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

Authors : Veli ÇAPALI

Pages : 1240-1248

Doi:10.31202/ecjse.1132803

View : 23 | Download : 10

Publication Date : 2022-12-31

Article Type : Research Paper

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 : Nükleer bilimler, makine öğrenimi, yapay zeka, ileri nükleer tıp teknolojileri