Abstract
In high energy physics experiments data quality plays a significant role for particle identification. Methods used in particle analysis are mainly based on high level knowledge and complex computation skills of human experts and require long time for data quality assurance. Artificial intelligence (AI) applications in various fields are getting important to improve the speed, accuracy and efficiency of human efforts. For this purpose, artificial intelligence-based machine learning approach can be used in particle physics analysis. Dielectrons (e-e+) are electromagnetic probes that provide information about evolution of the medium formed in high energy collisions due to lack of final state interactions. A high purity sample of e-e+ pairs can be obtained by traditional cut-based methods resulting in low efficiency. In this contribution, application of machine learning approaches in dielectron analysis is discussed.
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