Machine Learning Particle Flow

Particle Flow refers to the intricate process of identifying and reconstructing in- dividual particles generated during collision events. Reconstruction of the entire event is essential for measurement of particles, also the reconstruction of jets of particles originating from the fragmentation and hadronization of hard scatter- ing partons[1]. One of the most problematic aspect is to differentiate particles of various nature when they are close to or overlap each other. Utilizing Machine Learning (ML) techniques offers a range of advantages, including enhanced pat- tern recognition, effective feature extraction, event simulation refinement, and anomaly detection, among others. The first applications of Neural Networks date back to the 1980s [3]. In recent years many studies demonstrated con- temporary applications of Deep Learning (DL), in collider research have paved the way for accelerated processes and heightened algorithm performance. Mod- ern Machine Learning approaches, particularly those employing Graph Neural Networks (GNN), have found widespread utility across various tasks such as particle flow reconstruction, jet identification, and tackling the challenges posed by pileup interference. In recent years, GNNs have emerged as the defacto toolkit for graph-based data analysis and learning. Their application has signif- icantly advanced our capabilities in comprehending and extracting insights from intricate graph-structured data. The paper also discusses essential adjustments needed to pre-process a raw root file, ensuring its compatibility with the Ma- chine Learning model. It involves extracting critical information from the root file and transforming the data set into a structured format of nodes and edges. This format effectively captures particle details and hit types. Additionally, the paper covers the extraction of vital features and emphasizes the normalization of the data set to bring it to a consistent scale.