Repository for the Computing Methods for Experimental Physics and Data Analysis course exam project.
Luca Callisti, Marco Carotta, Igor Di Tota
The main purpose of this project is to implement a lossy compression, using flow-based generative models. The realization is obtained using Affine Autoregressive Flows, an example of Normalizing Flows. As an application of Normalizing Flows, it was also shown how new data can be generated from the Gaussian distributions into which the original data are mapped.
The goal of this project was to implement a machine learning algorithm to analyze some HEP data. This gave us a chance to learn how to use GitHub and try writing documentation.
Also, since this work was inspired by the development of the Baler tool, which achieves lossy compression using an autoencoder, the datasets used are the same, so a comparison could be made.
In this project, compression with two different models was studied, so the files original_model1.ipynb and original_model2.ipynb were created.
The document Description_of_the_project_Callisti_Carotta_DiTota contains the more detailed explanation, along with the results obtained, and is available in this repo.