Astronomy and Computing最新文献

{"title":"FITrig: A high-performance detection technique for efficient Ultra-Long-Period Pulsars","authors":"Xiaotong Li,&nbsp;Karel Adámek,&nbsp;Wesley Armour","doi":"10.1016/j.ascom.2025.101058","DOIUrl":"10.1016/j.ascom.2025.101058","url":null,"abstract":"<div><div>Ultra-long-period (ULP) pulsars, a newly identified class of celestial transients, offer unique insights into astrophysics, though very few have been detected to date. In radio astronomy, most time-domain detection methods cannot find these pulsars, and current image-based detection approaches still face challenges, including low sensitivity, high false positive rate, and low computational efficiency. In this article, we develop Fast Imaging Trigger (FITrig), a GPU-accelerated, statistics-based method for ULP pulsar detection and localisation. FITrig includes two complementary approaches — an image domain and an image-frequency domain strategy. FITrig offers advantages by increasing sensitivity to faint pulsars, suppressing false positives (from noise, processing artefacts, or steady sources), and improving search efficiency in large-scale wide-field images. Compared to the state-of-the-art source finder SOFIA 2, FITrig increases the detection speed by 4.3 times for large images (<span><math><mrow><mn>50</mn><mi>K</mi><mo>×</mo><mn>50</mn><mi>K</mi></mrow></math></span> pixels) and reduces false positives by up to 858.8 times (at 6<span><math><mi>σ</mi></math></span> significance) for the image domain branch, while the image-frequency domain branch suppresses false positives even further. FITrig maintains the capability to detect pulsars that are 20 times fainter than surrounding steady features, even under critical Nyquist sampling conditions. In this article, the performance of FITrig is demonstrated using both real-world data (MeerKAT observations of PSR J0901-4046) and simulated datasets based on MeerKAT and SKA Array Assembly (AA) 2 telescope configurations. With its real-time processing capabilities and scalability, FITrig is a promising tool for next-generation telescopes, such as the SKA, with the potential to uncover hidden ULP pulsars.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101058"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"gCAMB: A GPU-accelerated Boltzmann solver for next-generation cosmological surveys","authors":"Loriano Storchi ,&nbsp;Paolo Campeti ,&nbsp;Massimiliano Lattanzi ,&nbsp;Nicoló Antonini ,&nbsp;Enrico Calore ,&nbsp;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":"2026-04-01","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}

{"title":"GalaPy—Implementation strategies of the spectral modelling tool for galaxies in Python","authors":"Tommaso Ronconi,&nbsp;Andrea Lapi","doi":"10.1016/j.ascom.2026.101079","DOIUrl":"10.1016/j.ascom.2026.101079","url":null,"abstract":"<div><div>We present the computational design and implementation of GalaPy, a hybrid C++/Python library for the spectral energy distribution (SED) modelling of galaxies. Originally introduced in Ronconi et al. (2024), GalaPy has been developed within the Italian galaxy formation and cosmology community as part of the <em>ICSC – Centro Nazionale di Ricerca in High Performance Computing, Big Data e Quantum Computing</em>. The library combines the performance of compiled C++ routines with the flexibility of Python, enabling efficient generation and fitting of physically motivated SED models.</div><div>We describe the object-oriented architecture of the code, its hybrid parallelisation strategy, and the optimisations that ensure portability and minimal memory overhead. Parallel execution relies on a combination of vectorised array programming, shared-memory concurrency, and distributed-memory message passing.</div><div>Recent updates include Bayesian evidence-based model selection and a fully analytical, panchromatic active galactic nucleus component. These additions further improve the physical realism and the statistical power of the framework. GalaPy thus provides a modular and extensible platform for galaxy modelling, designed to interface and adapt seamlessly to the next generation of large-scale astrophysical analyses.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101079"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extending Galactic foreground emission with neural networks","authors":"Giuseppe Puglisi ,&nbsp;Avinash Anand ,&nbsp;Marina Migliaccio","doi":"10.1016/j.ascom.2026.101070","DOIUrl":"10.1016/j.ascom.2026.101070","url":null,"abstract":"<div><div>We introduce an innovative approach employing Cycle Generative Adversarial Networks (Cycle-GANs) to accurately simulate Carbon Monoxide (CO) emissions by learning features identified in thermal dust emission maps from the <em>Planck</em> satellite alongside HI data from HI4PI survey. Our training dataset is complemented by the targets represented by the two rotational transition lines of CO (<span><math><mrow><mi>J</mi><mo>:</mo><mn>1</mn><mo>−</mo><mn>0</mn><mo>,</mo><mspace></mspace><mn>2</mn><mo>−</mo><mn>1</mn></mrow></math></span>) provided by the <em>Planck</em> satellite. We ensure the robustness of our dataset by focusing on regions with a signal-to-noise ratio (SNR) exceeding 8. The outcomes, assessed utilizing angular power spectra and Minkowski functionals, confirm that our algorithm proficiently achieves the set goals, indicating that the amplitudes of the generated emission accurately reproduce the angular correlations and share the statistical properties of the employed CO targets. We thus aim at improving the current models of CO emission specifically in the high-Galactic latitude areas that have been hardly observed by the most recent surveys, and, in doing so, to address and overcome the limitations affecting current models regions. This research lays the groundwork for creating transformative synthetic simulations, leveraging convolutional neural networks tied to data procured from latest observations.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101070"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of a choice of cross-match radius on Gaia sky","authors":"Dana Kovaleva ,&nbsp;Pavel Kaygorodov ,&nbsp;Ekaterina Malik ,&nbsp;Oleg Malkov","doi":"10.1016/j.ascom.2025.101028","DOIUrl":"10.1016/j.ascom.2025.101028","url":null,"abstract":"<div><div>The data obtained by the Gaia space mission (Gaia Collaboration et al., 2016) provide a recent and notable example of a dataset that needs to be cross-matched with other datasets for various astronomical applications. We investigate how the properties of cross-matched datasets affect the results, in order to determine optimal matching parameters for each scientific task. We employ Gaia DR3 main catalogue and synthetically generated datasets to perform cross-match and obtain numerical metrics to predict probability of mismatch. This probability depends on the matching radius, the positional accuracy of the matched dataset, the surface density in the vicinity, and the stellar magnitude of the source. Gaia DR3 main catalogue was probed for the metrics of distribution to the nearest neighbour as a function of sky position. We employed 768 test areas of 1 degree radius distributed uniformly over the sky and obtained for each area mean, median and Q10 angular distance to the nearest neighbour. We found that the fraction of true positives decreases sharply when the ratio of positional accuracy to the characteristic nearest-neighbour distance exceeds 0.2. Simultaneously, while positional accuracy of the matched datasets approaches their characteristic distance, the probability of true positive and false positive outcomes are similar. It was demonstrated that employing “best neighbour” condition one may decrease the fraction of mismatches up to an order of magnitude, especially in the populated regions of sky and at larger positional errors of matched datasets. Using stellar magnitudes to constrain positional cross-matches is ineffective for faint sources. We calculated the expected fraction of mismatches as a function of sky position for cross-matches between Gaia and synthetic catalogues with positional uncertainties comparable to those of 2MASS and eRASS. In the most crowded sky regions (the Galactic Centre and disk), mismatches reach 15% for the nearest-neighbour and 4% for the best-neighbour criterion under 2MASS-level accuracy. For eRASS-level accuracy, they rise to 91% and 12%, respectively.</div><div>We provide metrics of distribution of distance to the nearest neighbour in Gaia DR3 main catalogue depending on sky coordinates in analytical and numerical form, as well as in the form of Python module gaia_density (<span><span>https://github.com/noncath/gaia_density</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101028"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MongoDB scalability for astronomical time series: The POEMAS solar radio telescope evaluation without HPC","authors":"W. Conde ,&nbsp;A. Valio ,&nbsp;C.G.G. de Castro","doi":"10.1016/j.ascom.2025.101053","DOIUrl":"10.1016/j.ascom.2025.101053","url":null,"abstract":"<div><div>The increasing temporal resolution and structural diversity of modern solar instruments place growing demands on database systems used in observational astronomy. At the Center for Radio Astronomy and Astrophysics Mackenzie (CRAAM), this challenge is amplified by the need to consolidate heterogeneous data streams from multiple telescopes within a single virtual machine. With only 32<!--> <!-->GB of RAM available (16<!--> <!-->GB allocated to the database), a central design question emerged: when restricted to a single physical host, can a virtualized sharded cluster offer practical scalability advantages over a standalone deployment? To investigate this, we conducted an empirical evaluation of MongoDB using 10<!--> <!-->ms observations from the POEMAS radiotelescope, tested at volumes of 15M, 150M, and 500M documents. Results show that, although sharding introduces coordination overhead for selective queries, it provides substantial gains for global aggregations, achieving speedups above 600<span><math><mo>×</mo></math></span> while maintaining compression ratios near 85%. The analysis identifies an operational threshold of roughly 150 million documents per collection to sustain stable performance under the available resources. Based on these findings, the same single-node configuration used in the benchmarks was employed to process the full historical POEMAS dataset, totaling 3.3 billion records and producing approximately 50<!--> <!-->GB of consolidated FITS products. These products and their associated metadata are made available to the community through a cloud-hosted portal with reduced operational cost. This work documents practical scalability boundaries for astronomical time-series in resource-constrained environments and supports the deployment currently operating at CRAAM.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101053"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards an optimal extraction of cosmological parameters from galaxy cluster surveys using convolutional neural networks","authors":"I. Sáez-Casares ,&nbsp;M. Calabrese ,&nbsp;D. Bianchi ,&nbsp;M.S. Cagliari ,&nbsp;M. Chiarenza ,&nbsp;J.-M. Christille ,&nbsp;L. Guzzo","doi":"10.1016/j.ascom.2026.101067","DOIUrl":"10.1016/j.ascom.2026.101067","url":null,"abstract":"<div><div>The possibility to constrain cosmological parameters from galaxy surveys using field-level machine learning methods that bypass traditional summary statistics analyses, depends crucially on our ability to generate simulated training sets. The latter need to be both realistic, as to reproduce the key features of the real data, and produced in large numbers, as to allow us to refine the precision of the training process. The analysis presented in this paper is an attempt to respond to these needs by (a) using clusters of galaxies as tracers of large-scale structure, together with (b) adopting a 3LPT code (<span>Pinocchio</span>) to generate a large training set of <span><math><mrow><mn>32</mn><mo>,</mo><mn>768</mn></mrow></math></span> mock X-ray cluster catalogues. X-ray luminosities are stochastically assigned to dark matter haloes using an empirical <span><math><mrow><mi>M</mi><mo>−</mo><msub><mrow><mi>L</mi></mrow><mrow><mi>X</mi></mrow></msub></mrow></math></span> scaling relation. Using this training set, we test the ability and performances of a 3D convolutional neural network (CNN) to predict the cosmological parameters, based on an input overdensity field derived from the cluster distribution. We perform a comparison with a neural network trained on traditional summary statistics, that is, the abundance of clusters and their power spectrum. Our results show that the field-level analysis combined with the cluster abundance yields a mean absolute relative error on the predicted values of <span><math><msub><mrow><mi>Ω</mi></mrow><mrow><mi>m</mi></mrow></msub></math></span> and <span><math><msub><mrow><mi>σ</mi></mrow><mrow><mn>8</mn></mrow></msub></math></span> that is a factor of <span><math><mrow><mo>∼</mo><mn>10</mn><mtext>%</mtext></mrow></math></span> and <span><math><mrow><mo>∼</mo><mn>20</mn><mtext>%</mtext></mrow></math></span> better than that obtained from the summary statistics. Furthermore, when information about the individual luminosity of each cluster is passed to the CNN, the gain in precision exceeds 50%.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101067"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Massive stochastic simulation of cosmic rays propagation in the heliosphere: The COSMICA code","authors":"Leone Bacciu ,&nbsp;Matteo Grazioso ,&nbsp;Giovanni Cavallotto ,&nbsp;Stefano Della Torre ,&nbsp;Massimo Gervasi ,&nbsp;Giuseppe La Vacca ,&nbsp;Sabina Rossi ,&nbsp;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":"2026-04-01","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}

{"title":"BOPAS: The Bologna Observatory Pipeline for Astrometry of Satellites","authors":"S. Palmiotto ,&nbsp;A. Carbognani ,&nbsp;A. Buzzoni ,&nbsp;D. Modenini ,&nbsp;P. Tortora","doi":"10.1016/j.ascom.2025.101045","DOIUrl":"10.1016/j.ascom.2025.101045","url":null,"abstract":"<div><div>Facing the increasing population of active and inactive objects along the different geocentric orbital regimes, the activities of Space Surveillance and Tracking are becoming more and more important for any efficient Space Traffic Management and Policy effort in order to quantify and (when possible) mitigate the collision risk with/among any space debris. However, the astrometric observations of resident space objects, including space debris, are not easy: due to their high angular speed, to get a good orbit solution, a temporal precision of the order of a few milliseconds and the ability to measure the position of streaks are required. These observational difficulties are similar to those encountered in the astrometry of very fast near-Earth asteroids as they pass closer to Earth. We developed our own image processing pipeline for astrometry of satellite streaks, and tested it with observations of resident space objects and near-Earth asteroids from our telescope asset for Space Surveillance and Tracking, obtaining astrometric errors in the order of 1 arcsec. We propose our pipeline as a valid tool among the other ones in the literature. The software presented in this paper could also be useful to process observations of fast NEAs, and is freely available on GitHub, where anyone can download and adapt it as needed.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101045"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Artificial Neural Network based scheme for I/O FLASH AMR code optimization","authors":"M. Sortino,&nbsp;V. Antonuccio-Delogu","doi":"10.1016/j.ascom.2026.101066","DOIUrl":"10.1016/j.ascom.2026.101066","url":null,"abstract":"<div><div>Current modular Computational Fluid Dynamics (hereafter <em>CFD</em>) codes often implement efficient parallel numerical schemes, resulting in efficient scalability scalable. The efficiency of the I/O phases however has received comparatively less attention. In this work we study the I/O performance of a highly structured community CFD-based multipurpose code FLASH (v.4.3). We have trained an Artificial Neural Network (hereafter <em>ANN</em>) on a set of about 10000 I/O realistic runs, where the output files are sufficiently large and complex. We demonstrate that the <em>ANN</em> <!-->is quite successful in predicting the I/O efficiency in real time, given some input system status parameters.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"55 ","pages":"Article 101066"},"PeriodicalIF":1.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173399","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}