Astronomy and Computing最新文献_第7页

Vector to matrix representation for CNN networks for classifying astronomical data 用于天文数据分类的 CNN 网络的向量到矩阵表示法

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-08-15 DOI: 10.1016/j.ascom.2024.100864

Loris Nanni , Sheryl Brahnam

{"title":"Vector to matrix representation for CNN networks for classifying astronomical data","authors":"Loris Nanni , Sheryl Brahnam","doi":"10.1016/j.ascom.2024.100864","DOIUrl":"10.1016/j.ascom.2024.100864","url":null,"abstract":"<div><p>Choosing the right classifier is crucial for effective classification in various astronomical datasets aimed at pattern recognition. While the literature offers numerous solutions, the support vector machine (SVM) continues to be a preferred choice across many scientific fields due to its user-friendliness. In this study, we introduce a novel approach using convolutional neural networks (CNNs) as an alternative to SVMs. CNNs excel at handling image data, which is arranged in a grid pattern. Our research explores converting one-dimensional vector data into two-dimensional matrices so that CNNs pre-trained on large image datasets can be applied. We evaluate different methods to input data into standard CNNs by using two-dimensional feature vector formats. In this work, we propose a new method of data restructuring based on a set of wavelet transforms. The robustness of our approach is tested across two benchmark datasets/problems: brown dwarf identification and threshold crossing event (Kepler data) classification. The proposed ensembles produce promising results on both datasets. The MATLAB code of the proposed ensemble is available at <span><span>https://github.com/LorisNanni/Vector-to-matrix-representation-for-CNN-networks-for-classifying-astronomical-data</span><svg><path></path></svg></span></p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"49 ","pages":"Article 100864"},"PeriodicalIF":1.9,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213133724000799/pdfft?md5=6fb2d421f70e65fecee6f27c9d3b7ade&pid=1-s2.0-S2213133724000799-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077452","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}

引用次数: 0

Grouping and long term prediction of sunspot cycle characteristics-A fuzzy clustering approach 太阳黑子周期特征的分组和长期预测--一种模糊聚类方法

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-07-01 DOI: 10.1016/j.ascom.2024.100836

B.T. Anilkumar (Assistant Professor) , A Sabarinath (Scientist)

引用次数: 0

Predicting sunspot number from topological features in spectral images I: Machine learning approach 从光谱图像的拓扑特征预测太阳黑子数量 I：机器学习方法

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-07-01 DOI: 10.1016/j.ascom.2024.100857

D. Sierra-Porta , M. Tarazona-Alvarado , D.D. Herrera Acevedo

{"title":"Predicting sunspot number from topological features in spectral images I: Machine learning approach","authors":"D. Sierra-Porta , M. Tarazona-Alvarado , D.D. Herrera Acevedo","doi":"10.1016/j.ascom.2024.100857","DOIUrl":"10.1016/j.ascom.2024.100857","url":null,"abstract":"<div><p>This study presents an advanced machine learning approach to predict the number of sunspots using a comprehensive dataset derived from solar images provided by the Solar and Heliospheric Observatory (SOHO). The dataset encompasses various spectral bands, capturing the complex dynamics of solar activity and facilitating interdisciplinary analyses with other solar phenomena. We employed five machine learning models: Random Forest Regressor, Gradient Boosting Regressor, Extra Trees Regressor, Ada Boost Regressor, and Hist Gradient Boosting Regressor, to predict sunspot numbers. These models utilized four key heliospheric variables — Proton Density, Temperature, Bulk Flow Speed and Interplanetary Magnetic Field (IMF) — alongside 14 newly introduced topological variables. These topological features were extracted from solar images using different filters, including HMIIGR, HMIMAG, EIT171, EIT195, EIT284, and EIT304. In total, 60 models were constructed, both incorporating and excluding the topological variables. Our analysis reveals that models incorporating the topological variables achieved significantly higher accuracy, with the r2-score improving from approximately 0.30 to 0.93 on average. The Extra Trees Regressor (ET) emerged as the best-performing model, demonstrating superior predictive capabilities across all datasets. These results underscore the potential of combining machine learning models with additional topological features from spectral analysis, offering deeper insights into the complex dynamics of solar activity and enhancing the precision of sunspot number predictions. This approach provides a novel methodology for improving space weather forecasting and contributes to a more comprehensive understanding of solar-terrestrial interactions.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100857"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213133724000726/pdfft?md5=263e96a037564f7a5811a7559eb104fa&pid=1-s2.0-S2213133724000726-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141850824","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}

引用次数: 0

Thermodynamical analysis of Phantom AdS black holes 幻影 AdS 黑洞的热力学分析

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-07-01 DOI: 10.1016/j.ascom.2024.100862

Abdelhay Salah Mohamed , Euaggelos E. Zotos

引用次数: 0

Enhancement of the MATISSE tool for the geological analysis of planetary surfaces: A study on central pit craters on Mercury 增强行星表面地质分析 MATISSE 工具：对水星中央凹坑的研究

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-07-01 DOI: 10.1016/j.ascom.2024.100852

V. Camplone , A. Zinzi , M. Massironi , A.P. Rossi , F. Zucca

{"title":"Enhancement of the MATISSE tool for the geological analysis of planetary surfaces: A study on central pit craters on Mercury","authors":"V. Camplone , A. Zinzi , M. Massironi , A.P. Rossi , F. Zucca","doi":"10.1016/j.ascom.2024.100852","DOIUrl":"10.1016/j.ascom.2024.100852","url":null,"abstract":"<div><p>In this work we present the improved capabilities of MATISSE (Multi-purpose Advanced Tool for Instruments for the Solar System Exploration) tool which is now able to integrate geological maps and analyze specific data based on selected parameters (target, mission, instrument, geological units and area of interest). To demonstrate the effectiveness of this approach we focused on “central pit” craters on Mercury, with particular regard to the ones exposed in the Hokusai, Victoria, and Derain quadrangles.</p><p>The use of MATISSE for this application allowed us for an analysis of these morphologies, confirming a tendency for their location on volcanic terrains. The integrated research approach adopted in this study has proven to be a significant advantage in geological analysis, accelerating the process of data collection and interpretation. In conclusion, this study shows how the continuous evolution of scientific tools devoted to data handling and management based on FAIR principles, such as MATISSE, has the potential to open new perspectives in understanding planetary-scale geological processes.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"48 ","pages":"Article 100852"},"PeriodicalIF":1.9,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213133724000672/pdfft?md5=14329405866db02079ed860ee55a31b3&pid=1-s2.0-S2213133724000672-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141407497","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}

引用次数: 0

On the effective vibrational temperature of the source using (2)3∏ - X3∏ system of GeC molecule 利用 GeC 分子的 (2)3∏ - X3∏ 体系计算源的有效振动温度

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-07-01 DOI: 10.1016/j.ascom.2024.100859

R. Sindhan , N. Venkatesh Bharathi , S. Ramaswamy

{"title":"On the effective vibrational temperature of the source using (2)3∏ - X3∏ system of GeC molecule","authors":"R. Sindhan , N. Venkatesh Bharathi , 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}

引用次数: 0

A review of unsupervised learning in astronomy 天文学无监督学习综述

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-07-01 DOI: 10.1016/j.ascom.2024.100851

S. Fotopoulou

{"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}

引用次数: 0

Estimation of orbital parameters from (u,v)-coverage for a space radio interferometer 根据空间无线电干涉仪的（u,v）覆盖率估算轨道参数

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-07-01 DOI: 10.1016/j.ascom.2024.100855

I.I. Bulygin , M.A. Shchurov , A.G. Rudnitskiy

引用次数: 0

LCGCT: A light curve generator in customisable-time-bin based on time-series database LCGCT：基于时间序列数据库的可定制时间-bin 光曲线生成器

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-07-01 DOI: 10.1016/j.ascom.2024.100845

Z. Zhang , Y. Xu , C. Cui , D. Fan

{"title":"LCGCT: A light curve generator in customisable-time-bin based on time-series database","authors":"Z. Zhang , Y. Xu , C. Cui , 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}

引用次数: 0

TensorFlow Hydrodynamics Analysis for Ly-α Simulations 用于 Ly-α 模拟的 TensorFlow 流体动力学分析

IF 1.9 4区物理与天体物理

Astronomy and Computing Pub Date : 2024-07-01 DOI: 10.1016/j.ascom.2024.100858

J. Ding , B. Horowitz , Z. Lukić

引用次数: 0