Yapay Sinir Ağları ve Makine Öğrenme Yöntemlerinin Nükleer Fizik Uygulamaları

Abstract

Yapay zekâ ve makine öğrenimi yöntemlerindeki ilerlemeler, bilimsel araştırmalarda geniş uygulanabilirliği olan araçlar sağlamışlardır. Bu teknikler, nükleer teori, deneysel yöntemler, hızlandırıcı teknoloji ve nükleer verilerdeki konuları kapsayan çeşitli alanlarda uygulanmakta ve bilimsel keşifleri ve toplumsal uygulamaları kolaylaştıracak ilerlemeleri sağlamaktadır. Deneysel verilerin analizi ve nükleer sistemlerin teorik olarak modellemesi, fiziğin tüm alanlarında olduğu gibi, korelasyonlara dayalı tahmin yapmak ve etkileşimleri sağlamak amaçlar. Deneysel çalışmalar, her biri benzersiz operasyon, veri toplama ve analiz yöntemlerine sahip dünya çapında birçok laboratuvarı kullanır. Benzer şekilde, teorik nükleer fizikte yayılan odak ölçekleri, algoritma yöntemleri ve belirsizlik ölçümü için geniş ihtiyaçlara yol açar. Boyut ve enerji ölçeklerinde veri türleri dizilerini kullanan bu teorik çalışmalar, YSA/ML yöntemlerinin uygulamaları için mükemmel bir ortam yaratır. Ayrıca, bu yöntemlerin son on yılda daha pratik hale gelmesiyle, nükleer bilim ve teknolojide öğrenmeye dayalı yöntemlerin popülaritesinin artacağı öngörülmekte; sonuç olarak, bu tür metodolojileri uygulamanın yararlarını ve engellerini anlamak, daha iyi araştırma planları oluşturmaya ve proje risklerini ve fırsatlarını belirlemeye yardımcı olabilir. Bu çalışma, yapay zekâ ve makine öğrenmesi teknikleri ile yapılmış nükleer fizik araştırmaları ve nükleer tıp teknolojileri hakkında bilgi vermektedir.

Keywords

• Nükleer bilimler
• makine öğrenimi
• yapay zeka
• ileri nükleer tıp teknolojileri

Applications of Artificial Neural Networks and Machine Learning Methods in Nuclear Physics

Abstract

Advances in artificial intelligence/machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of covering topics in nuclear theory, experimental methods, accelerator technology, and nuclear data, leading to advances that will facilitate scientific discoveries and societal applications. The analysis of experimental data and the theoretical modeling of nuclear systems aims, as is the case in all fields of physics, at uncovering the basic laws of motion in order to make predictions and estimations, as well as finding correlations and causations for the strongly interacting matter. Experimental efforts utilize many laboratories worldwide, each with unique operation, data acquisition, and analysis methods. Similarly, the scales of focus spanned in theoretical nuclear physics lead to broad needs for algorithmic methods and uncertainty quantification. These efforts, utilizing arrays of data types across size and energy scales, create a perfect environment for applications of ANN/ML methods. Furthermore, as these methods have become more practical during the past decade, it is foreseen that the popularity of learning-based methods in nuclear science and technology will increase; consequently, understanding the benefits and barriers of implementing such methodologies can help create better research plans, and identify project risks and opportunities. This study gives information on nuclear physics research and nuclear medical technologies that have been done by artificial intelligence and machine learning techniques.

Keywords

• Nuclear science
• machine learning
• artificial intelligence
• advanced nuclear medical technologies

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