Infopaket

Scientific seminar on the specialty

ВБ 4.4

Вибіркова дисципліна для ОП

Second

2018/2019

4 Semester

3

Preparation of students for the presentation of master's theses for defense at the examination board and preparation of questions for the comprehensive exam.

Full-time form

1. Know the basic postulates of classical and relativistic mechanics and special theory of relativity; physical principles of operation of ionizing radiation detectors; characteristics of ionizing radiation; principle of operation of accelerators, algorithms and methods of experimental data processing. 2. Be able to present a master's thesis; clearly answer the questions submitted to the comprehensive state exam in physics; to orient in questions of modern physics. 3. Have an idea of physical phenomena and processes in subatomic structures,

formation of students' generalizations of high energy physics and nuclear physics through a set of knowledge close to the topics of master's research, to form approaches to the methodology of modern scientific and practical research, professional application of theoretical knowledge in professional activities, preparation for a comprehensive physics exam for masters.

1. P.A. Zyla et al. (Particle Data Group), Prog. Theor. Exp. Phys. 2020, 083C01 (2020) and 2021 update. Machine learning. 2. M. D. Schwartz. Modern Machine Learning and Particle Physics. /M. D. Schwartz //https://arxiv.org/abs/2103.12226v1 3. Antonia Creswell. Generative Adversarial Networks: An Overview. / A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, A. A Bharath // https://arxiv.org/abs/1710.07035v1, 10.1109/MSP.2017.2765202 4. A. Butter. Generative Networks for LHC events. / A.Butter and T. Plehn // https://arxiv.org/abs/2008.08558v1 5. F. Psihas. A Review on Machine Learning for Neutrino Experiments /F. Psihas, M. Groh, Ch. Tunnell, K. Warburton.// Int. J. Mod. Phys. A. 2020. Vol.35. I.33. -P. 2043005, 10.1142/S0217751X20430058 6. J. Shlomi. Graph neural networks in particle physics. /J. Shlomi, P. Battaglia, J.-R. Vlimant//Machine Learning: Science and Technology - 2020. -Vol. 2, -N.2. -P. 021001. 10.1088/2632-2153/abbf9a

Seminars - 30 hours. Independent work - 60 hours.

Semester assessment: (max / min) Survey during classes, preparation of reports on nanosystems physics - 60 points / 36 points The test is made in writing. The maximum number of points that can be obtained by a student during the test is 40. To obtain an overall positive grade in the discipline, the grade for the test may not be less than 24 points. A student is not admitted to the test if he / she scored less than 36 points during the semester.

Ukrainian