Pub Date : 2025-12-12DOI: 10.1016/j.ascom.2025.101046
Chiara Curletto , Paolo Massa , Valeria Tagliafico , Cristina Campi , Federico Benvenuto , Michele Piana , Andrea Tacchino
Solar flares are the most explosive phenomena in the solar system and the main trigger of the events’ chain that starts from Coronal Mass Ejections and leads to geomagnetic storms with possible impacts on the infrastructures at Earth. Data-driven solar flare forecasting relies on either deep learning approaches, which are operationally promising but with a low explainability degree, or machine learning algorithms, which can provide information on the physical descriptors that mostly impact the prediction. This paper describes a web-based technological platform for the execution of a computational pipeline of feature-based machine learning methods that provide predictions of the flare occurrence, feature ranking information, and assessment of the prediction performances.
{"title":"PRESOL: A web-based computational setting for feature-based flare forecasting","authors":"Chiara Curletto , Paolo Massa , Valeria Tagliafico , Cristina Campi , Federico Benvenuto , Michele Piana , Andrea Tacchino","doi":"10.1016/j.ascom.2025.101046","DOIUrl":"10.1016/j.ascom.2025.101046","url":null,"abstract":"<div><div>Solar flares are the most explosive phenomena in the solar system and the main trigger of the events’ chain that starts from Coronal Mass Ejections and leads to geomagnetic storms with possible impacts on the infrastructures at Earth. Data-driven solar flare forecasting relies on either deep learning approaches, which are operationally promising but with a low explainability degree, or machine learning algorithms, which can provide information on the physical descriptors that mostly impact the prediction. This paper describes a web-based technological platform for the execution of a computational pipeline of feature-based machine learning methods that provide predictions of the flare occurrence, feature ranking information, and assessment of the prediction performances.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101046"},"PeriodicalIF":1.8,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737788","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 : 2025-12-08DOI: 10.1016/j.ascom.2025.101041
R.C. Bernardo , Y. Chen
Genetic algorithm (GA) belong to a class of nature-inspired evolutionary algorithms that leverage concepts from natural selection to perform optimization tasks. In cosmology, the standard method for estimating parameters is the Markov chain Monte Carlo (MCMC) approach, renowned for its reliability in determining cosmological parameters. This paper presents a pedagogical examination of GA as a potential corroborative tool to MCMC for cosmological parameter estimation. Utilizing data sets from cosmic chronometers and supernovae with a curved CDM model, we explore the impact of GA’s key hyperparameters — such as the fitness function, crossover rate, and mutation rate — on the population of cosmological parameters determined by the evolutionary process. We compare the results obtained with GA to those by MCMC, analyzing their effectiveness and viability for cosmological application.
{"title":"Genetic algorithm demystified for cosmological parameter estimation","authors":"R.C. Bernardo , Y. Chen","doi":"10.1016/j.ascom.2025.101041","DOIUrl":"10.1016/j.ascom.2025.101041","url":null,"abstract":"<div><div>Genetic algorithm (GA) belong to a class of nature-inspired evolutionary algorithms that leverage concepts from natural selection to perform optimization tasks. In cosmology, the standard method for estimating parameters is the Markov chain Monte Carlo (MCMC) approach, renowned for its reliability in determining cosmological parameters. This paper presents a pedagogical examination of GA as a potential corroborative tool to MCMC for cosmological parameter estimation. Utilizing data sets from cosmic chronometers and supernovae with a curved <span><math><mi>Λ</mi></math></span>CDM model, we explore the impact of GA’s key hyperparameters — such as the fitness function, crossover rate, and mutation rate — on the population of cosmological parameters determined by the evolutionary process. We compare the results obtained with GA to those by MCMC, analyzing their effectiveness and viability for cosmological application.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101041"},"PeriodicalIF":1.8,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737787","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 : 2025-12-06DOI: 10.1016/j.ascom.2025.101043
Leone Bacciu , Matteo Grazioso , Giovanni Cavallotto , Stefano Della Torre , Massimo Gervasi , Giuseppe La Vacca , Sabina Rossi , Marco S. Nobile
The accurate modeling of galactic cosmic ray (GCR) propagation in the heliosphere requires solving the Parker Transport Equation (PTE), a multidimensional nonlinear equation that cannot be addressed analytically without strong approximations. In recent decades, stochastic differential equation (SDE)–Monte Carlo methods have emerged as a powerful numerical strategy for this problem, thanks to their numerical stability, relatively low memory requirements, and intrinsic parallelism. The increasing availability of general-purpose Graphics Processing Units (GPUs) has further revolutionized this approach by enabling massive parallelization of particle trajectories at relatively low cost. In this work, we introduce COSMICA (COde for a Speedy Montecarlo Involving Cuda Architecture), a new open-source multi-GPU code written in CUDA/C++ for the three-dimensional solution of the PTE. COSMICA has been specifically designed to optimize GPU resource usage and scalability, with strategies including memory hierarchy exploitation, register-conscious kernel design, warp-aware scheduling, and parameter reordering for multi-GPU execution. Benchmark results demonstrate that COSMICA reduces runtimes from weeks to hours for large-scale simulations. These optimizations make COSMICA a versatile tool for systematic studies of cosmic-ray modulation and parameter exploration, thereby expanding the feasibility of investigations that were previously computationally prohibitive. The present article constitutes the first part of a two-paper series, focusing on code design and computational performance; a companion paper will present its validation against benchmark models.
银河系宇宙射线（GCR）在日球层传播的精确建模需要求解帕克输运方程（PTE），这是一个多维非线性方程，没有强近似就无法解析求解。近几十年来，随机微分方程(SDE) -蒙特卡罗方法由于其数值稳定性、相对较低的内存需求和内在的并行性，已成为解决该问题的强大数值策略。通用图形处理单元（gpu）的日益普及进一步革新了这种方法，以相对较低的成本实现了粒子轨迹的大规模并行化。在这项工作中，我们介绍了COSMICA （COde for a Speedy Montecarlo涉及Cuda架构），这是一个用Cuda / c++编写的新的开源多GPU代码，用于PTE的三维解决方案。COSMICA专门用于优化GPU资源使用和可扩展性，其策略包括内存层次利用，寄存器意识内核设计，扭曲感知调度和多GPU执行的参数重新排序。基准测试结果表明，COSMICA将大规模模拟的运行时间从数周缩短到数小时。这些优化使COSMICA成为系统研究宇宙射线调制和参数探索的通用工具，从而扩大了以前在计算上禁止的研究的可行性。本文是两篇系列文章的第一部分，重点是代码设计和计算性能；另一篇论文将介绍其对基准模型的验证。
{"title":"Massive stochastic simulation of cosmic rays propagation in the heliosphere: The COSMICA code","authors":"Leone Bacciu , Matteo Grazioso , Giovanni Cavallotto , Stefano Della Torre , Massimo Gervasi , Giuseppe La Vacca , Sabina Rossi , Marco S. Nobile","doi":"10.1016/j.ascom.2025.101043","DOIUrl":"10.1016/j.ascom.2025.101043","url":null,"abstract":"<div><div>The accurate modeling of galactic cosmic ray (GCR) propagation in the heliosphere requires solving the Parker Transport Equation (PTE), a multidimensional nonlinear equation that cannot be addressed analytically without strong approximations. In recent decades, stochastic differential equation (SDE)–Monte Carlo methods have emerged as a powerful numerical strategy for this problem, thanks to their numerical stability, relatively low memory requirements, and intrinsic parallelism. The increasing availability of general-purpose Graphics Processing Units (GPUs) has further revolutionized this approach by enabling massive parallelization of particle trajectories at relatively low cost. In this work, we introduce COSMICA (COde for a Speedy Montecarlo Involving Cuda Architecture), a new open-source multi-GPU code written in CUDA/C++ for the three-dimensional solution of the PTE. COSMICA has been specifically designed to optimize GPU resource usage and scalability, with strategies including memory hierarchy exploitation, register-conscious kernel design, warp-aware scheduling, and parameter reordering for multi-GPU execution. Benchmark results demonstrate that COSMICA reduces runtimes from weeks to hours for large-scale simulations. These optimizations make COSMICA a versatile tool for systematic studies of cosmic-ray modulation and parameter exploration, thereby expanding the feasibility of investigations that were previously computationally prohibitive. The present article constitutes the first part of a two-paper series, focusing on code design and computational performance; a companion paper will present its validation against benchmark models.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101043"},"PeriodicalIF":1.8,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737786","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 : 2025-12-04DOI: 10.1016/j.ascom.2025.101042
S. Riggi , P. Romano , A. Pilzer , U. Becciani
We present a comparative study of transformer-based architectures for solar flare forecasting using heterogeneous data modalities, including images, video sequences, and time-series observations. Our analysis evaluates three recent foundational models SigLIP2 for image encoding, VideoMAE for spatio-temporal video representation, and Moirai2 for multivariate time-series forecasting applied to publicly available datasets of solar magnetograms from the SDO/HMI mission and soft X-ray fluxes acquired by GOES satellites. All models are trained and validated under consistent data splits and evaluation criteria, with the goal of assessing the strengths and limitations of transformer backbones across spatial and temporal representations of solar activity. We investigate multiple loss formulations (weighted BCE, focal, and score-oriented) and training balance strategies to mitigate class imbalance typical of flare datasets. Results show that while both SigLIP2 and VideoMAE achieve typical performance on image and video data (True Skill Statistic TSS 0.60–0.65), the time-series model Moirai2 reaches superior forecasting skill (TSS 0.74) using irradiance-based temporal evolution alone. These findings highlight the potential of pretrained transformer architectures and cross-modal learning for advancing operational space weather forecasting, paving the way toward unified multimodal models that integrate visual and temporal information.
{"title":"Solar flare forecasting with foundational transformer models across image, video, and time-series modalities","authors":"S. Riggi , P. Romano , A. Pilzer , U. Becciani","doi":"10.1016/j.ascom.2025.101042","DOIUrl":"10.1016/j.ascom.2025.101042","url":null,"abstract":"<div><div>We present a comparative study of transformer-based architectures for solar flare forecasting using heterogeneous data modalities, including images, video sequences, and time-series observations. Our analysis evaluates three recent foundational models <span><math><mo>−</mo></math></span> <em>SigLIP2</em> for image encoding, <em>VideoMAE</em> for spatio-temporal video representation, and <em>Moirai2</em> for multivariate time-series forecasting <span><math><mo>−</mo></math></span> applied to publicly available datasets of solar magnetograms from the SDO/HMI mission and soft X-ray fluxes acquired by GOES satellites. All models are trained and validated under consistent data splits and evaluation criteria, with the goal of assessing the strengths and limitations of transformer backbones across spatial and temporal representations of solar activity. We investigate multiple loss formulations (weighted BCE, focal, and score-oriented) and training balance strategies to mitigate class imbalance typical of flare datasets. Results show that while both SigLIP2 and VideoMAE achieve typical performance on image and video data (True Skill Statistic TSS <span><math><mo>∼</mo></math></span> 0.60–0.65), the time-series model Moirai2 reaches superior forecasting skill (TSS <span><math><mo>∼</mo></math></span> 0.74) using irradiance-based temporal evolution alone. These findings highlight the potential of pretrained transformer architectures and cross-modal learning for advancing operational space weather forecasting, paving the way toward unified multimodal models that integrate visual and temporal information.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101042"},"PeriodicalIF":1.8,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684926","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.2025.101039
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}
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