Astronomy and Computing最新文献

{"title":"Cosmological anisotropies with Finsler–Randers geometry and large-scale observations","authors":"J. Praveen,&nbsp;S.K. Narasimhamurthy","doi":"10.1016/j.ascom.2025.101001","DOIUrl":"10.1016/j.ascom.2025.101001","url":null,"abstract":"<div><div>We investigate a cosmological model grounded in Finsler–Randers geometry introducing anisotropic corrections via the Barthel connection and a redshift-dependent parameter of the form <span><math><mrow><mi>η</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow><mo>=</mo><mn>1</mn><mo>+</mo><mi>n</mi><mi>z</mi></mrow></math></span>. This framework extends standard cosmology by incorporating directional dependencies in spacetime and modifies the evolution of principal cosmological parameters including the Hubble parameter <span><math><mrow><mi>H</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow></mrow></math></span>, deceleration parameter <span><math><mrow><mi>q</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow></mrow></math></span> and dark energy equation of state <span><math><mrow><mi>ω</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow></mrow></math></span>. By fitting the model to current Cosmic Chronometers (CC), Baryon Acoustic Oscillations (BAO), and Pantheon+ supernova datasets, we constrain the anisotropy parameter <span><math><mi>n</mi></math></span> and obtain updated best-fit values for <span><math><msub><mrow><mi>H</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>, <span><math><msub><mrow><mi>Ω</mi></mrow><mrow><mi>m</mi><mn>0</mn></mrow></msub></math></span>, <span><math><mi>n</mi></math></span> and <span><math><msub><mrow><mi>ω</mi></mrow><mrow><mi>DE</mi></mrow></msub></math></span>. Our results indicate that the linear anisotropy can lead to non-trivial modifications in cosmic expansion with the predicted Hubble constant <span><math><mrow><msub><mrow><mi>H</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>=</mo><mn>68</mn><mo>.</mo><mn>56</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>10</mn></mrow></math></span> km/s/Mpc lying between the Planck and SH0ES values, thereby partially alleviating the Hubble tension. This study demonstrates that geometric anisotropy within the Finsler–Randers framework provides a physically motivated extension to <span><math><mi>Λ</mi></math></span>CDM and offers new possibilities of studying outstanding challenges in modern cosmology in Finsler geometric background.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101001"},"PeriodicalIF":1.8,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100077","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 XMM-Newton and INTEGRAL operational ground segment: Synergetic systems and software operated for more than two decades","authors":"Marcus G.F. Kirsch ,&nbsp;Greta De Marco ,&nbsp;Frank Dreger ,&nbsp;Aniris Inojosa ,&nbsp;Gianluca Gaudenzi ,&nbsp;Jutta M. Huebner ,&nbsp;Sebastian Kriege ,&nbsp;Richard Southworth ,&nbsp;Uwe Weissmann","doi":"10.1016/j.ascom.2025.101000","DOIUrl":"10.1016/j.ascom.2025.101000","url":null,"abstract":"<div><div>The European Space Agency (ESA) has been operating the XMM-Newton and INTEGRAL observatories for over two decades, providing invaluable high-energy astrophysical data. Both missions share a synergistic operational ground segment, allowing for streamlined mission control, automation enhancements, and optimized resource utilization. This paper discusses the evolution of the mission control system, the integration of automation for spacecraft and instrument operations, and the adoption of virtualization technologies to ensure long-term mission sustainability. Additionally, it highlights the challenges of maintaining real-time telemetry-driven operations while minimizing operational costs. Lessons learned from these missions provide key insights for future ESA science operations. A key outcome highlighted in this work is the long-term success of retaining the legacy SOCS-2000 system, which has reliably operated for over two decades despite evolving hardware and software environments.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101000"},"PeriodicalIF":1.8,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050404","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":"AstroClearNet: Deep image prior for multi-frame astronomical image restoration","authors":"Yashil Sukurdeep ,&nbsp;Fausto Navarro ,&nbsp;Tamás Budavári","doi":"10.1016/j.ascom.2025.100999","DOIUrl":"10.1016/j.ascom.2025.100999","url":null,"abstract":"<div><div>Recovering high-fidelity images of the night sky from blurred observations is a fundamental problem in astronomy, where traditional methods typically fall short. In ground-based astronomy, combining multiple exposures to enhance signal-to-noise ratios is further complicated by variations in the point-spread function caused by atmospheric turbulence. In this work, we present a self-supervised multi-frame method, based on deep image priors, for denoising, deblurring, and coadding ground-based exposures. Central to our approach is a carefully designed convolutional neural network that integrates information across multiple observations and enforces physically motivated constraints. We demonstrate the method’s potential by processing Hyper Suprime-Cam exposures, yielding promising preliminary results with sharper restored images.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"53 ","pages":"Article 100999"},"PeriodicalIF":1.8,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988323","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":"Morphological classification of eclipsing binary stars using computer vision methods","authors":"Š. Parimucha ,&nbsp;M. Gabdeev ,&nbsp;Y. Markus ,&nbsp;M. Vaňko ,&nbsp;P. Gajdoš","doi":"10.1016/j.ascom.2025.100998","DOIUrl":"10.1016/j.ascom.2025.100998","url":null,"abstract":"<div><div>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 (<em>ResNet50</em>) and vision transformers (<em>vit_base_patch16_224</em>), 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 (<span><math><mrow><mo>&gt;</mo><mn>96</mn><mtext>%</mtext></mrow></math></span>) on validation data across multiple passbands (Gaia <span><math><mi>G</mi></math></span>, <span><math><mi>I</mi></math></span>, and <span><math><mrow><mi>T</mi><mi>E</mi><mi>S</mi><mi>S</mi></mrow></math></span>) and demonstrated strong performance (<span><math><mrow><mo>&gt;</mo><mn>94</mn><mtext>%</mtext></mrow></math></span>, up to 100% for <span><math><mrow><mi>T</mi><mi>E</mi><mi>S</mi><mi>S</mi></mrow></math></span>) 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.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"53 ","pages":"Article 100998"},"PeriodicalIF":1.8,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903854","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":"PWV and coherence time for Tassemit by ERA5","authors":"Tarik Mouhtafid,&nbsp;M. Sabil,&nbsp;Z. Ihsane,&nbsp;S. Oujaoura,&nbsp;E.A. Siher","doi":"10.1016/j.ascom.2025.100981","DOIUrl":"10.1016/j.ascom.2025.100981","url":null,"abstract":"<div><div>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 <span><math><msub><mrow><mi>τ</mi></mrow><mrow><mi>O</mi><mi>A</mi></mrow></msub></math></span>, and scintillation rate <span><math><msubsup><mrow><mi>σ</mi></mrow><mrow><mi>I</mi></mrow><mrow><mn>2</mn></mrow></msubsup></math></span>. The latter are key astroclimatic parameters for qualifying of new astronomical sites.</div><div>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 <span><math><msub><mrow><mi>τ</mi></mrow><mrow><mi>O</mi><mi>A</mi></mrow></msub></math></span> and scintillation rate <span><math><msubsup><mrow><mi>σ</mi></mrow><mrow><mi>I</mi></mrow><mrow><mn>2</mn></mrow></msubsup></math></span> at the Observatorio del Roque de Los Muchachos ORM: The results were encouraging, as the recalculated values of <span><math><msub><mrow><mi>τ</mi></mrow><mrow><mi>O</mi><mi>A</mi></mrow></msub></math></span> and <span><math><msubsup><mrow><mi>σ</mi></mrow><mrow><mi>I</mi></mrow><mrow><mn>2</mn></mrow></msubsup></math></span> for the Tassemit site showed improved precision and consistency with expected atmospheric conditions. We have also recalculated <span><math><msub><mrow><mi>τ</mi></mrow><mrow><mi>O</mi><mi>A</mi></mrow></msub></math></span> and <span><math><msubsup><mrow><mi>σ</mi></mrow><mrow><mi>I</mi></mrow><mrow><mn>2</mn></mrow></msubsup></math></span> for the Tassemit site, and we have the medians of the values of <span><math><msub><mrow><mi>τ</mi></mrow><mrow><mi>O</mi><mi>A</mi></mrow></msub></math></span> and <span><math><msubsup><mrow><mi>σ</mi></mrow><mrow><mi>I</mi></mrow><mrow><mn>2</mn></mrow></msubsup></math></span> which are 5.93 ms and 0.00307 arcsec, respectively. This is used as a reference in astronomy.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"53 ","pages":"Article 100981"},"PeriodicalIF":1.8,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932222","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":"Deep learning-based McIntosh classification of sunspot groups","authors":"Xue Deng ,&nbsp;Yunfei Yang ,&nbsp;Xiaoli Zhang ,&nbsp;Song Feng ,&nbsp;Wei Dai ,&nbsp;Bo Liang ,&nbsp;Jianping Xiong","doi":"10.1016/j.ascom.2025.100995","DOIUrl":"10.1016/j.ascom.2025.100995","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"53 ","pages":"Article 100995"},"PeriodicalIF":1.8,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878377","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":"Cosmological implications and constraints in Hoyle–Narlikar gravity theory","authors":"Dinesh Chandra Maurya ,&nbsp;Y. Aditya","doi":"10.1016/j.ascom.2025.100993","DOIUrl":"10.1016/j.ascom.2025.100993","url":null,"abstract":"<div><div>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 <span><math><mrow><mi>C</mi><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>=</mo><mi>t</mi><mo>+</mo><mo>∫</mo><mfrac><mrow><mn>1</mn></mrow><mrow><msup><mrow><mi>a</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></mfrac><mo>+</mo><msub><mrow><mi>c</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>,</mo><mi>t</mi><mo>+</mo><mo>∫</mo><mfrac><mrow><mn>1</mn></mrow><mrow><mi>a</mi></mrow></mfrac><mo>+</mo><msub><mrow><mi>c</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>,</mo><mi>t</mi><mo>+</mo><mo>∫</mo><mfrac><mrow><mn>1</mn></mrow><mrow><msqrt><mrow><mi>a</mi></mrow></msqrt></mrow></mfrac><mo>+</mo><msub><mrow><mi>c</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></math></span> 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 <span><math><msub><mrow><mi>H</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> and <span><math><msub><mrow><mi>Ω</mi></mrow><mrow><mi>m</mi><mn>0</mn></mrow></msub></math></span> by combining the examination of the CC and Pantheon datasets with <span><math><mrow><mn>1</mn><mi>σ</mi></mrow></math></span> and <span><math><mrow><mn>2</mn><mi>σ</mi></mrow></math></span> 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 <span><math><msub><mrow><mi>ω</mi></mrow><mrow><mi>e</mi><mi>f</mi><mi>f</mi></mrow></msub></math></span>, the deceleration parameter <span><math><mrow><mi>q</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow></mrow></math></span>, and Om diagnostic analysis to group the models into stages of evolution. We also discuss the present age of the universe.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"53 ","pages":"Article 100993"},"PeriodicalIF":1.8,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144813962","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":"OntoPortal-Astro, a semantic artefact catalogue for astronomy","authors":"Baptiste Cecconi ,&nbsp;Laura Debisschop ,&nbsp;Sébastien Derrière ,&nbsp;Mireille Louys ,&nbsp;Carmen Corre ,&nbsp;Nina Grau ,&nbsp;Clévent Jonquet","doi":"10.1016/j.ascom.2025.100991","DOIUrl":"10.1016/j.ascom.2025.100991","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"53 ","pages":"Article 100991"},"PeriodicalIF":1.8,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144894967","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 image simplification using Generative AI","authors":"Sai Teja Erukude,&nbsp;Lior Shamir","doi":"10.1016/j.ascom.2025.100990","DOIUrl":"10.1016/j.ascom.2025.100990","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"53 ","pages":"Article 100990"},"PeriodicalIF":1.9,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679229","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":"Charge reconstruction of HERD silicon charge detectors based on MLP","authors":"Longkun Yu ,&nbsp;Jing Wang ,&nbsp;Rui Qiao ,&nbsp;Ke Gong ,&nbsp;Wenxi Peng ,&nbsp;Jiaju Wei ,&nbsp;Bing Lu ,&nbsp;Dongya Guo ,&nbsp;Yaqing Liu ,&nbsp;Xuan Liu ,&nbsp;Chenxing Zhang ,&nbsp;Ming Xu ,&nbsp;Zhigang Wang ,&nbsp;Ruijie Wang ,&nbsp;Tianwei Bao ,&nbsp;Yongwei Dong ,&nbsp;Oscar Adriani ,&nbsp;Eugenio Berti ,&nbsp;Pietro Betti ,&nbsp;Jorge Casaus ,&nbsp;Nicola Zampa","doi":"10.1016/j.ascom.2025.100986","DOIUrl":"10.1016/j.ascom.2025.100986","url":null,"abstract":"<div><div>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-<span><math><mi>Z</mi></math></span> data, we also introduced transfer learning to improve charge reconstruction for high-<span><math><mi>Z</mi></math></span> 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 <span><math><mi>Z</mi></math></span> = 10 to <span><math><mi>Z</mi></math></span> = 28.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"53 ","pages":"Article 100986"},"PeriodicalIF":1.9,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614636","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}