Speaker
Description
This study explores a convolutional neural network (CNN) approach to classify events produced in high-energy collisions by the presence of heavy (charm and bottom), light (up, down, strange) and gluon jets, with the main characteristic being that jets are not reconstructed in our approach. The method constructs image-like representations based on the kinematics of charged decay products using detector-level variables, which allow CNNs to identify visual patterns characteristic of each jet type. As an ongoing step, we also study model sensitivity to jet properties in reconstructed jets with the intent to translate them to an event landscape that can increase our models performance. This approach not only demonstrates strong classification performance, highlighting the versatility of CNN architectures in jet tagging, but also reveals the abillity of AI methods to recognize structures that can be associated to flavor specific jets, even in the absence of jet reconstruction.