Makine öğrenmesi yöntemi ile dielektron çiftlerinin tanımlanması

Yıl 2022, , 349 - 358, 05.01.2022

Serpil Yalçın Kuzu

https://doi.org/10.25092/baunfbed.988684

Öz

Dielektronlar olarak adlandırılan elektron (e-) pozitron (e+) çiftleri, evrenin oluşumunu anlamak için yapılan yüksek enerjili parçacık çarpışma deneylerinin çeşitli süreçlerinde üretilen elektromanyetik sinyallerdir. Bu parçacık çiftleri, güçlü kuvvet etkileşimi yapmamaları sebebiyle bulundukları ortamın özelliklerinden etkilenmezler ve böylece çeşitli üretim mekanizmaları ile ilgili önemli bilgi sağlarlar. Dielektronları ölçmek için yüksek saflıkta çift sinyalleri gereklidir. Bu sinyalleri, kendisinden çok daha büyük olan arka plan (fon) kaynaklarından (e+e+, e-e-) ayırt etmek için karmaşık analiz teknikleri gereklidir. Geleneksel parçacık analiz yöntemleri ile dielektron çiftleri yüksek sistematik belirsizlikler ile üretilir. Son yıllarda çeşitli alanlardaki yapay zeka (AI) uygulamaları, insan çabalarının hızını, doğruluğunu ve verimliliğini artırmak için önem kazanmaktadır. Bu çalışmada dielektron analizinde yapay zeka tabanlı makine öğrenmesi yaklaşımı kullanılmıştır. Çalışmada rastgele orman (RO) sınıflandırma yöntemi Büyük Hadron Çarpıştırıcısı 2010 yılı verisinde bulunan dielektronların elde edilmesine uygulanmıştır. Yapılan çalışmada %90.9 duyarlılık ve %92.0 kesinlik ile RO metodu uygulanmış dielektron analizleri %98.2 başarı göstermiştir.

Anahtar Kelimeler

Dielektron, rastgele orman sınıflandırma yöntemi, makine öğrenimi.

Destekleyen Kurum

TÜBİTAK

Proje Numarası

TÜBİTAK-1001 119F302

Teşekkür

Bu çalışma Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) 119F302 numaralı proje ile desteklenmiştir. Ayben Karasu Uysal'a faydalı önerileri için özel teşekkür ederim.

Identification of dielectron pairs with machine learning method

Öz

Dielectrons, electron (e-) positron (e+) pairs, are electromagnetic signals produced in various processes of high-energy particle collision experiments to understand the formation of the universe. Since these particle pairs do not interact strongly, they are not affected by the features of their environment. Therefore, they provide significant information about various production mechanisms. High purity pair signals are needed to measure dielectrons. Complex analysis techniques are required to distinguish these signals from much larger background sources (e+e+, e-e-). With conventional particle analysis methods, dielectron pairs are produced with high systematic uncertainties. In recent years, artificial intelligence (AI) applications in various fields have gained importance to increase the speed, accuracy and efficiency of human labors. In this study, artificial intelligence-based machine learning approach was used in dielectron analysis. In the study, the random forest (RO) classification method was applied to obtain dielectrons in the Large Hadron Collider 2010 data. In the study, the RO method applied dielectron analysis showed 98.2% success with 90.9% efficiency and 92.0% precision.

Anahtar Kelimeler

Dielectron, random forest classifier, machine learning

Proje Numarası

TÜBİTAK-1001 119F302

Kaynakça

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