Pub Date : 2025-07-11DOI: 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- data, we also introduced transfer learning to improve charge reconstruction for high- 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 = 10 to = 28.
{"title":"Charge reconstruction of HERD silicon charge detectors based on MLP","authors":"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","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}
Pub Date : 2025-07-10DOI: 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.
{"title":"From global climate models (GCMs) to exoplanet spectra with the Global Emission Spectra (GlobES)","authors":"Thomas J. Fauchez , Geronimo L. Villanueva , Vincent Kofman , Gabriella Suissa , Ravi K. Kopparapu","doi":"10.1016/j.ascom.2025.100982","DOIUrl":"10.1016/j.ascom.2025.100982","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"53 ","pages":"Article 100982"},"PeriodicalIF":1.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595809","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}
Pub Date : 2025-07-09DOI: 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).
{"title":"Survey-wide asteroid discovery with a high-performance computing enabled non-linear digital tracking framework","authors":"N. Golovich , T. Steil , A. Geringer-Sameth , K. Iwabuchi , R. Dozier , 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":"2025-07-09","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}
Pub Date : 2025-07-05DOI: 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 and the Gauss–Bonnet invariant 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 , , and , 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.
{"title":"A cosmological probe in a theory of higher-order gravity","authors":"Shaily , A. Srivastava , H.V. Chauhan , A. Pratap , J.K. Singh","doi":"10.1016/j.ascom.2025.100988","DOIUrl":"10.1016/j.ascom.2025.100988","url":null,"abstract":"<div><div>In this work, we study a cosmological model in a modified gravity containing the Ricci scalar <span><math><mi>R</mi></math></span> and the Gauss–Bonnet invariant <span><math><mi>G</mi></math></span> 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 <span><math><msub><mrow><mi>H</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>, <span><math><mi>α</mi></math></span>, and <span><math><msub><mrow><mi>t</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>, 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.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"53 ","pages":"Article 100988"},"PeriodicalIF":1.9,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606100","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}
Pub Date : 2025-07-04DOI: 10.1016/j.ascom.2025.100989
A. Chilingarian
The proposed local intrinsic dimensionality method (TIDIM algorithm) demonstrates significant potential in detecting specific filament-like and globular cluster-like structures. It provides a non-parametric, reproducible, and resolution-flexible framework for identifying complex structures within three-dimensional distributions. This approach offers a complementary perspective to traditional statistical tools by focusing on local features such as voids, filaments, and globular clusters. Additionally, it allows for the reconstruction of discrete tracers using the same flexible framework. Its capacity to localize anisotropies and apply TIDIM on a spatial grid makes it particularly useful for comparisons with simulations. The Sobol grid-based TIDIM analysis complements galaxy-based assessments by enabling the detection of structures in sparsely populated regions.
{"title":"Intrinsic dimensionality estimation for the galaxy’s distribution structure analysis","authors":"A. Chilingarian","doi":"10.1016/j.ascom.2025.100989","DOIUrl":"10.1016/j.ascom.2025.100989","url":null,"abstract":"<div><div>The proposed local intrinsic dimensionality method (TIDIM algorithm) demonstrates significant potential in detecting specific filament-like and globular cluster-like structures. It provides a non-parametric, reproducible, and resolution-flexible framework for identifying complex structures within three-dimensional distributions. This approach offers a complementary perspective to traditional statistical tools by focusing on local features such as voids, filaments, and globular clusters. Additionally, it allows for the reconstruction of discrete tracers using the same flexible framework. Its capacity to localize anisotropies and apply TIDIM on a spatial grid makes it particularly useful for comparisons with simulations. The Sobol grid-based TIDIM analysis complements galaxy-based assessments by enabling the detection of structures in sparsely populated regions.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"53 ","pages":"Article 100989"},"PeriodicalIF":1.9,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588747","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}
Pub Date : 2025-06-25DOI: 10.1016/j.ascom.2025.100985
Y. Maruccia , S. Cavuoti , M. Brescia , G. Riccio , S. Molinari , D. Elia , E. Schisano
Star formation (SF) studies are benefiting from the huge amount of data made available by recent large-area Galactic plane surveys conducted between 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.
{"title":"The evolutionary path of star-forming clumps in Hi-GAL","authors":"Y. Maruccia , S. Cavuoti , M. Brescia , G. Riccio , S. Molinari , D. Elia , 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":"2025-06-25","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}
Pub Date : 2025-06-16DOI: 10.1016/j.ascom.2025.100974
S.K. Garain
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.
{"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":"2025-06-16","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}
Pub Date : 2025-06-16DOI: 10.1016/j.ascom.2025.100972
Anindita Nandi, Biswajit Pandey
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 colour, stellar mass, specific star formation rate, metallicity, morphology, and . Our analysis reveals that the first three principal components (PC1, PC2 and PC3) account for approximately 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 . Our analysis confirms that cosmic web environments play a significant role in shaping the correlations between galaxy properties.
{"title":"Impact of cosmic web on galaxy properties and their correlations: Insights from Principal Component Analysis","authors":"Anindita Nandi, 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>></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":"2025-06-16","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}
Pub Date : 2025-06-14DOI: 10.1016/j.ascom.2025.100971
Isao Shoji , Tadafumi Takata , Yoshihiko Mizumoto
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.
{"title":"Geometric analysis of variability of radiocarbon abundances and solar activity","authors":"Isao Shoji , Tadafumi Takata , 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":"2025-06-14","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}
Pub Date : 2025-06-13DOI: 10.1016/j.ascom.2025.100973
S. Yatawatta, A.-J. Boonstra, C.P. Broekema
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.
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