阶段将系统分为分离和过度接触类型，在第二阶段确定斑点的存在或不存在。二元分类模型在多个波段（Gaia G、I和TESS）的验证数据上获得了很高的准确率（>96%），并且在OGLE、DEBCat和WUMaCat目录的大量观测数据上进行测试时表现出了很强的性能（>94%， TESS高达100%）。虽然主要的二元分类非常成功，但自动斑点检测的次要任务表现不佳，这揭示了我们的模型在识别细微光度特征方面的显着局限性。这项研究强调了计算机视觉在大规模调查中EB形态分类的潜力，但强调了对鲁棒、自动化斑点检测的进一步研究的必要性。

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引用次数: 0

PWV and coherence time for Tassemit by ERA5 基于ERA5的tassembly的PWV和相干时间

IF 1.8 4区物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS

Astronomy and Computing

Pub Date : 2025-08-23 DOI: 10.1016/j.ascom.2025.100981

Tarik Mouhtafid, M. Sabil, Z. Ihsane, S. Oujaoura, E.A. Siher

In this publication, we follow up our work in the High Atlas, in particular at of Tassemit in the Beni Mellal mountains. Our database is drawn from reanalyses ERA5 by the European Centre for Weather Forecasting, covering a period of 10 years, evaluating new parameters: precipitation water vapor (PWV), cloud cover, wind speed, geopotential, total precipitation, evaporation, coherence time

τ_{O A}

, and scintillation rate

σ_{I}^{2}

. The latter are key astroclimatic parameters for qualifying of new astronomical sites.

The results highlight favorable atmospheric conditions, with a mean and median PWV of 3.68 and 3.35 mm, respectively. The site also features low cloud cover, moderate wind speeds, and low total precipitation, ensuring climatic stability suitable for advanced astronomical projects. We compared the measurements made by MASS-DIMM and calculated by ERA5 for coherence time

τ_{O A}

and scintillation rate

σ_{I}^{2}

at the Observatorio del Roque de Los Muchachos ORM: The results were encouraging, as the recalculated values of

τ_{O A}

and

σ_{I}^{2}

for the Tassemit site showed improved precision and consistency with expected atmospheric conditions. We have also recalculated

τ_{O A}

and

σ_{I}^{2}

for the Tassemit site, and we have the medians of the values of

τ_{O A}

and

σ_{I}^{2}

which are 5.93 ms and 0.00307 arcsec, respectively. This is used as a reference in astronomy.

在本出版物中，我们继续我们在高地图集的工作，特别是在贝尼梅拉尔山脉的塔塞米特。我们的数据库来自欧洲天气预报中心ERA5的再分析，涵盖了10年的时间，评估了新的参数：降水水汽（PWV）、云量、风速、位势、总降水、蒸发、相干时间τOA和闪烁率σI2。后者是确定新的天文地点的关键天文参数。结果显示大气条件有利，平均和中位数PWV分别为3.68和3.35 mm。该站点还具有低云量、中等风速和低总降水量的特点，确保了适合高级天文项目的气候稳定性。我们比较了MASS-DIMM和ERA5在Roque de Los Muchachos ORM天文台的相干时间τOA和闪烁速率σI2的测量结果，结果令人鼓舞，因为Tassemit站点的τOA和σI2的重新计算值显示出更高的精度和与预期大气条件的一致性。我们还重新计算了Tassemit站点的τOA和σI2，得到τOA和σI2的中位数分别为5.93 ms和0.00307 arcsec。这被用作天文学的参考。

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引用次数: 0

Deep learning-based McIntosh classification of sunspot groups 基于深度学习的太阳黑子群McIntosh分类

IF 1.8 4区物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS

Astronomy and Computing

Pub Date : 2025-08-16 DOI: 10.1016/j.ascom.2025.100995

Xue Deng , Yunfei Yang , Xiaoli Zhang , Song Feng , Wei Dai , Bo Liang , Jianping Xiong

Different McIntosh classes of sunspot groups are associated with the occurrence of different levels flares. Thus, accurately classifying sunspot groups is of great significance for flare prediction. In this paper, a deep learning model named SungDC is proposed for the McIntosh classification of sunspot groups. The SungDC is designed as a single multi-classifier to simultaneously perform the classification of 60 McIntosh classes. An AGCM module is incorporated to enhance its feature extraction capability. An LCFPN neck is designed to mitigate the distortion of sunspot group features, thereby improving the quality of features. A deep learning dataset sourced from SDO/HMI continuous spectral full-disk solar images was built. In addition, a region-level data rotation augmentation technique (RLR) was improved to alleviate the problem of sample imbalance. The experimental results show that the AP, AR, and AF metrics of the SungDC are 0.645, 0.586, and 0.608, respectively. The precisions of the dki, eki, ehc, dkc, ekc, and fkc sunspot groups, which are tightly associated with M- and X-class flares, are 0.905, 0.828, 0.920, 0.710, 0.711, and 0.463, respectively. It demonstrates that the multi-classification challenge posed by sunspot groups can be feasibly addressed by deep learning methodologies. This method can also serve for research on flare prediction.

不同麦金托什类别的太阳黑子群与不同水平的耀斑的发生有关。因此，准确分类太阳黑子群对耀斑预测具有重要意义。本文提出了一种用于太阳黑子群McIntosh分类的深度学习模型SungDC。SungDC被设计成一个单一的多分类器，同时执行60个麦金托什类的分类。集成了AGCM模块，增强了特征提取能力。为了减轻太阳黑子群特征失真，提高特征质量，设计了LCFPN颈。建立了基于SDO/HMI连续光谱全盘太阳图像的深度学习数据集。此外，改进了区域级数据旋转增强技术（RLR），以缓解样本不平衡问题。实验结果表明，SungDC的AP、AR和AF指标分别为0.645、0.586和0.608。与M级和x级耀斑密切相关的太阳黑子群dki、eki、ehc、dkc、ekc和fkc的精度分别为0.905、0.828、0.920、0.710、0.711和0.463。这表明，太阳黑子群带来的多分类挑战可以通过深度学习方法解决。该方法也可用于耀斑预测的研究。

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引用次数: 0

Cosmological implications and constraints in Hoyle–Narlikar gravity theory Hoyle-Narlikar引力理论的宇宙学含义和约束

IF 1.8 4区物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS

Astronomy and Computing

Pub Date : 2025-08-07 DOI: 10.1016/j.ascom.2025.100993

Dinesh Chandra Maurya , Y. Aditya

We discuss some cosmological implications and constraints in Hoyle–Narlikar’s creation field theory to explain the cosmic evolution of the expanding universe. To obtain an analytical solution of the modified field equations, we use the specific choices of the creation field

C (t) = t + \int \frac{1}{a^{2}} + c_{1}, t + \int \frac{1}{a} + c_{2}, t + \int \frac{1}{\sqrt{a}} + c_{3}

and get three alternatives as Model I, II, and III. For the background source of dust fluid, we find the Hubble function for each model and subsequently obtain values for the model parameters

H_{0}

and

Ω_{m 0}

by combining the examination of the CC and Pantheon datasets with

1 σ

and

2 σ

confidence levels. Using these values of model parameters, we measure the values of derived parameters and discuss the results by presenting the geometrical behavior of cosmological parameters. We talk about three models side by side and compare them using the effective equation of state parameter

ω_{e f f}

, the deceleration parameter

q (z)

, and Om diagnostic analysis to group the models into stages of evolution. We also discuss the present age of the universe.

我们讨论了Hoyle-Narlikar创造场理论中的一些宇宙学含义和限制，以解释宇宙膨胀的宇宙演化。为了得到修正场方程的解析解，我们使用创建场C(t)=t+∫1a2+c1,t+∫1a+c2，t+∫1a+c3的具体选择，得到模型I， II和III三种选择。对于尘埃流体的背景源，我们找到了每个模型的哈勃函数，然后通过结合CC和Pantheon数据集的1σ和2σ置信水平的检查，获得了模型参数H0和Ωm0的值。利用这些模型参数的值，我们测量了衍生参数的值，并通过给出宇宙学参数的几何行为来讨论结果。我们将三个模型并列讨论，并利用状态参数ωeff、减速参数q(z)的有效方程和Om诊断分析对它们进行比较，将模型划分为不同的演化阶段。我们还讨论了目前宇宙的年龄。

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引用次数: 0

OntoPortal-Astro, a semantic artefact catalogue for astronomy ontopportal - astro，一个天文学的语义人工制品目录

IF 1.8 4区物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS

Astronomy and Computing

Pub Date : 2025-08-05 DOI: 10.1016/j.ascom.2025.100991

Baptiste Cecconi , Laura Debisschop , Sébastien Derrière , Mireille Louys , Carmen Corre , Nina Grau , Clévent Jonquet

The astronomy communities are widely recognised as mature communities for their open science practices. However, while their data ecosystems are rather advanced and permit efficient data interoperability, there are still gaps between these ecosystems. Semantic artefacts (SAs) – e.g., ontologies, thesauri, vocabularies or metadata schemas – are a means to bridge that gap as they allow to semantically described the data and map the underlying concepts. The increasing use of SAs in astronomy presents challenges in description, selection, evaluation, trust, and mappings. The landscape remains fragmented, with SAs scattered across various registries in diverse formats and structures – not yet fully developed or encoded with rich semantic web standards like OWL or SKOS – and often with overlapping scopes. Enhancing data semantic interoperability requires common platforms to catalogue, align, and facilitate the sharing of FAIR (Findable, Accessible, Interoperable and Reusable) SAs. In the frame of the FAIR-IMPACT project, we prototyped a SA catalogue for astronomy, heliophysics and planetary sciences. This exercise resulted in improved vocabulary and ontology management in the communities, and is now paving the way for better interdisciplinary data discovery and reuse. This article presents current practices in our discipline, reviews candidate SAs for such a catalogue, presents driving use cases and the perspective of a real production service for the astronomy community based on the OntoPortal technology, that will be called OntoPortal-Astro.

天文学团体因其开放的科学实践而被广泛认为是成熟的团体。然而，尽管他们的数据生态系统相当先进，并允许有效的数据互操作性，但这些生态系统之间仍然存在差距。语义工件（SAs）——例如，本体、词典、词汇表或元数据模式——是弥合这一差距的一种手段，因为它们允许对数据进行语义描述并映射底层概念。在天文学中越来越多地使用sa，在描述、选择、评估、信任和映射方面提出了挑战。这个领域仍然是碎片化的，sa以不同的格式和结构分散在不同的注册表中——还没有完全开发或用OWL或SKOS等丰富的语义web标准编码——并且经常有重叠的范围。增强数据语义互操作性需要通用平台来编目、对齐和促进FAIR（可查找、可访问、可互操作和可重用）sa的共享。在FAIR-IMPACT项目的框架内，我们为天文学、太阳物理学和行星科学制作了一个SA目录的原型。这一实践改进了社区中的词汇表和本体管理，现在为更好的跨学科数据发现和重用铺平了道路。本文介绍了我们学科的当前实践，回顾了这样一个目录的候选应用程序，介绍了驱动用例和基于ontopportal技术的天文社区实际生产服务的视角，该技术将被称为ontopportal - astro。

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引用次数: 0

Galaxy image simplification using Generative AI 使用生成式AI简化星系图像

IF 1.9 4区物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS

Astronomy and Computing

Pub Date : 2025-07-17 DOI: 10.1016/j.ascom.2025.100990

Sai Teja Erukude, Lior Shamir

Modern digital sky surveys have been acquiring images of billions of galaxies. While these images often provide sufficient details to analyze the shape of the galaxies, accurate analysis of such high volumes of images requires effective automation. Current solutions often rely on machine learning annotation of the galaxy images based on a set of pre-defined classes. Here we introduce a new approach to galaxy image analysis that is based on generative AI. The method simplifies the galaxy images and automatically converts them into a “skeletonized” form. The simplified images allow accurate measurements of the galaxy shapes and analysis that is not limited to a certain pre-defined set of classes. We demonstrate the method by applying it to galaxy images acquired by the DESI Legacy Survey. The code and data used in the method are publicly available. The method was applied to 125,000 DESI Legacy Survey images, and the catalog of the simplified images is publicly available.

现代数字巡天已经获得了数十亿个星系的图像。虽然这些图像通常提供足够的细节来分析星系的形状，但对如此大量的图像进行准确分析需要有效的自动化。目前的解决方案通常依赖于基于一组预定义类的星系图像的机器学习注释。本文介绍了一种基于生成式人工智能的星系图像分析新方法。该方法简化了星系图像，并自动将其转换为“骨架”形式。简化的图像允许对星系形状的精确测量和分析，而不局限于特定的预定义类别。我们通过将其应用于由DESI遗产调查获得的星系图像来演示该方法。方法中使用的代码和数据是公开的。该方法应用于125,000张DESI Legacy Survey图像，简化图像的目录是公开的。

引用次数: 0

Charge reconstruction of HERD silicon charge detectors based on MLP 基于MLP的HERD硅电荷探测器的电荷重构

IF 1.9 4区物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS

Astronomy and Computing

Pub Date : 2025-07-11 DOI: 10.1016/j.ascom.2025.100986

Longkun Yu , Jing Wang , Rui Qiao , Ke Gong , Wenxi Peng , Jiaju Wei , Bing Lu , Dongya Guo , Yaqing Liu , Xuan Liu , Chenxing Zhang , Ming Xu , Zhigang Wang , Ruijie Wang , Tianwei Bao , Yongwei Dong , Oscar Adriani , Eugenio Berti , Pietro Betti , Jorge Casaus , Nicola Zampa

The High Energy Cosmic-Radiation Detection (HERD) is an experimental facility designed for the study of space astronomy and particle astrophysics. The Silicon Charge Detector (SCD), as the outermost detector of HERD, has the primary objective of precisely measuring cosmic rays ranging from hydrogen to nickel. To enhance the charge resolution of the silicon charge detector by fully utilizing multi-channel information, this study employed Support Vector Machines (SVM) and Multi-Layer Perceptron (MLP) for charge reconstruction. Given the challenge of low statistics in high-

Z

data, we also introduced transfer learning to improve charge reconstruction for high-

Z

samples. Compared to our previous results (Zhanget al., 2024), the machine learning algorithm achieved an average improvement of approximately 9.8% in charge resolution for heavy nuclei with

Z

= 10 to

Z

= 28.

高能宇宙辐射探测（HERD）是为研究空间天文学和粒子天体物理学而设计的实验设备。硅电荷探测器（SCD）作为HERD最外层的探测器，其主要目标是精确测量从氢到镍的宇宙射线。为了充分利用多通道信息，提高硅电荷探测器的电荷分辨率，本研究采用支持向量机（SVM）和多层感知器（MLP）进行电荷重构。考虑到高z数据的低统计量的挑战，我们还引入了迁移学习来改进高z样本的电荷重建。与我们之前的结果（Zhanget al., 2024）相比，机器学习算法在Z = 10到Z = 28的重核电荷分辨率上平均提高了约9.8%。

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引用次数: 0

From global climate models (GCMs) to exoplanet spectra with the Global Emission Spectra (GlobES) 从全球气候模式（GCMs）到具有全球发射光谱（GlobES）的系外行星光谱

IF 1.9 4区物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS

Astronomy and Computing

Pub Date : 2025-07-10 DOI: 10.1016/j.ascom.2025.100982

Thomas J. Fauchez , Geronimo L. Villanueva , Vincent Kofman , Gabriella Suissa , Ravi K. Kopparapu

In the quest to understand the climates and atmospheres of exoplanets, 3D global climate models (GCMs) have become indispensable. The ability of GCMs to predict atmospheric conditions complements exoplanet observations, creating a feedback loop that enhances our understanding of exoplanetary atmospheres and their environments. This paper discusses the capabilities of the Global Exoplanet Spectra (GlobES) module of the Planetary Spectrum Generator (PSG), which incorporates 3D atmospheric and surface information into spectral simulations, offering a free, accessible tool for the scientific community to study realistic planetary atmospheres. Through detailed case studies, including simulations of TRAPPIST-1 b , TRAPPIST-1 e, and Earth around Sun, this paper demonstrates the use of GlobES and its effectiveness in simulating transit, emission and reflected spectra, thus supporting the ongoing development and refinement of observational strategies using the James Webb Space Telescope (JWST) and future mission concept studies (e.g., Habitable Worlds Observatory [HWO]) in exoplanet research.

为了了解系外行星的气候和大气，3D全球气候模型（GCMs）已经变得不可或缺。gcm预测大气条件的能力与系外行星观测相补充，形成了一个反馈回路，增强了我们对系外行星大气及其环境的理解。本文讨论了行星光谱发生器（PSG）的全球系外行星光谱（GlobES）模块的功能，该模块将三维大气和地表信息纳入光谱模拟，为科学界研究真实的行星大气提供了一个免费的、可访问的工具。通过详细的案例研究，包括对TRAPPIST-1 b、TRAPPIST-1 e和地球绕日的模拟，本文展示了GlobES的使用及其在模拟凌日、发射和反射光谱方面的有效性，从而支持詹姆斯·韦伯太空望远镜（JWST）观测策略的持续发展和完善，以及未来任务概念研究（如宜居世界天文台[HWO]）在系外行星研究中的应用。

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引用次数: 0

Survey-wide asteroid discovery with a high-performance computing enabled non-linear digital tracking framework 用高性能计算支持的非线性数字跟踪框架进行全范围的小行星发现

IF 1.9 4区物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS

Astronomy and Computing

Pub Date : 2025-07-09 DOI: 10.1016/j.ascom.2025.100987

N. Golovich , T. Steil , A. Geringer-Sameth , K. Iwabuchi , R. Dozier , R. Pearce

Modern astronomical surveys detect asteroids by linking together their appearances across multiple images taken over time. This approach faces limitations in detecting faint asteroids and handling the computational complexity of trajectory linking. We present a novel method that adapts “digital tracking” – traditionally used for short-term linear asteroid motion across images – to work with large-scale synoptic surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time (Rubin/LSST). Our approach combines hundreds of sparse observations of individual asteroids across their non-linear orbital paths to enhance detection sensitivity by several magnitudes. To address the computational challenges of processing massive data sets and dense orbital phase spaces, we developed a specialized high-performance computing architecture. We demonstrate the effectiveness of our method through experiments that take advantage of the extensive computational resources at Lawrence Livermore National Laboratory. This work enables the detection of significantly fainter asteroids in existing and future survey data, potentially increasing the observable asteroid population by orders of magnitude across different orbital families, from near-Earth objects (NEOs) to Kuiper belt objects (KBOs).

现代天文调查通过将它们的外观与随时间拍摄的多幅图像联系起来来探测小行星。这种方法在探测微弱小行星和处理轨迹连接的计算复杂性方面存在局限性。我们提出了一种新的方法，将“数字跟踪”——传统上用于小行星在图像上的短期线性运动——用于大规模的天气调查，如维拉鲁宾天文台时空遗产调查（Rubin/LSST）。我们的方法结合了对单个小行星在其非线性轨道路径上的数百次稀疏观测，以提高几个数量级的探测灵敏度。为了解决处理大量数据集和密集轨道相位空间的计算挑战，我们开发了一种专门的高性能计算架构。我们通过利用劳伦斯利弗莫尔国家实验室广泛的计算资源的实验证明了我们方法的有效性。这项工作能够在现有和未来的调查数据中检测到明显较暗的小行星，潜在地增加了不同轨道家族的可观测小行星数量，从近地天体（NEOs）到柯伊伯带天体（kbo）。

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引用次数: 0

A cosmological probe in a theory of higher-order gravity 高阶引力理论中的宇宙探测器

IF 1.9 4区物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS

Astronomy and Computing

Pub Date : 2025-07-05 DOI: 10.1016/j.ascom.2025.100988

Shaily , A. Srivastava , H.V. Chauhan , A. Pratap , J.K. Singh

In this work, we study a cosmological model in a modified gravity containing the Ricci scalar

R

and the Gauss–Bonnet invariant

G

in a linear combination. We establish the model by using a model’s unconventional technique by parameterizing the scale factor, in which the model begins with a finite spatial volume at the time of the early evolution of the Universe and exhibits an accelerating expansion at later times. The expansion of the Universe transitions from an early decelerating state to a late-time accelerating state. We use the various diagnostic techniques to examine the stability of the model. The prime goal of studying the model is to obtain precise cosmological constraints for

H_{0}

,

α

, and

t_{0}

, and discuss the various physical features of the model according to constrained model parameters. Finally, we find that our model is a stable expanding and accelerating quintessence dark energy model in late times.

在这项工作中，我们研究了一个包含里奇标量R和高斯-博内不变量G的线性组合的修正重力下的宇宙学模型。我们采用了一种非常规的模型技术，通过参数化尺度因子来建立模型，其中模型在宇宙早期演化时以有限的空间体积开始，并在后期表现出加速膨胀。宇宙的膨胀从早期的减速状态过渡到后期的加速状态。我们使用各种诊断技术来检查模型的稳定性。研究该模型的主要目标是获得H0、α和t0的精确宇宙学约束，并根据约束的模型参数讨论模型的各种物理特征。最后，我们发现我们的模型在后期是一个稳定的膨胀和加速的精粹暗能量模型。

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引用次数: 0