Pub Date : 2025-12-02DOI: 10.1016/j.ascom.2025.101039
Vikash Kumar Sinha
This article proposes a numerical algorithm based on the homotopy perturbation technique to find the approximate solution of third-order nonlinear Lane–Emden equations arise in several scientific applications. We include the Adomian polynomials to handle the nonlinear terms. The third-order nonlinear Lane–Emden equations are characterized by two different models: the first model with twice shape factor and the second model with once shape factor. Both models have a multi-singularity at the origin. The proposed method deals with both models and yields highly accurate and reliable results. Three problems of first-kind and three problems of second- kind with different shape factors are included to examine the accuracy and applicability of the proposed algorithm. We compare the outcomes with the exact solution and the existing method. The CPU time for the proposed method across all problems has also been provided, indicating its computational efficiency. This method is capable of solving highly nonlinear problems in few iterations with high accuracy.
{"title":"Modified homotopy perturbation technique for solving third-order nonlinear Lane–Emden equations","authors":"Vikash Kumar Sinha","doi":"10.1016/j.ascom.2025.101039","DOIUrl":"10.1016/j.ascom.2025.101039","url":null,"abstract":"<div><div>This article proposes a numerical algorithm based on the homotopy perturbation technique to find the approximate solution of third-order nonlinear Lane–Emden equations arise in several scientific applications. We include the Adomian polynomials to handle the nonlinear terms. The third-order nonlinear Lane–Emden equations are characterized by two different models: the first model with twice shape factor and the second model with once shape factor. Both models have a multi-singularity at the origin. The proposed method deals with both models and yields highly accurate and reliable results. Three problems of first-kind and three problems of second- kind with different shape factors are included to examine the accuracy and applicability of the proposed algorithm. We compare the outcomes with the exact solution and the existing method. The CPU time for the proposed method across all problems has also been provided, indicating its computational efficiency. This method is capable of solving highly nonlinear problems in few iterations with high accuracy.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101039"},"PeriodicalIF":1.8,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684928","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 : 2025-12-02DOI: 10.1016/j.ascom.2025.101040
M. Maistrello , R. Maccary , C. Guidorzi
We present FAST-MEPSA, an optimised version of the MEPSA algorithm developed to detect peaks in uniformly sampled time series affected by uncorrelated Gaussian noise. Although originally conceived for the analysis of gamma-ray burst (GRB) light curves (LCs), MEPSA can be readily applied to other transient phenomena. The algorithm scans the input data by applying a set of 39 predefined patterns across multiple timescales. While robust and effective, its computational cost becomes significant at large re-binning factors. To address this, FAST-MEPSA introduces a sparser offset-scanning strategy. In parallel, building on MEPSA’s flexibility, we introduce a 40th pattern specifically designed to recover a class of elusive peaks that are typically sub-threshold and lie on the rising edge of broader structures—often missed by the original pattern set. Both versions of FAST-MEPSA — with 39 and 40 patterns — were validated on simulated GRB LCs. Compared to MEPSA, the new implementation achieves a speed-up of nearly a factor 400 at high re-binning factors, with only a minor () reduction in the number of detected peaks. It retains the same detection efficiency while significantly lowering the false positive rate of low significance. The inclusion of the new pattern increases the recovery of previously undetected and sub-threshold peaks. These improvements make FAST-MEPSA an effective tool for large-scale analyses where a robust trade-off between speed, efficiency, and reliability is essential. The adoption of 40 patterns instead of the classical 39 is advisable when an enhanced efficiency in detecting faint events is desired. The code is made publicly available.
{"title":"FAST-MEPSA: An optimised and faster version of peak detection algorithm MEPSA","authors":"M. Maistrello , R. Maccary , C. Guidorzi","doi":"10.1016/j.ascom.2025.101040","DOIUrl":"10.1016/j.ascom.2025.101040","url":null,"abstract":"<div><div>We present <span>FAST-MEPSA</span>, an optimised version of the <span>MEPSA</span> algorithm developed to detect peaks in uniformly sampled time series affected by uncorrelated Gaussian noise. Although originally conceived for the analysis of gamma-ray burst (GRB) light curves (LCs), <span>MEPSA</span> can be readily applied to other transient phenomena. The algorithm scans the input data by applying a set of 39 predefined patterns across multiple timescales. While robust and effective, its computational cost becomes significant at large re-binning factors. To address this, <span>FAST-MEPSA</span> introduces a sparser offset-scanning strategy. In parallel, building on <span>MEPSA</span>’s flexibility, we introduce a 40th pattern specifically designed to recover a class of elusive peaks that are typically sub-threshold and lie on the rising edge of broader structures—often missed by the original pattern set. Both versions of <span>FAST-MEPSA</span> — with 39 and 40 patterns — were validated on simulated GRB LCs. Compared to <span>MEPSA</span>, the new implementation achieves a speed-up of nearly a factor 400 at high re-binning factors, with only a minor (<span><math><mrow><mo>∼</mo><mn>4</mn><mtext>%</mtext></mrow></math></span>) reduction in the number of detected peaks. It retains the same detection efficiency while significantly lowering the false positive rate of low significance. The inclusion of the new pattern increases the recovery of previously undetected and sub-threshold peaks. These improvements make <span>FAST-MEPSA</span> an effective tool for large-scale analyses where a robust trade-off between speed, efficiency, and reliability is essential. The adoption of 40 patterns instead of the classical 39 is advisable when an enhanced efficiency in detecting faint events is desired. The code is made publicly available.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101040"},"PeriodicalIF":1.8,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684927","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}
Inferring cosmological parameters from Cosmic Microwave Background (CMB) data requires repeated and computationally expensive calculations of theoretical angular power spectra using Boltzmann solvers like CAMB. This creates a significant bottleneck, particularly for non-standard cosmological models and the high-accuracy demands of future surveys. While emulators based on deep neural networks can accelerate this process by several orders of magnitude, they first require large, pre-computed training datasets, which are costly to generate and model-specific. To address this challenge, we introduce gCAMB, a version of the CAMB code ported to GPUs, which preserves all the features of the original CPU-only code. By offloading the most computationally intensive modules to the GPU, gCAMB significantly accelerates the generation of power spectra, saving massive computational time, halving the power consumption in high-accuracy settings and, among other purposes, facilitating the creation of extensive training sets needed for robust cosmological analyses. We make the gCAMB software available to the community.
{"title":"gCAMB: A GPU-accelerated Boltzmann solver for next-generation cosmological surveys","authors":"Loriano Storchi , Paolo Campeti , Massimiliano Lattanzi , Nicoló Antonini , Enrico Calore , Pasquale Lubrano","doi":"10.1016/j.ascom.2025.101038","DOIUrl":"10.1016/j.ascom.2025.101038","url":null,"abstract":"<div><div>Inferring cosmological parameters from Cosmic Microwave Background (CMB) data requires repeated and computationally expensive calculations of theoretical angular power spectra using Boltzmann solvers like <span>CAMB</span>. This creates a significant bottleneck, particularly for non-standard cosmological models and the high-accuracy demands of future surveys. While emulators based on deep neural networks can accelerate this process by several orders of magnitude, they first require large, pre-computed training datasets, which are costly to generate and model-specific. To address this challenge, we introduce <span>gCAMB</span>, a version of the <span>CAMB</span> code ported to GPUs, which preserves all the features of the original CPU-only code. By offloading the most computationally intensive modules to the GPU, <span>gCAMB</span> significantly accelerates the generation of power spectra, saving massive computational time, halving the power consumption in high-accuracy settings and, among other purposes, facilitating the creation of extensive training sets needed for robust cosmological analyses. We make the <span><span>gCAMB <figure><img></figure></span><svg><path></path></svg></span> software available to the community.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101038"},"PeriodicalIF":1.8,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684925","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 : 2025-11-28DOI: 10.1016/j.ascom.2025.101037
Federica Cuna , Maria Bossa , Fabio Gargano , Mario Nicola Mazziotta
The application of advanced Artificial Intelligence (AI) techniques in astroparticle experiments represents a major advancement in both data analysis and experimental design. As space missions become increasingly complex, integrating AI tools is essential for optimizing system performance and maximizing scientific return. This study explores the use of Graph Neural Networks (GNNs) within the tracking systems of space-based experiments. A key challenge in track reconstruction is the high level of noise, primarily due to backscattering tracks, which can obscure the identification of primary particle trajectories. We propose a novel GNN-based approach for node-level classification tasks, specifically designed to distinguish primary tracks from backscattered ones within the tracker. In this framework, AI is employed as a powerful tool for pattern recognition, enabling the system to identify meaningful structures within complex tracking data and to discriminate signal from backscattering with higher precision. By addressing these challenges, our work aims to enhance the accuracy and reliability of data interpretation in astroparticle physics through the deployment of state-of-the-art AI methodologies.
{"title":"Application of Artificial Intelligence techniques to tracking systems in space experiments","authors":"Federica Cuna , Maria Bossa , Fabio Gargano , Mario Nicola Mazziotta","doi":"10.1016/j.ascom.2025.101037","DOIUrl":"10.1016/j.ascom.2025.101037","url":null,"abstract":"<div><div>The application of advanced Artificial Intelligence (AI) techniques in astroparticle experiments represents a major advancement in both data analysis and experimental design. As space missions become increasingly complex, integrating AI tools is essential for optimizing system performance and maximizing scientific return. This study explores the use of Graph Neural Networks (GNNs) within the tracking systems of space-based experiments. A key challenge in track reconstruction is the high level of noise, primarily due to backscattering tracks, which can obscure the identification of primary particle trajectories. We propose a novel GNN-based approach for node-level classification tasks, specifically designed to distinguish primary tracks from backscattered ones within the tracker. In this framework, AI is employed as a powerful tool for pattern recognition, enabling the system to identify meaningful structures within complex tracking data and to discriminate signal from backscattering with higher precision. By addressing these challenges, our work aims to enhance the accuracy and reliability of data interpretation in astroparticle physics through the deployment of state-of-the-art AI methodologies.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101037"},"PeriodicalIF":1.8,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617389","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 : 2025-11-21DOI: 10.1016/j.ascom.2025.101023
Anindita Nandi , Biswajit Pandey , Prakash Sarkar
We explore the impact of cosmic web environments on galaxy properties such as colour, stellar mass, star formation rate, and stellar metallicity, using a stellar mass-matched sample of simulated galaxies from the Illustris TNG simulation. We use Normalized Mutual Information (NMI) to quantify correlations among galaxy properties and apply Student’s t-test to assess the statistical significance of their differences across cosmic web environments. In every case, the null hypothesis is rejected at confidence, providing strong evidence that correlations among galaxy properties are strongly dependent on cosmic web environments.
{"title":"Tracing correlations between galaxy properties across the Cosmic Web: An IllustrisTNG-based study","authors":"Anindita Nandi , Biswajit Pandey , Prakash Sarkar","doi":"10.1016/j.ascom.2025.101023","DOIUrl":"10.1016/j.ascom.2025.101023","url":null,"abstract":"<div><div>We explore the impact of cosmic web environments on 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, star formation rate, and stellar metallicity, using a stellar mass-matched sample of simulated galaxies from the Illustris TNG simulation. We use Normalized Mutual Information (NMI) to quantify correlations among galaxy properties and apply Student’s t-test to assess the statistical significance of their differences across cosmic web environments. In every case, the null hypothesis is rejected at <span><math><mrow><mo>></mo><mn>99</mn><mo>.</mo><mn>99</mn><mtext>%</mtext></mrow></math></span> confidence, providing strong evidence that correlations among galaxy properties are strongly dependent on cosmic web environments.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101023"},"PeriodicalIF":1.8,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617125","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 : 2025-11-19DOI: 10.1016/j.ascom.2025.101021
Muhammad Ahsan , Shadab Rehman , Masood Ahmad , Maher Alwuthaynani , Ayesha Ayub
This research paper aims to present reliable numerical techniques to deal with singular differential equations, specifically the Lane–Emden equation. The challenges that arise from the singularity at point often leads to the difficulties in common schemes like the Euler and Runge–Kutta methods. To handle this, we implement the Haar wavelet collocation method (HWCM) and its advanced version, the higher-order Haar wavelet collocation method (HOHWCM). These methods are capable of effectively managing singularities and producing approximate solutions for Lane–Emden equations under a range of standard initial conditions, two point Robin conditions, two point mixed conditions, and two point integral conditions. The study incorporates both linear and nonlinear Lane–Emden equations. In nonlinear case, first the linearization technique based on Taylor series expansion is applied to the nonlinear Lane–Emden equation and then the linearized Lane–Emden equation has been solved iteratively with the help of Haar functions. The developed methods are simple to use and are computationally efficient. Results of numerical simulations reveal strong consistency between the numerical and exact results. Incorporating HOHWCM further increases solution accuracy without significantly escalating computational effort, making the method a valuable choice for tackling nonlinear boundary problems. An analysis covering convergence is conducted to support the fast approaching the numerical results towards the exact solutions (here HWCM convergence is of second order while HOHWCM is of order). These results are verified by implementing on different benchmark cases of Lane–Emden equations.
{"title":"High-order wavelet-based numerical algorithms for nonlinear singular Lane–Emden–Fowler equations: Applications to physical models in astrophysics","authors":"Muhammad Ahsan , Shadab Rehman , Masood Ahmad , Maher Alwuthaynani , Ayesha Ayub","doi":"10.1016/j.ascom.2025.101021","DOIUrl":"10.1016/j.ascom.2025.101021","url":null,"abstract":"<div><div>This research paper aims to present reliable numerical techniques to deal with singular differential equations, specifically the Lane–Emden equation. The challenges that arise from the singularity at point <span><math><mrow><mi>t</mi><mo>=</mo><mn>0</mn></mrow></math></span> often leads to the difficulties in common schemes like the Euler and Runge–Kutta methods. To handle this, we implement the Haar wavelet collocation method (HWCM) and its advanced version, the higher-order Haar wavelet collocation method (HOHWCM). These methods are capable of effectively managing singularities and producing approximate solutions for Lane–Emden equations under a range of standard initial conditions, two point Robin conditions, two point mixed conditions, and two point integral conditions. The study incorporates both linear and nonlinear Lane–Emden equations. In nonlinear case, first the linearization technique based on Taylor series expansion is applied to the nonlinear Lane–Emden equation and then the linearized Lane–Emden equation has been solved iteratively with the help of Haar functions. The developed methods are simple to use and are computationally efficient. Results of numerical simulations reveal strong consistency between the numerical and exact results. Incorporating HOHWCM further increases solution accuracy without significantly escalating computational effort, making the method a valuable choice for tackling nonlinear boundary problems. An analysis covering convergence is conducted to support the fast approaching the numerical results towards the exact solutions (here HWCM convergence is of second order while HOHWCM is of <span><math><msup><mrow><mrow><mo>(</mo><mn>2</mn><mo>+</mo><mn>2</mn><mi>μ</mi><mo>)</mo></mrow></mrow><mrow><mi>t</mi><mi>h</mi></mrow></msup></math></span> order). These results are verified by implementing on different benchmark cases of Lane–Emden equations.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101021"},"PeriodicalIF":1.8,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568633","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 : 2025-11-19DOI: 10.1016/j.ascom.2025.101027
Panagiotis N. Sakellariou , Spiros V. Georgakopoulos , Sotiris Tasoulis , Vassilis P. Plagianakos
The detection of gravitational waves has revolutionized our ability to explore fundamental aspects of the Universe. Traditionally, modeled gravitational-wave signals have been identified using template-based matched filtering, followed by coincidence analysis across multiple detectors in the signal-to-noise ratio time series. Recent advances in Machine Learning and Deep Learning have sparked growing interest in their application to both signal detection and parameter estimation. In this study, a hybrid Deep Learning strategy is proposed that leverages the effectiveness of Transformer encoders alongside well-established Convolutional Neural Network architectures in an attempt to estimate the intrinsic and extrinsic parameters of non-precessing binary black hole systems. The primary focus of this work is point estimation, producing single best-fit values for each parameter rather than full posterior distributions. This method is evaluated on both simulated signals embedded in Gaussian noise and real gravitational-wave events, and it demonstrates strong predictive performance and robustness across key astrophysical parameters.
{"title":"Binary black hole parameter estimation with hybrid CNN-Transformer Neural Networks","authors":"Panagiotis N. Sakellariou , Spiros V. Georgakopoulos , Sotiris Tasoulis , Vassilis P. Plagianakos","doi":"10.1016/j.ascom.2025.101027","DOIUrl":"10.1016/j.ascom.2025.101027","url":null,"abstract":"<div><div>The detection of gravitational waves has revolutionized our ability to explore fundamental aspects of the Universe. Traditionally, modeled gravitational-wave signals have been identified using template-based matched filtering, followed by coincidence analysis across multiple detectors in the signal-to-noise ratio time series. Recent advances in Machine Learning and Deep Learning have sparked growing interest in their application to both signal detection and parameter estimation. In this study, a hybrid Deep Learning strategy is proposed that leverages the effectiveness of Transformer encoders alongside well-established Convolutional Neural Network architectures in an attempt to estimate the intrinsic and extrinsic parameters of non-precessing binary black hole systems. The primary focus of this work is point estimation, producing single best-fit values for each parameter rather than full posterior distributions. This method is evaluated on both simulated signals embedded in Gaussian noise and real gravitational-wave events, and it demonstrates strong predictive performance and robustness across key astrophysical parameters.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101027"},"PeriodicalIF":1.8,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614861","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 : 2025-11-18DOI: 10.1016/j.ascom.2025.101024
Seungwan Han , Wonseok Kang , Jae-Hun Jung
Stellar classification based on the Morgan–Keenan (MK) system has long been a fundamental task in astronomy. Numerous studies have attempted to automate this process using machine learning (ML) applied to spectra from digital archives. However, these archives require wavelength calibration — a complex and time-consuming procedure — and spectral type determination relies on expert knowledge. As a result, the available dataset remains limited, containing no more than 1,500 reliably classified spectra for use in independent classification studies. To address this limitation, we constructed a large-scale dataset using stars previously classified in Nancy Houk’s catalog, which provides the coordinates and spectral types of stars observed on objective prism plates. Based on this information, we developed an algorithm to extract stellar spectra from the plates and associate them with the corresponding spectral types listed in the catalog. From a total of 1,064 plates, we obtained 91,050 stellar images and successfully extracted 70,360 usable spectra. For classification, we employed a convolutional neural network (CNN) and introduced a Gaussian encoding method, which better captures the continuous nature of spectral subclasses than conventional one-hot encoding. Our CNN model achieved an accuracy of 41.5% in classifying 49 spectral subclasses, slightly outperforming previous state-of-the-art models that reported 41.2%.
{"title":"Stellar spectral classification using convolutional neural networks on objective prism plates","authors":"Seungwan Han , Wonseok Kang , Jae-Hun Jung","doi":"10.1016/j.ascom.2025.101024","DOIUrl":"10.1016/j.ascom.2025.101024","url":null,"abstract":"<div><div>Stellar classification based on the Morgan–Keenan (MK) system has long been a fundamental task in astronomy. Numerous studies have attempted to automate this process using machine learning (ML) applied to spectra from digital archives. However, these archives require wavelength calibration — a complex and time-consuming procedure — and spectral type determination relies on expert knowledge. As a result, the available dataset remains limited, containing no more than 1,500 reliably classified spectra for use in independent classification studies. To address this limitation, we constructed a large-scale dataset using stars previously classified in Nancy Houk’s catalog, which provides the coordinates and spectral types of stars observed on objective prism plates. Based on this information, we developed an algorithm to extract stellar spectra from the plates and associate them with the corresponding spectral types listed in the catalog. From a total of 1,064 plates, we obtained 91,050 stellar images and successfully extracted 70,360 usable spectra. For classification, we employed a convolutional neural network (CNN) and introduced a Gaussian encoding method, which better captures the continuous nature of spectral subclasses than conventional one-hot encoding. Our CNN model achieved an accuracy of 41.5% in classifying 49 spectral subclasses, slightly outperforming previous state-of-the-art models that reported 41.2%.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101024"},"PeriodicalIF":1.8,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614253","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 : 2025-11-12DOI: 10.1016/j.ascom.2025.101022
Evgeny A. Smirnov
This paper presents a major enhancement to the resonances Python package that now implements full support for identifying and analyzing secular resonances. Building upon the established mean-motion resonance framework, the implementation introduces: (1) a flexible mathematical expression parser supporting arbitrary combinations of fundamental frequencies (, , , ), enabling analysis of both linear resonances (, , ) and more than 70 nonlinear resonances from the literature; (2) specialized libration detection algorithms optimized for secular timescales, with automated parameter adaptation for extended integration times; (3) integration with existing mean-motion resonance workflows through consistent interfaces, allowing unified dynamical studies. The package has been tested through automated unit and integration tests and manual validation against examples from the literature, with all test cases—including , , , , , and resonances passed successfully (with minor exceptions). The new version maintains the simplicity of the original interface, requiring only 3–4 lines of code for standard analyses, while providing researchers with powerful tools for systematic dynamical analysis and asteroid family studies. The package is available on GitHub under the MIT license.
{"title":"Implementation of secular resonance support in the open-source python package “resonances”","authors":"Evgeny A. Smirnov","doi":"10.1016/j.ascom.2025.101022","DOIUrl":"10.1016/j.ascom.2025.101022","url":null,"abstract":"<div><div>This paper presents a major enhancement to the <span>resonances</span> Python package that now implements full support for identifying and analyzing secular resonances. Building upon the established mean-motion resonance framework, the implementation introduces: (1) a flexible mathematical expression parser supporting arbitrary combinations of fundamental frequencies (<span><math><mi>g</mi></math></span>, <span><math><mi>s</mi></math></span>, <span><math><msub><mrow><mi>g</mi></mrow><mrow><mi>i</mi></mrow></msub></math></span>, <span><math><msub><mrow><mi>s</mi></mrow><mrow><mi>i</mi></mrow></msub></math></span>), enabling analysis of both linear resonances (<span><math><msub><mrow><mi>ν</mi></mrow><mrow><mn>5</mn></mrow></msub></math></span>, <span><math><msub><mrow><mi>ν</mi></mrow><mrow><mn>6</mn></mrow></msub></math></span>, <span><math><msub><mrow><mi>ν</mi></mrow><mrow><mn>16</mn></mrow></msub></math></span>) and more than 70 nonlinear resonances from the literature; (2) specialized libration detection algorithms optimized for secular timescales, with automated parameter adaptation for extended integration times; (3) integration with existing mean-motion resonance workflows through consistent interfaces, allowing unified dynamical studies. The package has been tested through automated unit and integration tests and manual validation against examples from the literature, with all test cases—including <span><math><msub><mrow><mi>ν</mi></mrow><mrow><mn>6</mn></mrow></msub></math></span>, <span><math><msub><mrow><mi>ν</mi></mrow><mrow><mn>16</mn></mrow></msub></math></span>, <span><math><msub><mrow><mi>z</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>, <span><math><msub><mrow><mi>z</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>, <span><math><mrow><mn>2</mn><msub><mrow><mi>ν</mi></mrow><mrow><mn>6</mn></mrow></msub><mo>−</mo><msub><mrow><mi>ν</mi></mrow><mrow><mn>5</mn></mrow></msub></mrow></math></span>, and <span><math><mrow><mn>3</mn><msub><mrow><mi>ν</mi></mrow><mrow><mn>6</mn></mrow></msub><mo>−</mo><mn>2</mn><msub><mrow><mi>ν</mi></mrow><mrow><mn>5</mn></mrow></msub></mrow></math></span> resonances passed successfully (with minor exceptions). The new version maintains the simplicity of the original interface, requiring only 3–4 lines of code for standard analyses, while providing researchers with powerful tools for systematic dynamical analysis and asteroid family studies. The package is available on GitHub under the MIT license.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101022"},"PeriodicalIF":1.8,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519794","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}
Precise and accurate estimation of cosmological parameters is crucial for understanding the Universe’s dynamics and addressing cosmological tensions. In this methods paper, we explore bio-inspired metaheuristic algorithms, including the Improved Multi-Operator Differential Evolution scheme and the Philippine Eagle Optimization Algorithm (PEOA), alongside the relatively known genetic algorithm, for cosmological parameter estimation. Using mock data that underlay a true fiducial cosmology, we test the viability of each optimization method to recover the input cosmological parameters with confidence regions generated by bootstrapping on top of optimization. We compare the results with Markov chain Monte Carlo (MCMC) in terms of accuracy and precision, and show that PEOA performs comparably well under the specific circumstances provided. Understandably, Bayesian inference and optimization serve distinct purposes, but comparing them highlights the potential of nature-inspired algorithms in cosmological analysis, offering alternative pathways to explore parameter spaces and validate standard results.
{"title":"Nature-inspired optimization, the Philippine Eagle, and cosmological parameter estimation","authors":"Reginald Christian Bernardo , Erika Antonette Enriquez , Renier Mendoza , Reinabelle Reyes , Arrianne Crystal Velasco","doi":"10.1016/j.ascom.2025.101026","DOIUrl":"10.1016/j.ascom.2025.101026","url":null,"abstract":"<div><div>Precise and accurate estimation of cosmological parameters is crucial for understanding the Universe’s dynamics and addressing cosmological tensions. In this methods paper, we explore bio-inspired metaheuristic algorithms, including the Improved Multi-Operator Differential Evolution scheme and the Philippine Eagle Optimization Algorithm (PEOA), alongside the relatively known genetic algorithm, for cosmological parameter estimation. Using mock data that underlay a true fiducial cosmology, we test the viability of each optimization method to recover the input cosmological parameters with confidence regions generated by bootstrapping on top of optimization. We compare the results with Markov chain Monte Carlo (MCMC) in terms of accuracy and precision, and show that PEOA performs comparably well under the specific circumstances provided. Understandably, Bayesian inference and optimization serve distinct purposes, but comparing them highlights the potential of nature-inspired algorithms in cosmological analysis, offering alternative pathways to explore parameter spaces and validate standard results.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101026"},"PeriodicalIF":1.8,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568634","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}
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