Astronomy and Computing最新文献

{"title":"That pesky A-term: Efficiently correcting for direction-, time-, and baseline-dependent effects in radio interferometric imaging","authors":"Torrance Hodgson,&nbsp;Melanie Johnston-Hollitt","doi":"10.1016/j.ascom.2025.101012","DOIUrl":"10.1016/j.ascom.2025.101012","url":null,"abstract":"<div><div>Radio interferometers must grapple with apparent fields of view that distort the true radio sky. These so-called ‘<span><math><mi>A</mi></math></span>-term’ distortions may be direction-, time- and baseline-dependent, and include effects like the primary beam and the ionosphere. Traditionally, properly handling these effects has been computationally expensive and, instead, less accurate, ad-hoc methods have been employed. Image domain gridding (<span>idg</span>; van der Tol et al., 2018) is a recently developed algorithm that promises to account for these <span><math><mi>A</mi></math></span>-terms both accurately and efficiently. Here we describe a new implementation of <span>idg</span> known as the Parallel Interferometric <span>gpu</span> Imager (Pigi). Pigi is capable of imaging at rates of almost half a billion visibilities per second on modest hardware, making it well suited for the projected data rates of the Square Kilometre Array, and is compatible with both <span>nvidia</span> and <span>amd</span> <span>gpu</span> hardware. Its accuracy is principally limited only by the degree to which <span><math><mi>A</mi></math></span>-terms are spatially sampled. Using data from the Murchison Widefield Array, we demonstrate the effectiveness of Pigi in correcting for simulated ionospheric effects and point to future work that would enable these results on real-world data.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101012"},"PeriodicalIF":1.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222038","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":"Karabo: A versatile SKA observation simulation framework","authors":"Rohit Sharma ,&nbsp;Simon Felix ,&nbsp;Luis Fernando Machado Poletti Valle ,&nbsp;Vincenzo Timmel ,&nbsp;Lukas Gehrig ,&nbsp;Andreas Wassmer ,&nbsp;Jennifer Studer ,&nbsp;Pascal Hitz ,&nbsp;Filip Schramka ,&nbsp;Michele Bianco ,&nbsp;Devin Crichton ,&nbsp;Marta Spinelli ,&nbsp;André Csillaghy ,&nbsp;Stefan Kögel ,&nbsp;Alexandre Réfrégier","doi":"10.1016/j.ascom.2025.101004","DOIUrl":"10.1016/j.ascom.2025.101004","url":null,"abstract":"<div><div>Karabo is a versatile Python-based software framework simplifying research with radio astronomy data. It bundles existing software packages into a coherent whole to improve the ease of use of its components. Karabo includes useful abstractions, like strategies to scale and parallelize typical workloads or science-specific Python modules. The framework includes functionality to access datasets and mock observations to study the Square Kilometre Array (SKA) instruments and their expected accuracy. SKA will address problems in a wide range of fields of astronomy. We demonstrate the application of Karabo relevant to some of the SKA science cases from HI intensity mapping, simulation of the radio surveys, radio source detection, the epoch of re-ionization and heliophysics. We discuss the capabilities, scalabilities and challenges of simulating large radio datasets in the context of SKA.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101004"},"PeriodicalIF":1.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159565","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":"Co-SOM: Co-training for photometric redshift estimation using Self-Organizing Maps","authors":"A. Callejas-Tavera ,&nbsp;E. Molino-Minero-Re ,&nbsp;O. Valenzuela","doi":"10.1016/j.ascom.2025.101019","DOIUrl":"10.1016/j.ascom.2025.101019","url":null,"abstract":"<div><div>The upcoming galaxy large-scale surveys, such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), will generate photometry for billions of galaxies. The interpretation of large-scale weak lensing maps, as well as the estimation of galaxy clustering, requires reliable redshifts with high precision for multi-band photometry. However, obtaining spectroscopy for billions of galaxies is impractical and complex; therefore, having a sufficiently large number of galaxies with spectroscopic observations to train supervised algorithms for accurate redshift estimation is a significant challenge and an open research area. We propose a novel methodology called Co-SOM based on Co-training and Self-Organizing Maps (SOM), integrating labeled (sources with spectroscopic redshifts) and unlabeled (sources with photometric observations only) data during the training process, through a selection method based on map topology (connectivity structure of the SOM lattice) to leverage the limited spectroscopy available for photo-z estimation. We utilized the magnitudes and colors of Sloan Digital Sky Survey data release 18 (SDSS-DR18) to analyze and evaluate the performance, varying the proportion of labeled data and adjusting the training parameters. For training sets of 1% of labeled data (<span><math><mrow><mo>≈</mo><mn>20</mn><mo>,</mo><mn>000</mn></mrow></math></span> galaxies) we achieved a performance of bias <span><math><mrow><mi>Δ</mi><mi>z</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>00007</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>00022</mn></mrow></math></span>, precision <span><math><mrow><msub><mrow><mi>σ</mi></mrow><mrow><mi>z</mi><mi>p</mi></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>00063</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>00032</mn></mrow></math></span> and outlier fraction <span><math><mrow><mi>o</mi><mi>u</mi><mi>t</mi><mtext>_</mtext><mi>f</mi><mi>r</mi><mi>a</mi><mi>c</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>02083</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>00027</mn></mrow></math></span>. Additionally, we conducted experiments varying the volume of labeled data, and the bias remains below <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></math></span>, regardless of the size of the spectroscopic or photometric data. These low-redshift results demonstrate the potential of semi-supervised learning to address spectroscopic limitations in future photometric surveys.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101019"},"PeriodicalIF":1.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417557","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":"Encapsulating textual contents into a MOC data structure for advanced applications","authors":"Giuseppe Greco ,&nbsp;Thomas Boch ,&nbsp;Pierre Fernique ,&nbsp;Manon Marchand ,&nbsp;Mark Allen ,&nbsp;Francois-Xavier Pineau ,&nbsp;Matthieu Baumann ,&nbsp;Marco Molinaro ,&nbsp;Roberto De Pietri ,&nbsp;Marica Branchesi ,&nbsp;Steven Schramm ,&nbsp;Gergely Dálya ,&nbsp;Elahe Khalouei ,&nbsp;Barbara Patricelli ,&nbsp;Giulia Stratta","doi":"10.1016/j.ascom.2025.101014","DOIUrl":"10.1016/j.ascom.2025.101014","url":null,"abstract":"<div><h3>Context:</h3><div>The Multi-Order Coverage map (MOC) is a widely adopted standard promoted by the International Virtual Observatory Alliance (IVOA) to support data sharing and interoperability within the Virtual Observatory (VO) ecosystem. This hierarchical data structure efficiently encodes and visualizes irregularly shaped regions of the sky, enabling applications such as cross-matching large astronomical catalogs, visualizing multi-wavelength and multi-messenger surveys, and facilitating collaborative research through seamless interoperability in big-data-driven exploration.</div></div><div><h3>Aims:</h3><div>This study aims to explore potential enhancements to the MOC data structure by encapsulating textual descriptions and semantic embeddings into sky regions. Specifically, we introduce “Textual MOCs”, in which textual content is encapsulated, and “Semantic MOCs” that transform textual content into semantic embeddings. These enhancements are designed to enable advanced operations such as similarity searches and complex queries and to integrate with generative artificial intelligence (GenAI) tools to improve context-aware interactions and response accuracy in astronomical data analysis, and support agent-based applications.</div></div><div><h3>Method:</h3><div>We experimented with Textual MOCs by annotating detailed descriptions directly into the MOC sky regions, enriching the maps with contextual information suitable for interactive learning tools. For Semantic MOCs, we converted the textual content into semantic embeddings, numerical representations capturing textual meanings in multidimensional spaces, and stored them in high-dimensional vector databases optimized for efficient retrieval.</div></div><div><h3>Results:</h3><div>The implementation of Textual MOCs enhances user engagement by providing meaningful descriptions within sky regions, facilitating the development of effective game-based learning. Semantic MOCs enable sophisticated query capabilities, such as similarity-based searches and context-aware data retrieval, enhancing astronomical data analyses. Integration with multimodal generative AI systems allows for more accurate and contextually relevant interactions supporting both spatial, semantic and visual operations for advancing astronomical data analysis capabilities. Through straightforward examples, we discuss the fundamentals of this new experimental implementation.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101014"},"PeriodicalIF":1.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269554","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":"RFI detection based on semi-supervised learning with improved Unet","authors":"J. Li,&nbsp;B. Liang,&nbsp;S. Feng,&nbsp;W. Dai,&nbsp;S. Wei","doi":"10.1016/j.ascom.2025.101020","DOIUrl":"10.1016/j.ascom.2025.101020","url":null,"abstract":"<div><div>Radio Frequency Interference (RFI) suppression is a crucial component of radio astronomical data processing. Accurate elimination of interference maintains the maximum observation purity for astronomical signals. Existing machine learning-based detection methods are overly reliant on fully labeled data, often requiring thousands of annotated samples to achieve satisfactory performance. Aiming at this limitation, we propose the Allspark-Unet model in this paper. It is a semi-supervised semantic segmentation network that incorporates a dedicated feature enhancement mechanism to reconstruct the feature representation of RFI signals. While achieving superior performance, the proposed architecture introduces a computational overhead compared to simpler baselines, representing a meaningful trade-off between performance gains and resource consumption. Experiments are conducted using a real dataset from the 40-meter radio telescope at Yunnan Observatory. Results demonstrate an accuracy of 0.98 with only 272 labeled data samples. Compared to the baseline method, an improvement of 1.52% in the F1 score (to 0.90) is achieved along with a 2.18% gain in the mean Intersection over Union (mIoU). Quantitative analysis reveals that Allspark-Unet effectively reduces the dependence on labeled data for RFI detection. The proposed feature reconstruction mechanism enables reliable interference detection even in small-sample scenarios. The detailed analysis of this performance-computational cost trade-off is presented and discussed in the study.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101020"},"PeriodicalIF":1.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466462","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":"Stellar spectral classification using convolutional neural networks on objective prism plates","authors":"Seungwan Han ,&nbsp;Wonseok Kang ,&nbsp;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":"2026-01-01","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}

{"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":"2026-01-01","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}

{"title":"High-order wavelet-based numerical algorithms for nonlinear singular Lane–Emden–Fowler equations: Applications to physical models in astrophysics","authors":"Muhammad Ahsan ,&nbsp;Shadab Rehman ,&nbsp;Masood Ahmad ,&nbsp;Maher Alwuthaynani ,&nbsp;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":"2026-01-01","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}

{"title":"Cosmological anisotropies with Finsler–Randers geometry and large-scale observations","authors":"J. Praveen,&nbsp;S.K. Narasimhamurthy","doi":"10.1016/j.ascom.2025.101001","DOIUrl":"10.1016/j.ascom.2025.101001","url":null,"abstract":"<div><div>We investigate a cosmological model grounded in Finsler–Randers geometry introducing anisotropic corrections via the Barthel connection and a redshift-dependent parameter of the form <span><math><mrow><mi>η</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow><mo>=</mo><mn>1</mn><mo>+</mo><mi>n</mi><mi>z</mi></mrow></math></span>. This framework extends standard cosmology by incorporating directional dependencies in spacetime and modifies the evolution of principal cosmological parameters including the Hubble parameter <span><math><mrow><mi>H</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow></mrow></math></span>, deceleration parameter <span><math><mrow><mi>q</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow></mrow></math></span> and dark energy equation of state <span><math><mrow><mi>ω</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow></mrow></math></span>. By fitting the model to current Cosmic Chronometers (CC), Baryon Acoustic Oscillations (BAO), and Pantheon+ supernova datasets, we constrain the anisotropy parameter <span><math><mi>n</mi></math></span> and obtain updated best-fit values for <span><math><msub><mrow><mi>H</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>, <span><math><msub><mrow><mi>Ω</mi></mrow><mrow><mi>m</mi><mn>0</mn></mrow></msub></math></span>, <span><math><mi>n</mi></math></span> and <span><math><msub><mrow><mi>ω</mi></mrow><mrow><mi>DE</mi></mrow></msub></math></span>. Our results indicate that the linear anisotropy can lead to non-trivial modifications in cosmic expansion with the predicted Hubble constant <span><math><mrow><msub><mrow><mi>H</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>=</mo><mn>68</mn><mo>.</mo><mn>56</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>10</mn></mrow></math></span> km/s/Mpc lying between the Planck and SH0ES values, thereby partially alleviating the Hubble tension. This study demonstrates that geometric anisotropy within the Finsler–Randers framework provides a physically motivated extension to <span><math><mi>Λ</mi></math></span>CDM and offers new possibilities of studying outstanding challenges in modern cosmology in Finsler geometric background.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"54 ","pages":"Article 101001"},"PeriodicalIF":1.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100077","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":"Nature-inspired optimization, the Philippine Eagle, and cosmological parameter estimation","authors":"Reginald Christian Bernardo ,&nbsp;Erika Antonette Enriquez ,&nbsp;Renier Mendoza ,&nbsp;Reinabelle Reyes ,&nbsp;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":"2026-01-01","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}