Astronomy and Computing最新文献

{"title":"Thermodynamic topology of Hot NUT-Kerr-Newman-Kasuya-Anti-de Sitter black hole","authors":"M.U. Shahzad ,&nbsp;Nazek Alessa ,&nbsp;A. Mehmood ,&nbsp;R. Javed","doi":"10.1016/j.ascom.2024.100900","DOIUrl":"10.1016/j.ascom.2024.100900","url":null,"abstract":"<div><div>This work is devoted to studying the thermodynamic topology of Hot Nut-Kerr-Newman-Kasuya-AdS black holes (BHs). To accomplish this, we identify the critical points that play a crucial role in understanding the intricate structure of these BHs. We determine these critical points’ associated topological charges, providing important information about the underlying topology. Furthermore, we explore the concept of zero points and their corresponding winding numbers. These calculations allow us to evaluate the topological number associated with BHs and provide important information about their overall topological characteristics. One of the most important characteristics is the triple critical point. These points support the conjecture that the behavior of BHs undergoes significant transformations, presenting us with unique opportunities to observe and comprehend novel physical phenomena. A winding number of zero corresponds to a simple, connected horizon, while non-zero winding numbers indicate more complex topologies, such as multi-sheeted horizons. For comprehensive analysis, we fixed various parameters, such as the electric and magnetic charges and the electric and magnetic potentials. This approach allows us to isolate and examine specific aspects of BH topology.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"51 ","pages":"Article 100900"},"PeriodicalIF":1.9,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144114","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":"Thermodynamic topology of Phantom AdS black holes in massive gravity via non-extensive entropies","authors":"Abdelhay Salah Mohamed ,&nbsp;M.U. Shahzad ,&nbsp;A. Mehmood ,&nbsp;E.E. Zotos","doi":"10.1016/j.ascom.2024.100909","DOIUrl":"10.1016/j.ascom.2024.100909","url":null,"abstract":"<div><div>In this study, we explore the topological numbers associated with Phantom AdS black holes (BHs) in massive gravity using non-extensive entropies. We analyze their characteristics using various entropy measures, including the Bekenstein–Hawking entropy, Rényi entropy, Barrows entropy, Sharma-Mittal entropy, and the logarithmic-corrected entropy. We observe that the calculated topological number (<span><math><mi>T</mi></math></span>) varies across different entropy measures. Nevertheless, what is particularly intriguing is the persistent coherence in the topological classification of BH, regardless of whether we consider the Rényi entropy, the logarithmic-corrected entropy or the Bekenstein–Hawking entropy.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"51 ","pages":"Article 100909"},"PeriodicalIF":1.9,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144115","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":"New crescent moon detection using Circular Hough Transform (CHT)","authors":"A.L.A.M. Nasir ,&nbsp;R. Umar ,&nbsp;W.N.J.W. Yussof ,&nbsp;N. Ahmad ,&nbsp;A.N. Zulkeflee ,&nbsp;N.H. Sabri ,&nbsp;W.M.Y.W. Chik ,&nbsp;N.A. Mahiddin ,&nbsp;A.H. Bely ,&nbsp;A.I.S. Izdihar","doi":"10.1016/j.ascom.2024.100902","DOIUrl":"10.1016/j.ascom.2024.100902","url":null,"abstract":"<div><div>Over 1.5 billion Muslims worldwide rely on a calendar based on the observation of the new crescent moon, particularly during the Hijri calendar months of Ramadhan, Shawal, and Zulhijjah. Accurately detecting the new crescent moon immediately after its conjunction is essential for ensuring the precision of the calendar, but it poses a technical challenge due to the low contrast between the new crescent moon and its background. This study aimed to solve the challenge of reliably identifying the new crescent moon at this critical phase, which is vital for accurately determining important dates that signify religious events. It intended to create and verify a computer vision model that would greatly improves the precision and efficiency of identifying a new crescent moon based on images. This will assist in precisely identifying important dates relevant to Muslim religious practices. The methodology employs image preprocessing techniques such as Gaussian Blur for image smoothing and Adaptive Thresholding for contrast enhancement to optimize the visibility of the crescent moon in a single image. The Circular Hough Transform (CHT) technique was used to accurately detect the new crescent moon, while the OpenCV software package helped to implement all of these methods, thus, providing a strong foundation for precisely identifying the new crescent moon. Findings of this study, which involved observations at Kolej Ugama Sultan Zainal Abidin (KUSZA) Observatory, Teluk Kemang Observatory, and Miri Observatory, indicate that the utilization of image processing techniques results in significant improvements in both the efficiency and precision of identifying and detecting the new crescent moon. These enhancements exceed the performance of conventional digital imaging methods employed during regular observations. The model’s processing rate surpasses the usual observation efficiency and showcases exceptional accuracy in distinguishing images that feature the new crescent moon from those that do not. In conclusion, the proposed model demonstrates an effective and efficient methodology for improving the precision of lunar calendar management and the quick planning of important Islamic religious events in accordance with the lunar cycle. This technological advancement offers a reliable method for accurately seeing the crescent moon, which has important implications for enhancing the precision of lunar-based calendars and coordinating important Islamic religious occasions.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"51 ","pages":"Article 100902"},"PeriodicalIF":1.9,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144108","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":"Processing of GASKAP-Hi pilot survey data using a commercial supercomputer","authors":"I.P. Kemp ,&nbsp;N.M. Pingel ,&nbsp;R. Worth ,&nbsp;J. Wake ,&nbsp;D.A. Mitchell ,&nbsp;S.D. Midgely ,&nbsp;S.J. Tingay ,&nbsp;J. Dempsey ,&nbsp;H. Dénes ,&nbsp;J.M. Dickey ,&nbsp;S.J. Gibson ,&nbsp;K.E. Jameson ,&nbsp;C. Lynn ,&nbsp;Y.K. Ma ,&nbsp;A. Marchal ,&nbsp;N.M. McClure-Griffiths ,&nbsp;S. Stanimirović ,&nbsp;J. Th. van Loon","doi":"10.1016/j.ascom.2024.100901","DOIUrl":"10.1016/j.ascom.2024.100901","url":null,"abstract":"<div><div>Modern radio telescopes generate large amounts of data, with the next generation Very Large Array (ngVLA) and the Square Kilometre Array (SKA) expected to feed up to 292 GB of visibilities per second to the science data processor (SDP). However, the continued exponential growth in the power of the world’s largest supercomputers suggests that for the foreseeable future there will be sufficient capacity available to provide for astronomers’ needs in processing ‘science ready’ products from the new generation of telescopes, with commercial platforms becoming an option for overflow capacity. The purpose of the current work is to trial the use of commercial high performance computing (HPC) for a large scale processing task in astronomy, in this case processing data from the GASKAP-H<span>i</span> pilot surveys. We delineate a four-step process which can be followed by other researchers wishing to port an existing workflow from a public facility to a commercial provider. We used the process to provide reference images for an ongoing upgrade to ASKAPSoft (the ASKAP SDP software), and to provide science images for the GASKAP collaboration, using the joint deconvolution capability of WSClean. We document the approach to optimising the pipeline to minimise cost and elapsed time at the commercial provider, and give a resource estimate for processing future full survey data. Finally we document advantages, disadvantages, and lessons learned from the project, which will aid other researchers aiming to use commercial supercomputing for radio astronomy imaging. We found the key advantage to be immediate access and high availability, and the main disadvantage to be the need for improved HPC knowledge to take best advantage of the facility.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"51 ","pages":"Article 100901"},"PeriodicalIF":1.9,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AstroMLab 1: Who wins astronomy jeopardy!?","authors":"Y.-S. Ting (丁源森) ,&nbsp;T.D. Nguyen ,&nbsp;T. Ghosal ,&nbsp;R. Pan (潘瑞) ,&nbsp;H. Arora ,&nbsp;Z. Sun (孙泽昌) ,&nbsp;T. de Haan ,&nbsp;N. Ramachandra ,&nbsp;A. Wells ,&nbsp;S. Madireddy ,&nbsp;A. Accomazzi","doi":"10.1016/j.ascom.2024.100893","DOIUrl":"10.1016/j.ascom.2024.100893","url":null,"abstract":"<div><div>We present a comprehensive evaluation of proprietary and open-weights large language models using the first astronomy-specific benchmarking dataset. This dataset comprises 4,425 multiple-choice questions curated from the Annual Review of Astronomy and Astrophysics, covering a broad range of astrophysical topics.<span><span>¹</span></span> Our analysis examines model performance across various astronomical subfields and assesses response calibration, crucial for potential deployment in research environments. Claude-3.5-Sonnet outperforms competitors by up to 4.6 percentage points, achieving 85.0% accuracy. For proprietary models, we observed a universal reduction in cost every 3-to-12 months to achieve similar score in this particular astronomy benchmark. open-weights models have rapidly improved, with LLaMA-3-70b (80.6%) and Qwen-2-72b (77.7%) now competing with some of the best proprietary models. We identify performance variations across topics, with non-English-focused models generally struggling more in exoplanet-related fields, stellar astrophysics, and instrumentation related questions. These challenges likely stem from less abundant training data, limited historical context, and rapid recent developments in these areas. This pattern is observed across both open-weights and proprietary models, with regional dependencies evident, highlighting the impact of training data diversity on model performance in specialized scientific domains. Top-performing models demonstrate well-calibrated confidence, with correlations above 0.9 between confidence and correctness, though they tend to be slightly underconfident. The development for fast, low-cost inference of open-weights models presents new opportunities for affordable deployment in astronomy. The rapid progress observed suggests that LLM-driven research in astronomy may become feasible in the near future.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"51 ","pages":"Article 100893"},"PeriodicalIF":1.9,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745130","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":"Extended black hole solutions in Rastall theory of gravity","authors":"M. Sharif ,&nbsp;M. Sallah","doi":"10.1016/j.ascom.2024.100897","DOIUrl":"10.1016/j.ascom.2024.100897","url":null,"abstract":"<div><div>We utilize the gravitational decoupling via the extended geometric deformation to extend the Schwarzschild vacuum solution to new black holes in Rastall theory. By employing linear transformations that deform both the temporal and radial coefficients of the metric, the field equations with a dual matter source are successfully decoupled into two sets. The first of these sets is described by the metric for the vacuum Schwarzschild spacetime, while the second set corresponds to the added extra source. Three extended solutions are obtained using two restrictions on the metric potentials and extra source, respectively. For selected values of the Rastall and decoupling parameters, we study the impact of the fluctuation of these parameters on the obtained models. We also investigate the asymptotic flatness of the resulting spacetimes by analysis of the metric coefficients. Finally, the nature of the additional source is explored for each model, via analysis of the energy conditions. It is found among other results that none of the obtained models satisfy the energy conditions, while only the model corresponding to the barotropic equation of state mimics an asymptotically flat spacetime.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"50 ","pages":"Article 100897"},"PeriodicalIF":1.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702064","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":"MAR: A Multiband Astronomical Reduction package","authors":"G.B. Oliveira Schwarz ,&nbsp;F. Herpich ,&nbsp;F. Almeida-Fernandes ,&nbsp;L. Nakazono ,&nbsp;N.M. Cardoso ,&nbsp;E. Machado-Pereira ,&nbsp;W. Schoenell ,&nbsp;H.D. Perottoni ,&nbsp;K. Menéndez-Delmestre ,&nbsp;L. Sodré ,&nbsp;A. Kanaan ,&nbsp;T. Ribeiro","doi":"10.1016/j.ascom.2024.100899","DOIUrl":"10.1016/j.ascom.2024.100899","url":null,"abstract":"<div><div>The Multiband Astronomical Reduction (MAR) is a multithreaded data reduction pipeline designed to handle raw astronomical images from the Southern Photometric Local Universe Survey, transforming them into frames that are ready for source extraction, photometry and flux calibration. MAR is a complete software written almost entirely in Python, with a flexible object-oriented approach, simplifying the implementation of new moduli. It contains a Python package, <span>mar</span>, with all essential operations to be used, a server where the pipeline resides, an interface that allows users to navigate quickly, and a database to store all data as well as important information and procedures applied to the images. MAR is now regularly used to process data from the Southern Photometric Local Universe Survey, but its methods may be used for developing other multiband data reduction packages. This paper explains each pipeline modulus of MAR and describes how its routines work.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"51 ","pages":"Article 100899"},"PeriodicalIF":1.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143144110","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":"Classification of galaxies from image features using best parameter selection by horse herd optimization algorithm (HOA)","authors":"Ahmadreza Yeganehmehr,&nbsp;Hossein Ebrahimnezhad","doi":"10.1016/j.ascom.2024.100898","DOIUrl":"10.1016/j.ascom.2024.100898","url":null,"abstract":"<div><div>With the advancement of observation technology, visual data has made significant progress, rendering manual image classification less effective. Consequently, various image processing and automatic classification methods have garnered attention from researchers. Scientists estimate that there are approximately 2 trillion observable galaxies in the universe. Each galaxy possesses unique characteristics that are distinguishable. Therefore, finding a method to quickly and accurately identify these characteristics of each galaxy and classify them rapidly can greatly enhance the galaxy detection and classification process, while minimizing human errors. The objective of the present study is to determine the class of galaxies with from telescope image features using an optimized classifier with best parameters. The proposed method uses the HOA algorithm, based on the behavior of horse herds, to find the best parameters. This method evaluates the model's error with different SVM parameters and selects the optimal SVM parameters for constructing M-SVM. Using this method, the proposed algorithm is trained and ultimately applied to classify the test data. The results indicate that the developed model correctly classified up to 94.11% of the test dataset (1) and 90.74% of the test dataset (2).</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"50 ","pages":"Article 100898"},"PeriodicalIF":1.9,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702063","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":"Accelerating radio astronomy imaging with RICK","authors":"E. De Rubeis ,&nbsp;G. Lacopo ,&nbsp;C. Gheller ,&nbsp;L. Tornatore ,&nbsp;G. Taffoni","doi":"10.1016/j.ascom.2024.100895","DOIUrl":"10.1016/j.ascom.2024.100895","url":null,"abstract":"<div><div>This paper presents an implementation of radio astronomy imaging algorithms on modern High Performance Computing (HPC) infrastructures, exploiting distributed memory parallelism and acceleration throughout multiple GPUs. Our code, called RICK (Radio Imaging Code Kernels), is capable of performing the major steps of the <span><math><mi>w</mi></math></span>-stacking algorithm presented in Offringa et al. (2014) both inter- and intra-node, and in particular has the possibility to run entirely on the GPU memory, minimising the number of data transfers between CPU and GPU. This feature, especially among multiple GPUs, is critical given the huge sizes of radio datasets involved.</div><div>After a detailed description of the new implementations of the code with respect to the first version presented in Gheller et al. (2023), we analyse the performances of the code for each step involved in its execution. We also discuss the pros and cons related to an accelerated approach to this problem and its impact on the overall behaviour of the code. Such approach to the problem results in a significant improvement in terms of runtime with respect to the CPU version of the code, as long as the amount of computational resources does not exceed the one requested by the size of the problem: the code, in fact, is now limited by the communication costs, with the computation that gets heavily reduced by the capabilities of the accelerators.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"50 ","pages":"Article 100895"},"PeriodicalIF":1.9,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A numerical solution of Schrödinger equation for the dynamics of early universe","authors":"M.Z. Mughal ,&nbsp;F. Khan","doi":"10.1016/j.ascom.2024.100894","DOIUrl":"10.1016/j.ascom.2024.100894","url":null,"abstract":"<div><div>Artificial neural networks (ANNs) have attained widespread success across varied disciplines. This study is designated for looking into an application of an integrated intelligent computing paradigm concerning dynamics in the early Universe through numerical solutions to the Schrödinger equation. To arrive at this we leverage the Levenberg–Marquardt backpropagation networks (LMBNs) to probe cosmic evolution in the early Universe with the Friedmann–Lemaitre–Robertson–Walker (FLRW) metric for a flat minisuperspace model of the Universe in the background. This leads to bridging quantum mechanics and inflationary Universe dynamics conducing to quantum cosmology within the standard model. Wheeler–DeWitt equation corresponds to the time-independent Schrödinger equation obtained from the equations of motion for a single scalar field in flat spacetime with FLRW metric. Utilizing the ntstool the whole computing process is operated for simulation. To evaluate the accuracy and efficiency of the proposed scheme a comparative analysis is carried out. To construct continuous neural network mappings we employ the explicit Runge–Kutta method as the target parameter for generating datasets. To determine the solution datasets of different scenarios, the training, testing, and validation processes are employed to take advantage of these in the learning of neural network models established upon the backpropagation technique of Levenberg–Marquardt. By varying related parameters we develop three scenarios that produce nine cases, three for each. The data plots of performance, training state, error histogram, regression, time-series response, and error autocorrelation represent the visualization of the results. These plots show a complete case description by displaying all the necessary data values. The analysis of these plots is presented to validate all the cases. Performing the analysis by mean square error (MSE) validates the achieved accuracy of the results by validating and verifying neural networks. This work is motivated by the compelling need to develop innovative computational methods for solving complex cosmological questions to untangle the conundrums of the early universe. The attractive numerical solutions of the Schrödinger equation for the early Universe heralds a step towards quantum cosmology based on the interplay of the Wheeler–DeWitt equation and time-independent Schrödinger equation. There is an increasing trend to use computational methods to solve ordinary and partial differential equations with the help of code development in Matlab. For this purpose feed-forward artificial neural network is used for investigating the Schrödinger equation.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"50 ","pages":"Article 100894"},"PeriodicalIF":1.9,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702126","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}