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Journals / Turkish Journal of Astronomy and Astrophysics / 2026 / Cilt: 6 Sayı: 1

Classification of Eclipsing Binary Light Curves with Deep Learning Neural Network Algorithms

Derin Öğrenme Sinir Ağı Algoritmaları ile İkili Işık Eğrilerinin Sınıflandırılması

Burak Ulaş(ÇANAKKALE ONSEKİZ MART ÜNİVERSİTESİ)0000-0002-4624-3847
Pages
18–27
Publication Date
2025-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Our results show that the algorithm, which is selected among 132 neural network architectures, estimates the morphological classes of an independent validation dataset, 705 true data, with an accuracy of 92%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:We present an image classification algorithm utilising a deep learning convolutional neural network architecture, which categorises the morphologies of eclipsing binary systems based on their light curves.
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Key Findings & Data:The algorithm trains the machine with light curve images generated from the observational data of eclipsing binary stars in contact, detached and semi-detached morphologies, whose light curves are provided by Kepler, ASAS and CALEB catalogues.
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Conclusion & Impact:Our results show that the algorithm, which is selected among 132 neural network architectures, estimates the morphological classes of an independent validation dataset, 705 true data, with an accuracy of 92%.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:(stars:) binaries: eclipsing · methods: data analysis · techniques: image processing

Abstract

We present an image classification algorithm utilising a deep learning convolutional neural network architecture, which categorises the morphologies of eclipsing binary systems based on their light curves. The algorithm trains the machine with light curve images generated from the observational data of eclipsing binary stars in contact, detached and semi-detached morphologies, whose light curves are provided by Kepler, ASAS and CALEB catalogues. The structure of the architecture is explained, the parameters of the network layers and the resulting metrics are discussed. Our results show that the algorithm, which is selected among 132 neural network architectures, estimates the morphological classes of an independent validation dataset, 705 true data, with an accuracy of 92%.

Özet

Bu çalışmada, gözlemsel ışık eğrilerini kullanarak değen, ayrık ve yarı-ayrık yapıya sahip çift yıldız sistemlerini sınıflandıran bir görüntü sınıflandırma algoritması sunulmuştur. Algoritma, Kepler, ASAS ve CALEB kataloglarından alınan çift yıldız sistemlerine ait ışık eğrilerinden üretilen görüntülerle eğitilmiş bir derin öğrenme evrişimsel sinir ağı (Convolutional Neural Network, CNN) mimarisi temellidir. Modelin mimari yapısı açıklanmış, ağ katmanlarının parametreleri ve elde edilen performans metrikleri tartışılmıştır. Sonuçlar, 132 farklı sinir ağı mimarisi arasından seçilen algoritmanın, 705 gözlemsel veriden oluşan bağımsız bir doğrulama veri kümesi üzerinde yapısal sınıflandırmayı %92 doğrulukla tahmin edebildiğini göstermektedir.

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Cite This Article

Ulaş, B. (2025). Classification of Eclipsing Binary Light Curves with Deep Learning Neural Network Algorithms. Turkish Journal of Astronomy and Astrophysics, Cilt: 6 Sayı: 1, 18–27. https://doi.org/10.55064/tjaa.1708479

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