Astronomy and Computing最新文献

{"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}

{"title":"DarsakX: A Python package for designing and analyzing imaging performance of X-ray telescopes","authors":"N.K. Tiwari ,&nbsp;S.V. Vadawale ,&nbsp;N.P.S. Mithun ,&nbsp;C.S. Vaishnava ,&nbsp;B. Saiguhan","doi":"10.1016/j.ascom.2024.100829","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100829","url":null,"abstract":"<div><p>The imaging performance and sensitivity of an X-ray telescope when observing astrophysical sources are primarily governed by the optical design, geometrical uncertainties (figure errors, surface roughness, and mirror alignment inaccuracies), and the reflectivity properties of the X-ray reflecting mirror surface. To thoroughly evaluate the imaging performance of an X-ray telescope with an optical design similar to Wolter-1 optics, which comprises multiple shells with known geometrical uncertainties and mirror reflectivity properties, appropriate computational tools are essential. These tools are used to estimate the angular resolution and effective area for various source energies and locations and, more importantly, to assess the impact of figure errors on the telescope’s imaging performance. Additionally, they can also be used to optimize optics geometry by modifying it in reference to the Wolter-1 optics, aiming to minimize the optical aberration associated with the Wolter-1 configuration. In this paper, we introduce DarsakX, a Python-based ray tracing computational tool specifically designed to estimate the imaging performance of a multi-shell X-ray telescope. DarsakX has the capability to simulate the impact of figure errors present in the axial direction of a mirror shell. The geometrical shape of the mirror shells can be defined as a combination of figure error with the base optics, such as Wolter-1 or Conical optics. Additionally, DarsakX allows the exploration of new optical designs involving two reflections similar to Wolter-1 optics but with an improved angular resolution for wide-field telescopes. Developed through an analytical approach, DarsakX ensures computational efficiency, enabling fast processing.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"47 ","pages":"Article 100829"},"PeriodicalIF":2.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140842939","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":"Probing Weyl-type f(Q,T) gravity: Cosmological implications and constraints","authors":"A.H.A. Alfedeel ,&nbsp;M. Koussour ,&nbsp;N. Myrzakulov","doi":"10.1016/j.ascom.2024.100821","DOIUrl":"10.1016/j.ascom.2024.100821","url":null,"abstract":"&lt;div&gt;&lt;p&gt;In this paper, we investigate the cosmological implications and constraints of Weyl-type &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;Q&lt;/mi&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; gravity. This theory introduces a coupling between the non-metricity &lt;span&gt;&lt;math&gt;&lt;mi&gt;Q&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt; and the trace &lt;span&gt;&lt;math&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt; of the energy–momentum tensor, using the principles of proper Weyl geometry. In this geometry, the scalar non-metricity &lt;span&gt;&lt;math&gt;&lt;mi&gt;Q&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;, which characterizes the deviations from Riemannian geometry, is expressed in its standard Weyl form &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mo&gt;∇&lt;/mo&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;μ&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;g&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;α&lt;/mi&gt;&lt;mi&gt;β&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;w&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;μ&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;g&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;α&lt;/mi&gt;&lt;mi&gt;β&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; and is determined by a vector field &lt;span&gt;&lt;math&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;w&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;μ&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt;. To study the implications of this theory, we propose a deceleration parameter with a single unknown parameter &lt;span&gt;&lt;math&gt;&lt;mi&gt;χ&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;, which we constrain by using the latest cosmological data. By solving the field equations derived from Weyl-type &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;Q&lt;/mi&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; gravity, we aim to understand the behavior of the energy conditions within this framework. In the present work, we consider two well-motivated forms of the function &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;Q&lt;/mi&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;: (i) the linear model represented by &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;Q&lt;/mi&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mi&gt;α&lt;/mi&gt;&lt;mi&gt;Q&lt;/mi&gt;&lt;mo&gt;+&lt;/mo&gt;&lt;mfrac&gt;&lt;mrow&gt;&lt;mi&gt;β&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;6&lt;/mn&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;κ&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/mfrac&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, and (ii) the coupling model represented by &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;f&lt;/mi&gt;&lt;mrow&gt;&lt;mo&gt;(&lt;/mo&gt;&lt;mi&gt;Q&lt;/mi&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;mo&gt;)&lt;/mo&gt;&lt;/mrow&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mfrac&gt;&lt;mrow&gt;&lt;mi&gt;γ&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;6&lt;/mn&gt;&lt;msubsup&gt;&lt;mrow&gt;&lt;mi&gt;H&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msubsup&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;κ&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/mfrac&gt;&lt;mi&gt;Q&lt;/mi&gt;&lt;mi&gt;T&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;, where &lt;span&gt;&lt;math&gt;&lt;mi&gt;α&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;, &lt;span&gt;&lt;math&gt;&lt;mi&gt;β&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;, and &lt;span&gt;&lt;math&gt;&lt;mi&gt;γ&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt; are free parameters. Here, &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;κ&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mfrac&gt;&lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;16&lt;/mn&gt;&lt;mi&gt;π&lt;/mi&gt;&lt;mi&gt;G&lt;/mi&gt;&lt;/mrow&gt;&lt;/mfrac&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; represents the gravitational coupling constant. In both of the models considered, the strong energy condition is violated, indicating consistency with the present accelerated expansion. However, the null, weak, and dominant ener","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"47 ","pages":"Article 100821"},"PeriodicalIF":2.5,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140199472","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":"Orbit classification in a galaxy model with a biaxial dark matter halo","authors":"H.I. Alrebdi ,&nbsp;K.E. Papadakis ,&nbsp;F.L. Dubeibe ,&nbsp;E.E. Zotos","doi":"10.1016/j.ascom.2024.100820","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100820","url":null,"abstract":"<div><p>This paper presents an analysis of a galaxy model with axial symmetry that includes a spherical nucleus, a disk, and a biaxial halo composed of dark matter. The aim is to provide a complete overview of the orbital dynamics of the galactic system and determine the impact of the flattening parameter of the dark matter halo using various numerical methods. Additionally, a comprehensive presentation of the system’s network of symmetric periodic orbits is provided. The findings indicate that the flattening parameter has a reduced effect on the network of symmetric periodic orbits when an oblate halo component is considered, as compared to the case when the prolate halo is present. Finally, the study reveals that the convergence of the characteristic curves of the orbital families with multiplicities 2, 3, 4, and 5 strongly depends on the flattening parameter.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"47 ","pages":"Article 100820"},"PeriodicalIF":2.5,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140141543","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":"Orbital dynamics of a binary system composed of a massive star and a black hole companion","authors":"E.M. Moneer ,&nbsp;F.L. Dubeibe ,&nbsp;E.E. Zotos","doi":"10.1016/j.ascom.2024.100819","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100819","url":null,"abstract":"<div><p>In this work, we consider a binary system composed of a massive star and a black hole companion. By monitoring the mass transfer from the star to the black hole, we perform a thorough and systematic orbit classification for determining the final states of a test particle moving inside the gravitational field of the binary system. For this purpose, we deploy the theory of the time-independent restricted three-body problem for modeling the motion of the test particle. Our analysis strongly suggests that escaping, collisional and bounded motion dominate in different phases of the mass transport, while the overall orbital dynamics of the test particle become very intricate for larger differences between the masses of the two primaries. Moreover, the bounded motion is further classified into regular, sticky, and chaotic by using the SALI chaos indicator.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"47 ","pages":"Article 100819"},"PeriodicalIF":2.5,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140123230","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":"AstroSA: An astronomical observation scheduler assessment framework in python","authors":"H. Xie ,&nbsp;Z. Kang ,&nbsp;X. Jiang","doi":"10.1016/j.ascom.2024.100806","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100806","url":null,"abstract":"<div><p>Time-domain astronomy, as a leading aspect of astronomical research, demands a significant increase in telescope hours. An efficient scheduler is crucial to handle the large number of observational requests effectively. However, the commonly used schedulers in observatories have not yet fully utilized the advancements in mathematics and computer science. In order to establish a connection between astronomy and the latest achievements in these fields, we propose the Astronomical Observing Scheduler Assessment Framework (<span>AstroSA</span>), implemented as a Python package. The <span>AstroSA</span> offers a rapid and user-friendly quantitative evaluator of the scheduler with five built-in metrics: expected quality of observed data, overhead ratio, scientific value, schedule rate, and ratio to the best airmass. Additionally, <span>AstroSA</span> includes a default virtual telescope and a night of cloud coverage, so that users can start to use it with minimal settings.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"47 ","pages":"Article 100806"},"PeriodicalIF":2.5,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140069551","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}