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MatteoD00/ProjectML_DielectronMass

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Regression Neural Network for e+e- invariant mass reconstruction

The aim of this project is to build a Shallow Neural Network model whose goal is to predict the invariant mass of e+e- pairs from the Kaggle dataset CERN Electron Collision Data generated by the CMS collaboration using a simulation of pp collision during the preliminary studies for the LHC experiment.

This notebook was created as an exam project for the course Machine Learning for Applied and High Energy Physics at the University of Turin. The main focus is not about the absolute performance but is a toy example used to test and improve skills about ML design.

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Simple NN model to analyse data from pp collision simulations evaluating masses of particles after e+e- decay

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