EXOLIFE: Makine Öğrenmesi Kullanarak Ötegezegenlerin Tespit Edilmesi ve Yaşanabilirlik Tahmini Yapılması

Year 2024, Volume: 6 Issue: 2, 85 - 96

Eren Yılmaz , Muhammet Enes Artan , Ahmet Bilal Yanartaş

https://doi.org/10.51489/tuzal.1554248

Abstract

Ötegezegenler, günümüzde astronomi alanında en çok çalışılan konular arasında yer almaktadır. Farklı türlerde oluşan ötegezegenlerin tespiti için çeşitli yöntemler geliştirilmiş ve bu sayede saptama mümkün hale gelmiştir. Bu araştırmada, ötegezegen tespiti için kullanılan uzaktan algılama ve makine öğrenmesi yöntemleri, algoritmalarla süreci hızlandırmaktadır. Projede, XGBoost, Rastgele Orman, Çok Katmanlı Algılayıcı, K-En Yakın Komşu, Lojistik Regresyon ve Destek Vektör Sınıflandırıcısı modelleri eğitilmiş ve hem yaşanılabilirlik hem de ötegezegen tespiti için karşılaştırmalar yapılmıştır. NASA verileriyle eğitilen bu makine öğrenmesi sistemi, Python yazılım diliyle oluşturulmuştur. Çalışma, “Ötegezegenlerin tespiti ve yaşanılabilirlik ölçütü kapsamında değerlendirilmesi makine öğrenmesi ile yüksek doğruluk oranlarına çıkarılabilir.” hipotezine dayanarak Dünya benzeri ötegezegenleri bulmayı hedeflemiştir. Sonuçlarda, yaşanılabilirlik saptamasında %97.46 doğruluk oranı ile XGBoost algoritması en başarılı model olarak öne çıkmıştır. Gezegen tespitinde de %96’lık doğruluk oranıyla XGBoost, en başarılı model olmuştur. Araştırma, yüksek başarı oranıyla astronomi/astrofizik literatürüne önemli katkılar sağlamıştır. Ayrıca, çalışmanın sonucunda bir Grafiksel Kullanıcı Arayüzü (GUI) oluşturulmuş ve test edilen modeller işlevsel hale getirilmiştir.

Keywords

Ötegezegen, NASA, Makine Öğrenmesi, XGBoost, Biyoimza

Ethical Statement

Yapılan çalışmada yazarlar, araştırma ve yayın etiğine aykırı bir durum olmadığını ve çalışmanın etik kurul izni gerektirmediğini beyan etmektedir.

Supporting Institution

Bu çalışma TUBITAK 2204-A Bilimsel Araştırma Programı Tarafından Desteklenmiştir..

EXOLIFE: Detection and Habitability Estimation of Exoplanets Using Machine Learning Techniques

Abstract

Exoplanets are among the most studied and remarkable topics in astronomy. Over the years, various methods have emerged for exoplanet detection, allowing for the identification of numerous exoplanet types. In this context, remote sensing and machine learning, which are central to our research, have significantly accelerated the detection process by leveraging algorithms. Our study involved training several machine learning models, including XGBoost, Random Forest, Multilayer Perceptron, K-Nearest Neighbor, Logistic Regression, and Support Vector Classifier, to compare their performance in both habitability assessment and exoplanet detection. The research utilized machine learning models trained on space observation data obtained from NASA, with the Python programming language serving as the foundation for the system's infrastructure. Our hypothesis was that "The detection of exoplanets and their evaluation within the scope of the habitability criterion can be increased to high accuracy rates with machine learning." Unlike merely detecting exoplanets, this study specifically aimed to identify Earth-like exoplanets. The XGBoost algorithm emerged as the most successful model in determining habitability, achieving an accuracy rate of 97.46% and demonstrating high precision and sensitivity. For exoplanet detection, all models achieved a main test accuracy rate of 96%; however, when considering sensitivity and precision, XGBoost was again the most effective. This research, following the synthesis and analysis of these two parameters, achieved a very high success rate compared to previous studies and made a significant contribution to the astronomy/astrophysics literature. Additionally, a Graphical User Interface (GUI) was developed, making the tested models functional through an application. The study successfully reached its goal of contributing important findings to the field.

Keywords

Exoplanet, NASA, Machine Learning, XGBoost, Biosignature

Ethical Statement

In the study, the author/s declare that there is no violation of research and publication ethics and that the study does not require ethics committee approval.

Supporting Institution

This study was supported by TUBITAK 2204-A Scientific Research Program.

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