Astronomy and Computing最新文献

{"title":"The statistical OPEA models indicate an anomaly in the solar X-ray flare energy levels","authors":"E. Yoldaş,&nbsp;H.A. Dal","doi":"10.1016/j.ascom.2023.100756","DOIUrl":"https://doi.org/10.1016/j.ascom.2023.100756","url":null,"abstract":"<div><p><span><span>We have recognized an anomaly on the solar activity cycle, while we researching on the question that there can be two different flare mechanisms working on the stellar surface. We discuss an anomaly in the </span>solar flare activity levels in the cycle minima and maxima from the 20th to 22nd Solar Activity Cycles, depending on the models and analyses of the X-ray data. In this study, we used the GOES satellite data accumulated from 1976 to 1989 and detected 670 solar flares. We have separately carried out two independent statistical analyses for the examination of the solar flare activity. Firstly, we derived the equivalent durations distribution via the total durations of flares. Contrary to our expectations based on flare stars, it seems that this distribution is modelled by four different fits, instead of just one model fit. We found that the model parameters, such as </span><span><math><mrow><mi>p</mi><mi>l</mi><mi>a</mi><mi>t</mi><mi>e</mi><mi>a</mi><mi>u</mi></mrow></math></span>, <span><math><mrow><mi>h</mi><mi>a</mi><mi>l</mi><mi>f</mi><mo>−</mo><mi>t</mi><mi>i</mi><mi>m</mi><mi>e</mi></mrow></math></span>, and <span><math><mrow><mi>s</mi><mi>p</mi><mi>a</mi><mi>n</mi></mrow></math></span><span>, vary via time in the different trends from the Solar Activity Cycle. Secondly, we have derived the cumulative frequency models for solar flares. Contrary to the studies in the literature, we found that these frequency models do not take shape depending on the minimum or maximum of a solar activity cycle. If these results are not an unexpected anomaly, this study indicates that the existence of a different trend can be revealed apart from the solar activity cycle for the flare energy level variation, when the solar flare behaviour is separately determined for each cycle and they are compared with each other.</span></p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49733788","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":"Deep learning for crescent detection and recognition: Implementation of Mask R-CNN to the observational Lunar dataset collected with the Robotic Lunar Telescope System","authors":"R. Muztaba ,&nbsp;H.L. Malasan ,&nbsp;M. Djamal","doi":"10.1016/j.ascom.2023.100757","DOIUrl":"https://doi.org/10.1016/j.ascom.2023.100757","url":null,"abstract":"<div><p><span><span>The ability of the human eye to identify a crescent depends on its apparent object contrast versus the sky background, and inaccurate assessments are common when identifying it. The use of telescopes and cameras to monitor the crescent moon is becoming increasingly important as technology advances. Thus, in this study we developed an automated moon detection system with </span>deep learning and integrated for the robotic telescope OZT-ALTS with an infrared camera. By utilizing a deep learning method called Mask R-CNN, we have created infrared camera software with the goal of identifying and recognizing the crescent moon. The result shows, a total of 3,202 manually annotated moon images were used for deep-learning-trained models. We tested several combinations of training hyperparameters and image distribution numbers. The results show that the crescent detection issue can be resolved using a Mask R-CNN. Using the top-performing Mask R-CNN configuration, the trained model achieved a mean averaged precision (mAP) at the intersection over union (IOU) of 0.5, with a 99% for the extreme condition of a young crescent concealed by clouds and a 99% for the normal case for each </span>moon phase. We also show that such systems can be utilized as a framework for future monitoring, detection, and recognition of the young crescent and all moon phases.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49711536","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":"Investigating the suitability of data-driven methods for extracting physical parameters in cosmological models","authors":"K.Y. Kim,&nbsp;H.W. Lee","doi":"10.1016/j.ascom.2023.100762","DOIUrl":"https://doi.org/10.1016/j.ascom.2023.100762","url":null,"abstract":"<div><p>Recent cosmological observations have reached a level of precision that enables the determination and statistical analysis of cosmological parameters with increased accuracy. Despite the significant progress in observational data, our current understanding is still insufficient to fully elucidate the origins of dark energy and dark matter. Addressing the complexities of the observational data may necessitate the development of more sophisticated data analysis techniques or the formulation of new theoretical models. The estimation of some cosmological parameters exhibits variations depending on the chosen physical model, even when utilizing the same observational data. In order to overcome model-dependence, alternative methods such as machine learning techniques based solely on observed data are being explored. However, it is crucial to acknowledge that while this approach may provide insights into the underlying physical laws, it also carries the risk of generating entirely unphysical interpretations.</p><p>The primary objective of this article is to identify the most appropriate data-driven method for extracting physical parameters in cosmological models, with a specific focus on determining the values of two critical parameters: the Hubble constant (<span><math><msub><mrow><mi>H</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>) and the density parameter for dark energy (<span><math><msubsup><mrow><mi>Ω</mi></mrow><mrow><mi>Λ</mi></mrow><mrow><mn>0</mn></mrow></msubsup></math></span>). Our research findings demonstrate a rigorous comparison between the results derived exclusively from observational data and those predicted by the theoretical <span><math><mi>ΛCDM</mi></math></span> (Lambda Cold Dark Matter) model. Through this comparative analysis, we have successfully reaffirmed the effectiveness of the <span><math><mi>ΛCDM</mi></math></span> model in accurately describing the current observed universe.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92142395","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":"Radiation driven-dust hydrodynamics in late-phase AGB stars","authors":"H. Zargarnezhad ,&nbsp;R.J. Myers ,&nbsp;A.K. Speck ,&nbsp;J.A. McFarland","doi":"10.1016/j.ascom.2023.100766","DOIUrl":"https://doi.org/10.1016/j.ascom.2023.100766","url":null,"abstract":"<div><p>The interplay of stellar luminosity variations and dust hydrodynamics in Asymptotic Giant Branch (AGB) stars and the consequences for dust survival and mass loss remain elusive. In this work, we broadly investigate the role of dust and radiation hydrodynamics in forming dust and gas structures, heterogeneous clumps observable in AGB remnants and planetary nebulae (PNe). Of interest in this study are the spatial perturbations driven by instabilities in the space that the mass travels through. These spatial perturbations in the dust and gas field may be responsible for forming larger clumps, such as cometary knots, seen in the PNe phase. Previous studies have considered similar physics in dust-driven winds at shorter lengths and time scales, using either 1D simulations or 2D simulations with a single mixed particle–gas fluid. Here we present an Eulerian–Lagrangian method for studying this problem at larger length and time scales. Simulations are performed in 2D, solving the Euler equations with source terms resulting from the particle phase, represented by free Lagrangian points. Radiation coupling was implemented for the particle phase, modeling radiation heating and acceleration of the particles and subsequent coupling to the gas phase through non-continuum heat and momentum transfer models. Spatial perturbations of the dust and radiation fields were found to drive the formation of small dust mass clumps that survive to late times, though these remain below the size of those observed in many PNe.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91992120","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":"Fast grid to grid interpolation for radio interferometric imaging","authors":"N. Monnier ,&nbsp;F. Orieux ,&nbsp;N. Gac ,&nbsp;C. Tasse ,&nbsp;E. Raffin ,&nbsp;D. Guibert","doi":"10.1016/j.ascom.2023.100767","DOIUrl":"https://doi.org/10.1016/j.ascom.2023.100767","url":null,"abstract":"<div><p>Backward and forward interpolations on a Fourier grid are computationally expensive operations for radio interferometric imaging algorithms. By merging these operations, we propose the Grid to Grid (G2G) method which aims to reduce the computational cost and memory footprint<span>. We have also shown that the oversampling factor used for the convolution function in the G2G method strongly impacted the accuracy and computational speed. Acceleration on graphics processing units<span> (GPU), well suited for this embarrassingly parallel algorithm, has been studied mainly for backward operation. Thus, we propose a GPU and CPU implementation of the G2G method on Nvidia A100 and Intel Ice Lake processors. Experiments have shown a GPU performance improvement with a Fourier-point throughput better than up to 37% regarding standard gridder and degridder.</span></span></p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134655637","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":"An UBVRI calibration method based on Pan-STARRS photometric survey","authors":"X. Li ,&nbsp;X. Zeng ,&nbsp;A. Esamdin ,&nbsp;S. Zheng ,&nbsp;Y. Zhang","doi":"10.1016/j.ascom.2023.100755","DOIUrl":"10.1016/j.ascom.2023.100755","url":null,"abstract":"<div><p><span>The Landolt’s standard stars catalog is the most widely utilized for CCD astronomy in the </span><span><math><mrow><mi>U</mi><mi>B</mi><mi>V</mi><mi>R</mi><mi>I</mi></mrow></math></span><span> broad band photometry system. The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is one of the usage of the newly </span><span><math><mrow><mi>g</mi><mi>r</mi><mi>i</mi><mi>z</mi><mi>y</mi></mrow></math></span><span><span> photometric system. In this work, the main objective of this paper is to calibrate observations in the U, B, V, R, and I bands by utilizing data in the g, r, and i bands from the literature. First, the </span>machine learning<span><span> based method XGBoost is employed to train a model for selecting objects with </span>linear relations between the two catalogs. Then, the color transformation method is utilized to convert objects with </span></span><span><math><mrow><mi>g</mi><mi>r</mi><mi>i</mi></mrow></math></span> magnitudes of Pan-STARRS1 (PS1) catalog to the <span><math><mrow><mi>U</mi><mi>B</mi><mi>V</mi><mi>R</mi><mi>I</mi></mrow></math></span><span> system, and a set of transformation coefficients is presented. A photometric calibration system is developed and the color based calibration method is implemented in the system. The typical calibration errors of standard field </span><span><math><mrow><mi>G</mi><mi>D</mi><mspace></mspace><mn>279</mn></mrow></math></span> from instrumental magnitudes to those of standard <span><math><mrow><mi>U</mi><mi>B</mi><mi>V</mi><mi>R</mi><mi>I</mi></mrow></math></span> system are derived as 0.173<span><math><mspace></mspace></math></span>mag, 0.053<span><math><mspace></mspace></math></span>mag, 0.024<span><math><mspace></mspace></math></span>mag, 0.019<span><math><mspace></mspace></math></span>mag, 0.020<span><math><mspace></mspace></math></span>mag, respectively. Furthermore, the light curve comparison of SN 2017hpa of a large number of observations from different nights suggests that this method can be qualified for various astrometric observations.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49565861","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":"Observational constraints for an axially symmetric transitioning model with bulk viscosity parameterization","authors":"A. Dixit ,&nbsp;A. Pradhan ,&nbsp;V.K. Bhardwaj ,&nbsp;A. Beesham","doi":"10.1016/j.ascom.2023.100768","DOIUrl":"https://doi.org/10.1016/j.ascom.2023.100768","url":null,"abstract":"<div><p>In this paper, we have analyzed the significance of bulk viscosity in an axially symmetric Bianchi type-I model to study the accelerated expansion of the universe. We have considered four bulk viscosity parameterizations for the matter-dominated cosmological model. The function of the two significant Hubble <span><math><mrow><mi>H</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow></mrow></math></span> and deceleration parameters are discussed in detail. The energy parameters of the universe are computed using the most recent observational Hubble data (57 data points) in the redshift range <span><math><mrow><mn>0</mn><mo>.</mo><mn>07</mn><mo>≤</mo><mi>z</mi><mo>≤</mo><mn>2</mn><mo>.</mo><mn>36</mn></mrow></math></span>. In this model, we obtained all feasible solutions with the viscous component and analyzed the universe’s expansion history. Finally, we analyzed the statefinder diagnostic and found some interesting results. The outcomes of our developed model now properly align with observational results.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92073779","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":"An optimized training approach for meteor detection with an attention mechanism to improve robustness on limited data","authors":"V.Y. Shirasuna,&nbsp;A.L.S. Gradvohl","doi":"10.1016/j.ascom.2023.100753","DOIUrl":"10.1016/j.ascom.2023.100753","url":null,"abstract":"<div><p><span>Researchers have extensively used convolutional neural networks<span> to detect meteor falls on Earth. However, when dealing with limited available data, these networks may need more robustness to classify new real-world images correctly. This study proposes an optimized training approach of a pre-trained model with an attention mechanism<span> to achieve better generalization results in such a scenario. We compare two architectures, an optimized base model and another version with an attention mechanism. Furthermore, we present a new and publicly available optical meteor dataset that merges several public data sources. We used the merged dataset to train classification models combined with a stratified five-fold cross-validation strategy to determine the reliability of the prediction. The experimental results from both architectures showed good and similar performance. To further determine the best architecture, we performed an additional analysis with visual explanations in new observations. The architecture with an attention mechanism was the best model achieving a </span></span></span>false alarm ratio of 2.6% and an accuracy of 97%.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41560421","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":"Graph Theoretical Analysis of local ultraluminous infrared galaxies and quasars","authors":"O. Pavlou,&nbsp;I. Michos,&nbsp;V. Papadopoulou Lesta,&nbsp;M. Papadopoulos,&nbsp;E.S. Papaefthymiou,&nbsp;A. Efstathiou","doi":"10.1016/j.ascom.2023.100742","DOIUrl":"10.1016/j.ascom.2023.100742","url":null,"abstract":"<div><p>We present a methodological framework for studying galaxy evolution by utilizing Graph Theory and network analysis tools. We study the evolutionary processes of local ultraluminous infrared galaxies (ULIRGs) and quasars and the underlying physical processes, such as star formation and active galactic nucleus (AGN) activity, through the application of Graph Theoretical analysis tools. We extract, process and analyze mid-infrared spectra of local (z ¡ 0.4) ULIRGs and quasars between 5-38<span><math><mrow><mi>μ</mi><mi>m</mi></mrow></math></span> through internally developed Python routines, in order to generate similarity graphs, with the nodes representing ULIRGs being grouped together based on the similarity of their spectra. Additionally, we extract and compare physical features from the mid-IR spectra, such as the polycyclic aromatic hydrocarbons (PAHs) emission and silicate depth absorption features, as indicators of the presence of star-forming regions and obscuring dust, in order to understand the underlying physical mechanisms of each evolutionary stage of ULIRGs. Our analysis identifies five groups of local ULIRGs based on their mid-IR spectra, which is quite consistent with the well established <em>fork</em> classification diagram by providing a higher level classification. We demonstrate how graph clustering algorithms and network analysis tools can be utilized as unsupervised learning techniques for revealing direct or indirect relations between various galaxy properties and evolutionary stages, which provides an alternative methodology to previous works for classification in galaxy evolution. Additionally, our methodology compares the output of several graph clustering algorithms in order to demonstrate the best-performing Graph Theoretical tools for studying galaxy evolution.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45484754","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 GPU-accelerated viewer for HEALPix maps","authors":"A.V. Frolov","doi":"10.1016/j.ascom.2023.100758","DOIUrl":"https://doi.org/10.1016/j.ascom.2023.100758","url":null,"abstract":"<div><p><span>HEALPix by Górskiet al. (2005) is a de-facto standard for Cosmic Microwave Background (CMB) data storage and analysis, and is widely used in current and upcoming CMB experiments. Almost all the datasets in Legacy Archive for Microwave Background Data Analysis (</span><span>LAMBDA</span><svg><path></path></svg><span>) use HEALPix as a format of choice. Visualizing the data plays important role in research, and several toolsets were developed to do that for HEALPix maps, most notably original Fortran facilities and Python integration with </span><span>healpy</span>. With the current state of GPU performance, it is now possible to visualize extremely large maps in real time on a laptop or a tablet. HEALPix Viewer described here is developed for macOS, and takes full advantage of GPU acceleration to handle extremely large datasets in real time. It compiles natively on Intel and Arm64 architectures, and uses Metal framework for high-performance GPU computations. The aim of this project is to reduce the effort required for interactive data exploration, as well as time overhead for producing publication-quality maps. Drag and drop integration with Keynote and Powerpoint makes creating presentations easy. The main codebase is written in Swift, a modern and efficient compiled language, with high-performance computing parts delegated entirely to GPU, and a few inserts in C interfacing to <span>cfitsio</span><span><span> library for I/O. Graphical user interface is written in SwiftUI, a new declarative UI framework based on Swift. Most common spherical projections and colormaps are supported out of the box, and the available </span>source code makes it easy to customize the application and to add new features if desired. On a M1 Max laptop, an </span><span>nside</span>=8192 maps are processed in real time, with geometry effects rendered at 60fps in full resolution with no appreciable load to the machine. Main user-facing delays are limited to CPU-bound <span>cfitsio</span><span> load times, and sorting needed to construct Cumulative Distribution Function (CDF) estimators for statistical analysis (hidden in background queue). Overall performance improves on the current Python software stack by a factor of 3–180x depending on the task at hand.</span></p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92066511","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}