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Project: B7

Title:          Investigation of machine learning techniques to uncover the Higgs CP nature in CMS

DESY group:     CMS

Type:           Online project

Duration:       19th july - 10th sep 2021

Description: 
The project consists in estimating the effect brought by the implementation of Machine Learning (ML) 
to one of the frontier analyses in High Energy Physics (HEP). In particular, following the main line of the 
recently published "Analysis of the CP structure of the Yukawa coupling between the Higgs boson and tau leptons" 
performed with the CMS experiment, the student will measure the effect ML techniques have on the sensitivity 
to the Higgs boson CP structure. ML is heavily used in the analysis to categorise signal events from the background ones, 
which makes it a perfect playground for the student to get acquainted with this topic. 
By comparing cut-based approach to a range of ML-based ones (Neural Networks, Boosted Decision Trees, linear models, etc.) 
in terms of their performance, the student will obtain a general overview of several ML techniques 
and will be able to carry such knowledge into their future studies.

Special Qualifications expected:
- Good level of programming in C++, Python and usage of the ROOT framework (required)
- Basic knowledge of the Unix environment, at least for what concerns working from terminal (required)
- Prior knowledge in ML tools like Scikit-learn, LightGBM/XGBoost, TensorFlow/Keras, 
  PyTorch (beneficial, but not required)


Link to further information: https://inspirehep.net/literature/1809624 
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