Astronomy and Computing最新文献

{"title":"On the equilibrium points of the collinear restricted 4-body problem with non-spherical bodies","authors":"H.I. Alrebdi ,&nbsp;K.S. Al-mugren ,&nbsp;F.L. Dubeibe ,&nbsp;M.S. Suraj ,&nbsp;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 ,&nbsp;M. Bhushan ,&nbsp;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}

{"title":"Reconstructing the mid-infrared spectra of galaxies using ultraviolet to submillimeter photometry and Deep Generative Networks","authors":"Agapi Rissaki ,&nbsp;O. Pavlou ,&nbsp;D. Fotakis ,&nbsp;V. Papadopoulou Lesta ,&nbsp;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}

{"title":"Membership determination in open clusters using the DBSCAN Clustering Algorithm","authors":"M. Raja ,&nbsp;P. Hasan ,&nbsp;Md. Mahmudunnobe ,&nbsp;Md. Saifuddin ,&nbsp;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 ,&nbsp;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}

{"title":"Galmoss: A package for GPU-accelerated galaxy profile fitting","authors":"Mi Chen ,&nbsp;Rafael S. de Souza ,&nbsp;Quanfeng Xu ,&nbsp;Shiyin Shen ,&nbsp;Ana L. Chies-Santos ,&nbsp;Renhao Ye ,&nbsp;Marco A. Canossa-Gosteinski ,&nbsp;Yanping Cong","doi":"10.1016/j.ascom.2024.100825","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100825","url":null,"abstract":"<div><p>We introduce <span>galmoss</span>, a <span>python</span>-based, <span>torch</span>-powered tool for two-dimensional fitting of galaxy profiles. By seamlessly enabling GPU parallelization, <span>galmoss</span> meets the high computational demands of large-scale galaxy surveys, placing galaxy profile fitting in the CSST/LSST-era. It incorporates widely used profiles such as the Sérsic, Exponential disk, Ferrer, King, Gaussian, and Moffat profiles, and allows for the easy integration of more complex models. Tested on 8289 galaxies from the Sloan Digital Sky Survey (SDSS) g-band with a single NVIDIA A100 GPU, <span>galmoss</span> completed classical Sérsic profile fitting in about 10 min. Benchmark tests show that <span>galmoss</span> achieves computational speeds that are 6 <span><math><mo>×</mo></math></span> faster than those of default implementations.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"47 ","pages":"Article 100825"},"PeriodicalIF":2.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213133724000404/pdfft?md5=aa814e641039f49d85afa52faa3ce567&pid=1-s2.0-S2213133724000404-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140633059","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":"YOLO-ET: A Machine Learning model for detecting, localising and classifying anthropogenic contaminants and extraterrestrial microparticles optimised for mobile processing systems","authors":"L.J. Pinault ,&nbsp;H. Yano ,&nbsp;K. Okudaira ,&nbsp;I.A. Crawford","doi":"10.1016/j.ascom.2024.100828","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100828","url":null,"abstract":"<div><p>Imminent robotic and human activities on the Moon and other planetary bodies would benefit from advanced <em>in situ</em> Computer Vision and Machine Learning capabilities to identify and quantify microparticle terrestrial contaminants, lunar regolith disturbances, the flux of interplanetary dust particles, possible interstellar dust, <span><math><mi>β</mi></math></span>-meteoroids, and secondary impact ejecta. The YOLO-ET (ExtraTerrestrial) algorithm, an innovation in this field, fine-tunes Tiny-YOLO to specifically address these challenges. Designed for coreML model transference to mobile devices, the algorithm facilitates edge computing in space environment conditions. YOLO-ET is deployable as an app on an iPhone with LabCam® optical enhancement, ready for space application ruggedisation. Training on images from the Tanpopo aerogel panels returned from Japan’s Kibo module of the International Space Station, YOLO-ET demonstrates a 90% detection rate for surface contaminant microparticles on the aerogels, and shows promising early results for detection of both microparticle contaminants on the Moon and for evaluating asteroid return samples. YOLO-ET’s application to identifying spacecraft-derived microparticles in lunar regolith simulant samples and SEM images of asteroid Ryugu samples returned by Hayabusa2 and curated by JAXA’s Institute of Space and Astronautical Sciences indicate strong model performance and transfer learning capabilities for future extraterrestrial applications.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"47 ","pages":"Article 100828"},"PeriodicalIF":2.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S221313372400043X/pdfft?md5=25e2760231044e2c4d963333551bb0c4&pid=1-s2.0-S221313372400043X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140879259","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":"RFI-DRUnet: Restoring dynamic spectra corrupted by radio frequency interference—Application to pulsar observations","authors":"X. Zhang ,&nbsp;I. Cognard ,&nbsp;N. Dobigeon","doi":"10.1016/j.ascom.2024.100822","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100822","url":null,"abstract":"<div><p>Radio frequency interference (RFI) has been an enduring concern in radio astronomy, particularly for the observations of pulsars which require high timing precision and data sensitivity. In most works of the literature, RFI mitigation has been formulated as a detection task that consists of localizing possible RFI in dynamic spectra. This strategy inevitably leads to a potential loss of information since parts of the signal identified as possibly RFI-corrupted are generally not considered in the subsequent data processing pipeline. Conversely, this work proposes to tackle RFI mitigation as a joint detection and restoration that allows parts of the dynamic spectrum affected by RFI to be not only identified but also recovered. The proposed supervised method relies on a deep convolutional network whose architecture inherits the performance reached by a recent yet popular image-denoising network. To train this network, a whole simulation framework is built to generate large data sets according to physics-inspired and statistical models of the pulsar signals and of the RFI. The relevance of the proposed approach is quantitatively assessed by conducting extensive experiments. In particular, the results show that the restored dynamic spectra are sufficiently reliable to estimate pulsar times-of-arrivals with an accuracy close to the one that would be obtained from RFI-free signals.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"47 ","pages":"Article 100822"},"PeriodicalIF":2.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213133724000374/pdfft?md5=8c069fb5422507d7990c3ec6d6ed82ed&pid=1-s2.0-S2213133724000374-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140350503","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":"Affine EoS cosmologies: Observational and dynamical system constraints","authors":"A. Singh ,&nbsp;S. Krishnannair","doi":"10.1016/j.ascom.2024.100827","DOIUrl":"10.1016/j.ascom.2024.100827","url":null,"abstract":"<div><p>Within the framework of homogeneous and isotropic metric having flat spatial sections, we show that the accelerating universe expansion phenomena may be addressed with the dark fluid satisfying affine equation of state (EoS). The constraints on model parameters are presented by utilizing the late-times cosmic observational data and dynamical system perspectives. The late-time constraints on the model parameters are placed by using the Bayesian Monte Carlo method analysis. The dynamical system constraints are imposed by using the linear stability theory. We further analyze the behavior of cosmographic parameters, statefinder diagnostic and the energy conditions to explore different features of the universe in model. The cosmological parameters with the best fit values suggest that the universe is expanding with acceleration, classically stable and free from the finite-time future singularities.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"47 ","pages":"Article 100827"},"PeriodicalIF":2.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140786574","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":"Rotation and flipping invariant self-organizing maps with astronomical images: A cookbook and application to the VLA Sky Survey QuickLook images","authors":"A.N. Vantyghem ,&nbsp;T.J. Galvin ,&nbsp;B. Sebastian ,&nbsp;C.P. O’Dea ,&nbsp;Y.A. Gordon ,&nbsp;M. Boyce ,&nbsp;L. Rudnick ,&nbsp;K. Polsterer ,&nbsp;H. Andernach ,&nbsp;M. Dionyssiou ,&nbsp;P. Venkataraman ,&nbsp;R. Norris ,&nbsp;S.A. Baum ,&nbsp;X.R. Wang ,&nbsp;M. Huynh","doi":"10.1016/j.ascom.2024.100824","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100824","url":null,"abstract":"<div><p>Modern wide field radio surveys typically detect millions of objects. Manual determination of the morphologies is impractical for such a large number of radio sources. Techniques based on machine learning are proving to be useful for classifying large numbers of objects. The self-organizing map (SOM) is an unsupervised machine learning algorithm that projects a many-dimensional dataset onto a two- or three-dimensional lattice of neurons. This dimensionality reduction allows the user to visualize common features of the data better and develop algorithms for classifying objects that are not otherwise possible with large datasets. To this aim, we use the PINK implementation of a SOM. PINK incorporates rotation and flipping invariance so that the SOM algorithm may be applied to astronomical images. In this cookbook we provide instructions for working with PINK, including preprocessing the input images, training the model, and offering lessons learned through experimentation. The problem of imbalanced classes can be improved by careful selection of the training sample and increasing the number of neurons in the SOM (chosen by the user). Because PINK is not scale-invariant, structure can be smeared in the neurons. This can also be improved by increasing the number of neurons in the SOM.</p><p>We also introduce <span>pyink</span>, a Python package used to read and write PINK binary files, assist in common preprocessing operations, perform standard analyses, visualize the SOM and preprocessed images, and create image-based annotations using a graphical interface. A tutorial is also provided to guide the user through the entire process. We present an application of PINK to VLA Sky Survey (VLASS) images. We demonstrate that the PINK is generally able to group VLASS sources with similar morphology together. We use the results of PINK to estimate the probability that a given source in the VLASS QuickLook Catalogue is actually due to sidelobe contamination.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"47 ","pages":"Article 100824"},"PeriodicalIF":2.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213133724000398/pdfft?md5=2e063f0d599e31cc1166a1b2c8cd555a&pid=1-s2.0-S2213133724000398-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140550806","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}