Astronomy and Computing最新文献

{"title":"A general relativistic hydrodynamic simulation code for studying advective, sub-Keplerian accretion flow onto black holes","authors":"S.K. Garain","doi":"10.1016/j.ascom.2025.100974","DOIUrl":"10.1016/j.ascom.2025.100974","url":null,"abstract":"<div><div>In this paper, we describe a general relativistic hydrodynamics simulation code which is developed to simulate advective accretion flow onto black holes. We are particularly interested in the accretion simulations of sub-Keplerian matter in the close vicinity of black holes. Due to the presence of centrifugal barrier, a nearly free-falling sub-Keplerian accretion flow slows down close to a black hole and can even pass through shocks before accelerating again to the black hole. We design our simulation code using the high resolution shock capturing scheme so that such shock structures can be captured and analyzed for relevance. In this paper, we describe our implementation and validation of the code against a few known analytical and numerical results of sub-Keplerian matter accretion.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"53 ","pages":"Article 100974"},"PeriodicalIF":1.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297827","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":"Energy and polarization based online interference mitigation in radio interferometry","authors":"S. Yatawatta,&nbsp;A.-J. Boonstra,&nbsp;C.P. Broekema","doi":"10.1016/j.ascom.2025.100973","DOIUrl":"10.1016/j.ascom.2025.100973","url":null,"abstract":"<div><div>Radio frequency interference (RFI) is a persistent contaminant in terrestrial radio astronomy. While new radio interferometers are becoming operational, novel sources of RFI are also emerging. In order to strengthen the mitigation of RFI in modern radio interferometers, we propose an online RFI mitigation scheme that can be run in the correlator of such interferometers. We combine statistics based on the energy as well as the polarization alignment of the correlated signal to develop an online RFI mitigation scheme that can be applied to a data stream produced by the correlator in real-time, especially targeted at low duty-cycle or transient RFI detection. In order to improve the computational efficiency, we explore the use of both single precision and half precision floating point operations in implementing the RFI mitigation algorithm. This ideally suits its deployment in accelerator computing devices such as graphics processing units (GPUs) as used by the LOFAR correlator. We provide results based on simulations and real data to demonstrate the efficacy of the proposed method.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"53 ","pages":"Article 100973"},"PeriodicalIF":1.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144289191","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":"Geometric analysis of variability of radiocarbon abundances and solar activity","authors":"Isao Shoji ,&nbsp;Tadafumi Takata ,&nbsp;Yoshihiko Mizumoto","doi":"10.1016/j.ascom.2025.100971","DOIUrl":"10.1016/j.ascom.2025.100971","url":null,"abstract":"<div><div>This paper discusses a geometric time series analysis of variability of radiocarbon abundances and solar activity. Cosmic rays sometimes have a severe impact on the Earth’s environment. They interact with atoms in the atmosphere, producing radionuclides such as radiocarbon. Consequently, the ratio of radiocarbon to stable carbon in the atmosphere fluctuates on the basis of the influx of cosmic rays. Consequently, historical records of radiocarbon abundances show the intensity of cosmic rays in the past. The International Calibration (IntCal) curve widely used for radiocarbon dating is also used as a reference for such records. From a statistical point of view, detecting rapid changes in radiocarbon abundances, which are considered indicators of intense cosmic rays, from the IntCal data is challenging because such variations are generally smoothed out during the calibration process. However, in this study, we used a geometric time series analysis method to identify several rapid changes directly from the IntCal data. These variations in radiocarbon abundances also serve as indicators of solar activity. We also detected signals corresponding to solar grand minima and grand maxima by correlating them with temporal changes in the vector field derived from the dynamical system characterized by a second-order random oscillation.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"53 ","pages":"Article 100971"},"PeriodicalIF":1.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313610","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":"Impact of cosmic web on galaxy properties and their correlations: Insights from Principal Component Analysis","authors":"Anindita Nandi,&nbsp;Biswajit Pandey","doi":"10.1016/j.ascom.2025.100972","DOIUrl":"10.1016/j.ascom.2025.100972","url":null,"abstract":"<div><div>We use Principal Component Analysis (PCA) to analyse a volume-limited sample from the SDSS and explore how cosmic web environments affect the interrelations between various galaxy properties, such as <span><math><mrow><mo>(</mo><mi>u</mi><mo>−</mo><mi>r</mi><mo>)</mo></mrow></math></span> colour, stellar mass, specific star formation rate, metallicity, morphology, and <span><math><mrow><mi>D</mi><mn>4000</mn></mrow></math></span>. Our analysis reveals that the first three principal components (PC1, PC2 and PC3) account for approximately <span><math><mrow><mo>∼</mo><mn>85</mn><mtext>%</mtext></mrow></math></span> of the data variance. We classify galaxies into different cosmic web environments based on the eigenvalues of the deformation tensor and compare PC1, PC2, PC3 across these environments, ensuring a mass-matched sample of equal size for each environment. PC1 is dominated by colour, sSFR, D4000, and morphology. It displays clear bimodality across all cosmic web environments, with sheets and clusters showing distinct preferences for negative and positive PC1 values, respectively. This variation reflects the strong role of environmental processes in regulating star formation. PC2 and PC3, respectively show positively and negatively skewed unimodal distributions in all environments. PC2 is primarily influenced by metallicity whereas PC3 is dominated by stellar mass. It indicates that metallicity evolves gradually and is less sensitive to environmental extremes, highlighting the importance of internal, secular processes. PC3 likely captures residual variation in stellar mass within the two main galaxy populations (star-forming and quiescent) separated by PC1. A Kolmogorov–Smirnov (KS) test confirms that the distributions of PC1, PC2 and PC3 differ significantly across environments, with a confidence level exceeding 99.99%. Furthermore, we calculate the normalized mutual information (NMI) between the principal components and individual galaxy properties within different cosmic web environments. A two-tailed t-test reveals that for each relationship and each pair of environments, the null hypothesis is rejected with a confidence level <span><math><mrow><mo>&gt;</mo><mn>99</mn><mo>.</mo><mn>99</mn><mtext>%</mtext></mrow></math></span>. Our analysis confirms that cosmic web environments play a significant role in shaping the correlations between galaxy properties.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"53 ","pages":"Article 100972"},"PeriodicalIF":1.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313613","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":"2025-10-01","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":"Survey-wide asteroid discovery with a high-performance computing enabled non-linear digital tracking framework","authors":"N. Golovich ,&nbsp;T. Steil ,&nbsp;A. Geringer-Sameth ,&nbsp;K. Iwabuchi ,&nbsp;R. Dozier ,&nbsp;R. Pearce","doi":"10.1016/j.ascom.2025.100987","DOIUrl":"10.1016/j.ascom.2025.100987","url":null,"abstract":"<div><div>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).</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"53 ","pages":"Article 100987"},"PeriodicalIF":1.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655266","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":"2025-10-01","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":"The evolutionary path of star-forming clumps in Hi-GAL","authors":"Y. Maruccia ,&nbsp;S. Cavuoti ,&nbsp;M. Brescia ,&nbsp;G. Riccio ,&nbsp;S. Molinari ,&nbsp;D. Elia ,&nbsp;E. Schisano","doi":"10.1016/j.ascom.2025.100985","DOIUrl":"10.1016/j.ascom.2025.100985","url":null,"abstract":"<div><div>Star formation (SF) studies are benefiting from the huge amount of data made available by recent large-area Galactic plane surveys conducted between <span><math><mrow><mn>2</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span> and 3 mm. Fully characterizing SF demands integrating far-infrared/sub-millimetre (FIR/sub-mm) data, tracing the earliest phases, with near-/mid-infrared (NIR/MIR) observations, revealing later stages characterized by Young Stellar Objects (YSOs) just before main sequence star appearance. However, the resulting dataset is often a complex mix of heterogeneous and intricate features, limiting the effectiveness of traditional analysis in uncovering hidden patterns and relationships. In this framework, machine learning emerges as a powerful tool to handle the complexity of feature-rich datasets and investigate potential physical connections between the cold dust component traced by FIR/sub-mm emission and the presence of YSOs. We present a study on the evolutionary path of star forming clumps in the Hi-GAL survey through a multi-step approach, with the final aims of (a) obtaining a robust and accurate set of features able to well classify the star forming clumps in Hi-GAL based on their evolutionary properties, (b) establishing whether a connection exists between the cold material reservoir in clumps, traced by FIR/sub-mm emission, and the already formed YSOs, precursors of stars. For these purposes, our designed experiments aim at testing whether the FIR/sub-mm properties related to clumps are sufficient to predict the clump evolutionary stage, without considering the direct information about the embedded YSOs at NIR/MIR. Our machine learning-based method involves a four-step approach, based on feature engineering, data handling, feature selection and classification. This workflow ensures the identification of the most relevant features driving the SF process, and rigorously evaluates the results through a classification analysis. Our findings suggest that FIR/sub-mm and NIR/MIR emissions trace different evolutionary phases of star forming clumps, highlighting the complex and asynchronous nature of the SF process.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"53 ","pages":"Article 100985"},"PeriodicalIF":1.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491966","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":"2025-10-01","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":"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":"2025-07-01","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}