基于深度学习的双星参数预测方法

IF 2.7 3区 物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS
Islam Helmy, Mohamed Ismail, Doaa Eid
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摘要

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Deep learning-based prediction approaches of binary star parameters

The precise computation of binary star parameters is crucial for understanding their formation, evolution, and dynamics. However, large datasets of available astronomical measurements require substantial effort for computing using classic astronomical methods. Deep learning (DL) is a promising approach that can provide a proper solution for estimating the parameters and reducing the burden of the lengthy procedures of astronomical computations. This study proposes two DL-based models for estimating binary star parameters. The first is the well-known multi-layer perceptron (MLP) model, whereas the second is based on long short-term memory (LSTM). We rely on databases, such as large sky multi-object fiber spectroscopic telescope area (LAMOST), to train the proposed models. In addition, we verify the training ratio showing that the performance of both models at a low training ratio of \(30\%\), based on the mean square error (MSE), results in acceptable performance. Furthermore, the LSTM-based DL model outperforms the conventional MLP for different training ratios. Eventually, the two models have superiority compared to the benchmark methods.

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来源期刊
Experimental Astronomy
Experimental Astronomy 地学天文-天文与天体物理
CiteScore
5.30
自引率
3.30%
发文量
57
审稿时长
6-12 weeks
期刊介绍: Many new instruments for observing astronomical objects at a variety of wavelengths have been and are continually being developed. Furthermore, a vast amount of effort is being put into the development of new techniques for data analysis in order to cope with great streams of data collected by these instruments. Experimental Astronomy acts as a medium for the publication of papers of contemporary scientific interest on astrophysical instrumentation and methods necessary for the conduct of astronomy at all wavelength fields. Experimental Astronomy publishes full-length articles, research letters and reviews on developments in detection techniques, instruments, and data analysis and image processing techniques. Occasional special issues are published, giving an in-depth presentation of the instrumentation and/or analysis connected with specific projects, such as satellite experiments or ground-based telescopes, or of specialized techniques.
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