{"title":"GEPINN: An innovative hybrid method for a symbolic solution to the Lane–Emden type equation based on grammatical evolution and physics-informed neural networks","authors":"Hassan Dana Mazraeh , Kourosh Parand","doi":"10.1016/j.ascom.2024.100846","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100846","url":null,"abstract":"<div><p>In this paper, we present an innovative and powerful combination of grammatical evolution and a physics-informed neural network approach for symbolically solving the Lane–Emden type equation, which is a nonlinear ordinary differential equation. We employ a grammatical evolution algorithm based on a context-free grammar to construct a mathematical expression comprising some parameters. Subsequently, these parameters are determined using the physics-informed neural networks approach. To achieve this, the computational graph of the mathematical expression generated in each iteration of the grammatical evolution is treated as a network. To assess the proposed method, we consider the Lane–Emden type equation. The proposed method demonstrated that it is a capable method for symbolically solving nonlinear ordinary differential equations accurately.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100846"},"PeriodicalIF":2.5,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141242308","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":"Surveying image segmentation approaches in astronomy","authors":"D. Xu , Y. Zhu","doi":"10.1016/j.ascom.2024.100838","DOIUrl":"10.1016/j.ascom.2024.100838","url":null,"abstract":"<div><p>Image segmentation plays a critical role in unlocking the mysteries of the universe, providing astronomers with a clearer perspective on celestial objects within complex astronomical images and data cubes. Manual segmentation, while traditional, is not only time-consuming but also susceptible to biases introduced by human intervention. As a result, automated segmentation methods have become essential for achieving robust and consistent results in astronomical studies. This review begins by summarizing traditional and classical segmentation methods widely used in astronomical tasks. Despite the significant improvements these methods have brought to segmentation outcomes, they fail to meet astronomers’ expectations, requiring additional human correction, further intensifying the labor-intensive nature of the segmentation process. The review then focuses on the transformative impact of machine learning, particularly deep learning, on segmentation tasks in astronomy. It introduces state-of-the-art machine learning approaches, highlighting their applications and the remarkable advancements they bring to segmentation accuracy in both astronomical images and data cubes. As the field of machine learning continues to evolve rapidly, it is anticipated that astronomers will increasingly leverage these sophisticated techniques to enhance segmentation tasks in their research projects. In essence, this review serves as a comprehensive guide to the evolution of segmentation methods in astronomy, emphasizing the transition from classical approaches to cutting-edge machine learning methodologies. We encourage astronomers to embrace these advancements, fostering a more streamlined and accurate segmentation process that aligns with the ever-expanding frontiers of astronomical exploration.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100838"},"PeriodicalIF":2.5,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141132980","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":"Reinforcement learning","authors":"S. Yatawatta","doi":"10.1016/j.ascom.2024.100833","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100833","url":null,"abstract":"<div><p>Observing celestial objects and advancing our scientific knowledge about them involves tedious planning, scheduling, data collection and data post-processing. Many of these operational aspects of astronomy are guided and executed by expert astronomers. Reinforcement learning is a mechanism where we (as humans and astronomers) can teach agents of artificial intelligence to perform some of these tedious tasks. In this paper, we will present a state of the art overview of reinforcement learning and how it can benefit astronomy.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100833"},"PeriodicalIF":2.5,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213133724000489/pdfft?md5=60406246217a5031a654b5b2e6f0f6eb&pid=1-s2.0-S2213133724000489-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141164641","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}
C. Manzano, A. Miskolczi, H. Stiele, V. Vybornov, T. Fieseler, S. Pfalzner
{"title":"Learning from the present for the future: The Jülich LOFAR Long-term Archive","authors":"C. Manzano, A. Miskolczi, H. Stiele, V. Vybornov, T. Fieseler, S. Pfalzner","doi":"10.1016/j.ascom.2024.100835","DOIUrl":"10.1016/j.ascom.2024.100835","url":null,"abstract":"<div><p>The Forschungszentrum Jülich has been hosting the German part of the LOFAR archive since 2013. It is Germany’s most extensive radio astronomy archive, currently storing nearly 22 petabytes (PB) of data. Future radio telescopes are expected to require a dramatic increase in long-term data storage. Here, we take stock of the current data management of the Jülich LOFAR Data Archive, describe the ingestion, the storage system, the export to the long-term archive, and the request chain. We analysed the data availability over the last 10 years and searched for the underlying data access pattern and the energy consumption of the process. We determine hardware-related limiting factors, such as network bandwidth and cache pool availability and performance, and software aspects, e.g. workflow adjustment and parameter tuning, as the main data storage bottlenecks. By contrast, the challenge in providing the data from the archive for the users lies in retrieving the data from the tape archive and staging them. Building on this analysis, we suggest how to avoid/mitigate these problems in the future and define the requirements for future even more extensive long-term data archives.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100835"},"PeriodicalIF":2.5,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213133724000507/pdfft?md5=8384bf7573be7dd5e41b8607f6174d14&pid=1-s2.0-S2213133724000507-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141133607","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}
L. Yu , J. Wang , D. Guo , W. Peng , R. Qiao , K. Gong , Y. Liu , J. Wang , C. Zhang , W. Zhang
{"title":"CNN-based track reconstruction study for gamma-ray pair telescope","authors":"L. Yu , J. Wang , D. Guo , W. Peng , R. Qiao , K. Gong , Y. Liu , J. Wang , C. Zhang , W. Zhang","doi":"10.1016/j.ascom.2024.100834","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100834","url":null,"abstract":"<div><p>MeV Gamma-ray Telescope (MGT) is a conceptual mission aimed at improving the detection sensitivity of gamma-ray astronomy in the MeV energy range. It consists of three sub-detectors: Gamma-ray Conversion silicon tracker, CALOrimeter and Anti-Coincident Detector. In this paper, a track reconstruction algorithm based on Convolutional Neural Networks (CNN) is developed for MGT. In order to train and test the model, Geant4 simulation is used and generates a large number of gamma-ray events at nine energy points in the energy band from 0.5 GeV to 10 GeV. Finally, the reconstruction results of angular resolution, position resolution and acceptance are shown. The testing results indicate that the angular resolution of MGT significantly improves in the <span><math><mrow><mn>0</mn><mo>.</mo><mn>5</mn><mo>∼</mo><mn>10</mn></mrow></math></span> GeV range compared with Fermi-LAT.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100834"},"PeriodicalIF":2.5,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141094888","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}
H.I. Alrebdi , K.S. Al-mugren , F.L. Dubeibe , M.S. Suraj , E.E. Zotos
{"title":"On the equilibrium points of the collinear restricted 4-body problem with non-spherical bodies","authors":"H.I. Alrebdi , K.S. Al-mugren , F.L. Dubeibe , M.S. Suraj , E.E. Zotos","doi":"10.1016/j.ascom.2024.100832","DOIUrl":"10.1016/j.ascom.2024.100832","url":null,"abstract":"<div><p>This study investigates a variation of the collinear restricted four-body problem, introducing complexity by incorporating the oblate or prolate shapes of the three primary bodies. Employing various numerical techniques, we analyze the dynamical properties of the equilibrium points within the system. In addition to identifying the coordinates of the libration points, we examine their linear stability and dynamic classifications. Our primary focus is on understanding the interplay between the system’s mass and shape parameters, revealing how they collectively influence equilibrium dynamics. Specifically, our results demonstrate that oblate-shaped peripheral bodies consistently produce six (6) equilibrium points, while prolate spheroids yield an even number – 6, 10, 14, or 18 – depending on the specific mass and shape parameters.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100832"},"PeriodicalIF":2.5,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141057749","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":"A study of the equilibrium dynamics of the test particle in the collinear circular restricted four-body problem with non-spherical central primary","authors":"M.S. Suraj , M. Bhushan , M.C. Asique","doi":"10.1016/j.ascom.2024.100831","DOIUrl":"10.1016/j.ascom.2024.100831","url":null,"abstract":"<div><p>We consider the collinear restricted four-body problem (CR4BP), where the test particle of infinitesimal mass is moving under the gravitational influence of the three primary bodies. It is further assumed that the central primary is a non-spherical body, particularly either an oblate or prolate spheroid, whereas the peripheral primaries are spherical in shape. A numerical analysis is presented to unveil the effect of the oblateness and prolateness parameters on the position of equilibrium points (EPs) and their linear stability in the CR4BP. Moreover, the permissible regions of possible motion as determined by the zero-velocity surface and associated equipotential curves and the basins of convergence linked with the EPs on the orbital plane are presented. The existence and number of collinear EPs and non-collinear EPs in the problem depend on the combination of the mass parameter of the primaries and the oblateness/prolateness parameter. Additionally, the application of the problem in the Saturn-Moon(1)-Moon(2)-System has been presented.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100831"},"PeriodicalIF":2.5,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141024137","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}
Agapi Rissaki , O. Pavlou , D. Fotakis , V. Papadopoulou Lesta , A. Efstathiou
{"title":"Reconstructing the mid-infrared spectra of galaxies using ultraviolet to submillimeter photometry and Deep Generative Networks","authors":"Agapi Rissaki , O. Pavlou , D. Fotakis , V. Papadopoulou Lesta , A. Efstathiou","doi":"10.1016/j.ascom.2024.100823","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100823","url":null,"abstract":"<div><p>The mid-infrared spectra of galaxies are rich in features such as the Polycyclic Aromatic Hydrocarbon (PAH) and silicate dust features which give valuable information about the physics of galaxies and their evolution. For example they can provide information about the relative contribution of star formation and accretion from a supermassive black hole to the power output of galaxies. However, the mid-infrared spectra are currently available for a very small fraction of galaxies that have been detected in deep multi-wavelength surveys of the sky. In this paper we explore whether Deep Generative Network methods can be used to reconstruct mid-infrared spectra in the 5–35<span><math><mi>μ</mi></math></span>m range using the limited multi-wavelength photometry in <span><math><mrow><mo>∼</mo><mn>20</mn></mrow></math></span> bands from the ultraviolet to the submillimeter which is typically available in extragalactic surveys. For this purpose we use simulated spectra computed with a combination of radiative transfer models for starbursts, active galactic nucleus (AGN) tori and host galaxies. We find that our method using Deep Generative Networks, namely Generative Adversarial Networks and Generative Latent Optimization models, can efficiently produce high quality reconstructions of mid-infrared spectra in <span><math><mo>∼</mo></math></span> 60% of the cases. We discuss how our method can be improved by using more training data, photometric bands, model parameters or by employing other generative networks.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"47 ","pages":"Article 100823"},"PeriodicalIF":2.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140604964","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}
M. Raja , P. Hasan , Md. Mahmudunnobe , Md. Saifuddin , S.N. Hasan
{"title":"Membership determination in open clusters using the DBSCAN Clustering Algorithm","authors":"M. Raja , P. Hasan , Md. Mahmudunnobe , Md. Saifuddin , S.N. Hasan","doi":"10.1016/j.ascom.2024.100826","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100826","url":null,"abstract":"<div><p>In this paper, we apply the machine learning clustering algorithm Density Based Spatial Clustering of Applications with Noise (DBSCAN) to study the membership of stars in twelve open clusters (NGC 2264, NGC 2682, NGC 2244, NGC 3293, NGC 6913, NGC 7142, IC 1805, NGC 6231, NGC 2243, NGC 6451, NGC 6005 and NGC 6583) based on Gaia DR3 Data. This sample of clusters spans a variety of parameters like age, metallicity, distance, extinction and a wide parameter space in proper motions and parallaxes. We obtain reliable cluster members using DBSCAN as faint as <span><math><mrow><mi>G</mi><mo>∼</mo><mn>20</mn></mrow></math></span> mag and also in the outer regions of clusters. With our revised membership list, we plot color-magnitude diagrams and we obtain cluster parameters for our sample using ASteCA and compare it with the catalog values. We also validate our membership sample by spectroscopic data from APOGEE and GALAH for the available data. This paper demonstrates the effectiveness of DBSCAN in membership determination of clusters.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"47 ","pages":"Article 100826"},"PeriodicalIF":2.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140646796","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":"Machine Learning methods in Astronomy","authors":"Maggie Lieu , Ting-Yun Cheng","doi":"10.1016/j.ascom.2024.100830","DOIUrl":"10.1016/j.ascom.2024.100830","url":null,"abstract":"","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"47 ","pages":"Article 100830"},"PeriodicalIF":2.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141043607","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}