Astronomy and Computing最新文献

{"title":"On the effective vibrational temperature of the source using (2)3∏ - X3∏ system of GeC molecule","authors":"R. Sindhan ,&nbsp;N. Venkatesh Bharathi ,&nbsp;S. Ramaswamy","doi":"10.1016/j.ascom.2024.100859","DOIUrl":"10.1016/j.ascom.2024.100859","url":null,"abstract":"<div><p>In this work, the experimental potential energy curves for <span><math><mrow><msup><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow><mn>3</mn></msup><mstyle><mi>Π</mi></mstyle></mrow></math></span> and <span><math><mrow><msup><mrow><mi>X</mi></mrow><mn>3</mn></msup><mstyle><mi>Π</mi></mstyle></mrow></math></span> electronic states of GeC molecule have been constructed by using Rydberg-Klein-Rees (RKR) method. The radiative transition parameters viz., Franck-Condon (FC) factor, r-centroid, electronic transition moment, band strength, relative band strength, Einstein coefficients, radiative lifetime and oscillator strength for the <span><math><mrow><msup><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow><mn>3</mn></msup><mstyle><mi>Π</mi></mstyle><mo>−</mo><msup><mrow><mi>X</mi></mrow><mn>3</mn></msup><mstyle><mi>Π</mi></mstyle></mrow></math></span> system of GeC molecule have been estimated for the experimentally observed vibrational levels from Rydberg-Klein-Rees (RKR) potential and the estimated values are tabulated. The estimated effective vibrational temperature found as 5628 K for the <span><math><mrow><msup><mrow><mo>(</mo><mn>2</mn><mo>)</mo></mrow><mn>3</mn></msup><mstyle><mi>Π</mi></mstyle><mo>−</mo><msup><mrow><mi>X</mi></mrow><mn>3</mn></msup><mstyle><mi>Π</mi></mstyle></mrow></math></span> system of GeC molecule. The radiative transition parameters and effective vibrational temperature are evident that the possible presence of GeC molecule in solar and sunspots atmosphere. Further, these parameters are employed in rationalizations of astrochemical and astrophysical observations.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100859"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141849658","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 review of unsupervised learning in astronomy","authors":"S. Fotopoulou","doi":"10.1016/j.ascom.2024.100851","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100851","url":null,"abstract":"<div><p>This review summarises popular unsupervised learning methods, and gives an overview of their past, current, and future uses in astronomy. Unsupervised learning aims to organise the information content of a dataset, in such a way that knowledge can be extracted. Traditionally this has been achieved through dimensionality reduction techniques that aid the ranking of a dataset, for example through principal component analysis or by using auto-encoders, or simpler visualisation of a high dimensional space, for example through the use of a self organising map. Other desirable properties of unsupervised learning include the identification of clusters, <em>i.e.</em> groups of similar objects, which has traditionally been achieved by the k-means algorithm and more recently through density-based clustering such as HDBSCAN. More recently, complex frameworks have emerged, that chain together dimensionality reduction and clustering methods. However, no dataset is fully unknown. Thus, nowadays a lot of research has been directed towards self-supervised and semi-supervised methods that stand to gain from both supervised and unsupervised learning.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100851"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213133724000660/pdfft?md5=4f1896c41ddb28ebaf2391a955843baa&pid=1-s2.0-S2213133724000660-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141483677","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":"Estimation of orbital parameters from (u,v)-coverage for a space radio interferometer","authors":"I.I. Bulygin ,&nbsp;M.A. Shchurov ,&nbsp;A.G. Rudnitskiy","doi":"10.1016/j.ascom.2024.100855","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100855","url":null,"abstract":"<div><p>Searching for a suitable very long baseline (VLBI) interferometer geometry is a key task in planning observations, especially imaging sessions. VLBI image quality is characterized by <span><math><mrow><mo>(</mo><mi>u</mi><mo>,</mo><mi>v</mi><mo>)</mo></mrow></math></span>-coverage. With one or more radio telescopes located in space, such a task becomes more complex. This paper presents a method of recovering the optimal orbital parameters for space radio telescopes having a given desired <span><math><mrow><mo>(</mo><mi>u</mi><mo>,</mo><mi>v</mi><mo>)</mo></mrow></math></span>-coverage. In turn, this task can be called the inverse of the task of searching for the optimal geometry and orbital configurations of space-ground and pure space VLBI interferometers.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100855"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141483697","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":"LCGCT: A light curve generator in customisable-time-bin based on time-series database","authors":"Z. Zhang ,&nbsp;Y. Xu ,&nbsp;C. Cui ,&nbsp;D. Fan","doi":"10.1016/j.ascom.2024.100845","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100845","url":null,"abstract":"<div><p>In the era of time-domain astronomy, scientists often need to generate light curves with varying time-bin. However, an increase in time resolution typically leads to a substantial increase in data transmission. To enhance the data processing efficiency in time-domain astronomy, we propose a novel time-series data model for storing time-series observation data, and we construct the LCGCT, a tool designed to produce light curves with customisable time bins. To validate our approach, we utilise the 7-year MAXI/GSC (Gas Slit Camera of the Monitor of All-sky X-ray Image) X-ray source catalogue, incorporating its 24-h binned light curves for a comparative analysis with our approach. The results obtained confirm the accuracy and effectiveness of our proposed approach. Subsequently, we compare the storage capacity and query performance of LCGCT with a PostgreSQL-based implementation, and results show that LCGCT conserves 75% of the storage space and achieves three times the query speed. Owing to its noteworthy storage and query performance, our proposed time-series data model-based LCGCT can be used in time-domain astronomical projects with high time resolution.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100845"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141540251","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":"TensorFlow Hydrodynamics Analysis for Ly-α Simulations","authors":"J. Ding ,&nbsp;B. Horowitz ,&nbsp;Z. Lukić","doi":"10.1016/j.ascom.2024.100858","DOIUrl":"10.1016/j.ascom.2024.100858","url":null,"abstract":"<div><p>We introduce the Python program THALAS (TensorFlow Hydrodynamics Analysis for Lyman-Alpha Simulations), which maps baryon fields (baryon density, temperature, and velocity) to Ly<span><math><mi>α</mi></math></span> optical depth fields in both real space and redshift space. Unlike previous Ly<span><math><mi>α</mi></math></span> codes, THALAS is fully differentiable, enabling a wide variety of potential applications for general analysis of hydrodynamical simulations and cosmological inference. To demonstrate THALAS’s capabilities, we apply it to the Ly<span><math><mi>α</mi></math></span> forest inversion problem: given a Ly<span><math><mi>α</mi></math></span> optical depth field, we reconstruct the corresponding real-space dark matter density field. Such applications are relevant to both cosmological and three-dimensional tomographic analyses of Lyman Alpha forest data.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100858"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141839575","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":"pyC 2 Ray: A flexible and GPU-accelerated radiative transfer framework for simulating the cosmic epoch of reionization","authors":"P. Hirling ,&nbsp;M. Bianco ,&nbsp;S.K. Giri ,&nbsp;I.T. Iliev ,&nbsp;G. Mellema ,&nbsp;J.-P. Kneib","doi":"10.1016/j.ascom.2024.100861","DOIUrl":"10.1016/j.ascom.2024.100861","url":null,"abstract":"<div><p>Detailed modeling of the evolution of neutral hydrogen in the intergalactic medium during the Epoch of Reionization, <span><math><mrow><mn>5</mn><mo>≤</mo><mi>z</mi><mo>≤</mo><mn>20</mn></mrow></math></span>, is critical in interpreting the cosmological signals from current and upcoming 21-cm experiments such as the Low-Frequency Array (LOFAR) and the Square Kilometre Array (SKA). Numerical radiative transfer codes provide the most physically accurate models of the reionization process. However, they are computationally expensive as they must encompass enormous cosmological volumes while accurately capturing astrophysical processes occurring at small scales (<span><math><mrow><mo>≲</mo><mi>Mpc</mi></mrow></math></span>). Here, we present <span>pyC</span> <span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> <span>Ray</span>, an updated version of the massively parallel ray-tracing and chemistry code, <span>C</span> <span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> <span>-Ray</span>, which has been extensively employed in reionization simulations. The most time-consuming part of the code is calculating the hydrogen column density along the path of the ionizing photons. Here, we present the Accelerated Short-characteristics Octahedral ray-tracing (<span>ASORA</span>) method, a ray-tracing algorithm specifically designed to run on graphical processing units (GPUs). We include a modern <span>Python</span> interface, allowing easy and customized use of the code without compromising computational efficiency. We test <span>pyC</span> <span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> <span>Ray</span> on a series of standard ray-tracing tests and a complete cosmological simulation with volume size <span><math><msup><mrow><mrow><mo>(</mo><mn>349</mn><mspace></mspace><mi>Mpc</mi><mo>)</mo></mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span>, mesh size of <span><math><mrow><mn>25</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span> and approximately <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>6</mn></mrow></msup></mrow></math></span> sources. Compared to the original code, <span>pyC</span> <span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> <span>Ray</span> achieves the same results with negligible fractional differences, <span><math><mrow><mo>∼</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>5</mn></mrow></msup></mrow></math></span>, and a speedup factor of two orders of magnitude. Benchmark analysis shows that <span>ASORA</span> takes a few nanoseconds per source per voxel and scales linearly for an increasing number of sources and voxels within the ray-tracing radii.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100861"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934106","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":"Contribution of AI and deep learning in revolutionizing gravitational wave detection","authors":"Krishna Prajapati ,&nbsp;Snehal Jani ,&nbsp;Manisha Singh ,&nbsp;Ranjeet Brajpuriya","doi":"10.1016/j.ascom.2024.100856","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100856","url":null,"abstract":"<div><p>The fusion of cutting-edge computing techniques with physical detection of gravitational waves can be a potent solution for detecting and cleaning gravitational wave data, which further helps us in the identification of potential astrophysical sources. In this review article, we discuss the role of artificial intelligence approaches in the analysis of gravitational wave data. Below, we list both ground-based interferometers (like LIGO, VIRGO, etc.) and pulse timing arrays (like Parkes pulse timing array) as the current technologies used to find gravitational waves, along with their benefits and how they can be used to find different kinds of gravitational waves. We survey all four types of gravitational waves, each requiring a unique approach to both detection and data processing. We have extensively studied the use of deep learning techniques like convolutional neural networks, autoencoders, and LSTMs in the detection and parameter estimation of gravitational waves from various possible sources, including binary neutron star mergers and neutron star-black hole mergers, in detail. The review article also includes a thorough understanding of the noise and glitches in the real-time data of gravitational waves, as well as how the effective use of machine learning and deep learning techniques can be helpful in simulating waveforms and removing noise to quantify results.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100856"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141594495","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":"Characterization of ground-based telescope control systems: A systematic mapping study","authors":"S. Carrasco ,&nbsp;P. Galeas ,&nbsp;A. Cravero","doi":"10.1016/j.ascom.2024.100854","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100854","url":null,"abstract":"<div><p>Telescope operation is exceptionally complex, generally with respect to specialized control and monitoring systems. World-class astronomical facilities usually choose tailored control solutions to meet their specific needs. However, many of these telescopes share a common control architecture composed of a three-layer architecture: a top level for services and communication between software components, an intermediate level for coordination and execution of tasks in real-time, and a low level where the end hardware devices live. The first generations of telescopes also implemented centralized and customized solutions, which later evolved to highly decentralized components based on industrial standards, middleware, and open protocols. This paper reviews control and monitoring technologies used in modern world-class terrestrial observatories.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100854"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213133724000696/pdfft?md5=a2eb6a5bc16e444858e933a463f56886&pid=1-s2.0-S2213133724000696-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141486697","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":"State Space Modelling for detecting and characterising gravitational waves afterglows","authors":"D. d’Antonio ,&nbsp;M.E. Bell ,&nbsp;J.J. Brown ,&nbsp;C. Grazian","doi":"10.1016/j.ascom.2024.100860","DOIUrl":"10.1016/j.ascom.2024.100860","url":null,"abstract":"<div><p>We propose the usage of an innovative method for selecting transients and variables. These sources are detected at different wavelengths across the electromagnetic spectrum spanning from radio waves to gamma-rays. We focus on radio signals and use State Space Models, which are also referred to as Dynamic Linear Models. State Space Models (and more generally parametric auto-regressive models) have been the mainstay of economic modelling for some years, but rarely they have been used in Astrophysics.</p><p>The statistics currently used to identify radio variables and transients are not sophisticated enough to distinguish different types of variability. These methods simply report the overall modulation and significance of the variability, and the ordering of the data in time is insignificant. State Space Models are much more advanced and can encode not only the amount and significance of the variability but also properties, such as slope, rise or decline for a given time <span><math><mi>t</mi></math></span>.</p><p>In this work, we evaluate the effectiveness of State Space Models for transient and variable detection including classification in time-series astronomy. We also propose a method for detecting a transient source hosted in a variable active galaxy, whereby the time-series of a static host galaxy and the dynamic nature of the transient in the galaxy are intertwined. Furthermore, we examine the hypothetical scenario where the target transient we want to detect is the gravitational wave source GW170817 (or similar).</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100860"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213133724000751/pdfft?md5=293df2efed325a14984b8d05aad55f6f&pid=1-s2.0-S2213133724000751-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141853603","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":"Light curve classification with DistClassiPy: A new distance-based classifier","authors":"S. Chaini ,&nbsp;A. Mahabal ,&nbsp;A. Kembhavi ,&nbsp;F.B. Bianco","doi":"10.1016/j.ascom.2024.100850","DOIUrl":"https://doi.org/10.1016/j.ascom.2024.100850","url":null,"abstract":"<div><p>The rise of synoptic sky surveys has ushered in an era of big data in time-domain astronomy, making data science and machine learning essential tools for studying celestial objects. While tree-based models (<em>e.g.</em> Random Forests) and deep learning models dominate the field, we explore the use of different distance metrics to aid in the classification of astrophysical objects. We developed <span>DistClassiPy</span>, a new distance metric based classifier. The direct use of distance metrics is unexplored in time-domain astronomy, but distance-based methods can help make classification more interpretable and decrease computational costs. In particular, we applied <span>DistClassiPy</span> to classify light curves of variable stars, comparing the distances between objects of different classes. Using 18 distance metrics on a catalog of 6,000 variable stars across 10 classes, we demonstrate classification and dimensionality reduction. Our classifier meets state-of-the-art performance but has lower computational requirements and improved interpretability. Additionally, <span>DistClassiPy</span> can be tailored to specific objects by identifying the most effective distance metric for that classification. To facilitate broader applications within and beyond astronomy, we have made <span>DistClassiPy</span> open-source and available at <span>https://pypi.org/project/distclassipy/</span><svg><path></path></svg>.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100850"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141540252","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}