Machine Learning Applications in Astronomy

Abstract

With the developing technology, the variety and capacity of data collection tools in astronomy have also improved. With the increase in the amount of data collected, the number of data mining, big data applications, machine learning and artificial intelligence applications in this field is increasing day by day. The most important part in the calculations in astronomy is revealing and evaluating the structure of the data. Machine learning stands out in these calculations and has an important application area nowadays. The most common supervised and unsupervised machine learning methods used in astronomy are Support Vector Machines, Random Forests and also Artificial Neural Network for supervised learning and Self-Classifying/Organizing Map, Principal Component Analysis for unsupervised learning methods, respectively. Different machine learning methods find applications in more than one subsections, from the classification of celestial objects, to the evaluation of their observational properties, and to the evaluation of their models. Striking applications among these are; classification of galaxies, determination of variable stars, solar physics researches, exoplanet discoveries, and revealing of stellar parameters and modelling of stellar interior structure and evolution. This study presents a compilation of recent articles in astronomy in the last five years to create a Turkish material in astroinformatics and astrostatistics.

Keywords

• methods: statistical
• methods:data analysis
• software: data analysis

Astronomi Alanında Makine Öğrenmesi Uygulamaları

Abstract

Gelişen teknoloji ile birlikte astronomi alanında veri toplama araçlarının çeşitliliği ve kapasitesi de gelişti. Toplanan veri miktarının artması ile birlikte bu alandaki veri madenciliği, büyük veri uygulamaları, makine öğrenmesi ve yapay zeka uygulamalarının sayısı her geçen gün artıyor. Astronomi alanındaki hesaplamalarda da en önemli kısım verinin yapısının ortaya çıkarılması ve değerlendirilmesidir. Makine öğrenmesi günümüzde bu hesaplamalarda ön plana çıkarak önemli bir uygulama alanı bulunuyor. Bu alanda kullanılan en yaygın makine öğrenmesi yöntemleri denetimli öğrenmede Destek Vektör Makineleri (Support Vector Machines), Rastgele Orman (Random Forests) ve Yapay Sinir Ağları(Artificial Neural Network) iken denetimsiz öğrenmede Kendi Kendine Sınıflandırma/Düzenleme Haritası (Self-Classifying/Organizing Map), Temel Bileşen Çözümlemesi (Pricipal Component Analysis) ’dir. Birbirinden farklı makine öğrenmesi yöntemleri gökcisimlerinin sınıflandırılmasından, gözlemsel özelliklerinin değerlendirilmesine ve modellerle uyum değerlendirmesine kadar birden fazla alt başlıkta uygulama buluyor. Bunlar arasında ön plana çıkan çarpıcı örnekler; gökadaların sınıflandırılması, güneş fiziği araştırmaları , değişen yıldız türlerinin belirlenmesi, yeni gezegen keşifleri ve yıldızların temel parametrelerinin belirlenmesiyle yıldız iç yapı ve evrimlerinin ortaya çıkarılması ve modellenmesi üzerinedir. Bu çalışma astroenformatik ve astroistatistik alanında Türkçe kaynak oluşturmak adına son beş yıl içerisinde astronomi alanında güncel yazılmış makalelerden bir derleme sunmaktadır.

Keywords

• astronomi
• veri analizi
• makine öğrenmesi

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