Real time event selection at LHC with Spark and Deep Learning
This project focuses on building a machine learning pipeline for a high-energy physics particle classifier using Apache Spark, ROOT, Parquet, TensorFlow, and Jupyter with Python notebooks.
The training of DL models has yielded satisfactory results that align with the findings of the original research paper. The performance of the models can be evaluated through various metrics, including loss convergence, ROC curves, and AUC (Area Under the Curve) analysis.
By achieving results consistent with the original research paper, we validate the effectiveness of our DL models and the reliability of our implementation. These results contribute to advancing the field of high-energy physics and event classification at the LHC (Large Hadron Collider).
