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Aplikace strojového učení pro Electron Ion Collider

  • Vedoucí práce / Supervisor: Jaroslav Adam, Ph.D.
  • Pracoviště / Workplace: KF FJFI
  • Kontakt / Contact: adamjaro@centrum.cz

Název anglicky / Title English: Machine learning application for the Electron Ion Collider

Osnova / Outline:
The Electron Ion Collider (EIC) is planned to address fundamental questions on internal structure of protons and nuclei as well as on many aspects of electromagnetic and nuclear forces [1]. One of key features of the EIC is its ability to detect electrons scattered at very small angles [2]. In such a case the detection and reconstruction requires the use of machine learning methods.

The thesis will focus on introduction to the methods, their testing in detector simulations [3] and potential further development. The course of the thesis will be coordinated with a dedicated working group within the EIC project. 

Plán práce:
Říjen/Listopad: General introduction to the EIC project and detector simulations
Listopad/Prosinec: Introduction to simulation tools, reproduction of several basic results
Prosinec/Leden: Writing down first parts of the thesis, talk at the winter workshop
Únor/Duben: Application of the machine learning to several scenarios, evaluation and potential development
Duben/Květen: Writing down the thesis

Literatura / reference:
[1] R. Abdul Khalek et al.: Science Requirements and Detector Concepts 
for the Electron-Ion Collider: EIC Yellow Report,
BNL-220990-2021-FORE, arXiv:2103.05419
[2] J. Adam: GETaLM: A generator for electron tagger and luminosity 
monitor for electron - proton and ion collisions,
Comput.Phys.Commun. 272 (2022) 108251, arXiv:2105.10570
[3] C. Grupen, B. Shwartz, Particle detectors, Cambridge University 
Press, 2011