Speaker
Description
The reconstruction of primary vertices will become significantly more challenging with the High Luminosity Large Hadron Collider (HL-LHC) upgrade, as the number of simultaneous collisions, or pileup, is expected to reach up to $<\mu>=200$. This high pileup places substantial computational demands on conventional combinatorics-based algorithms, resulting in significantly increased latency and reduced accuracy. Machine learning offers a scalable and efficient alternative to address these challenges. This talk presents PV-Finder, a deep learning-based algorithm that uses reconstructed track parameters to directly predict primary vertex locations. The algorithm transforms track data into dense one-dimensional probability distributions, also referred to as kernel density estimations (KDEs), using a deep neural network. These KDEs serve as inputs to a convolutional neural network (CNN) to predict vertex position. We evaluate the performance of PV-Finder under Run 3 conditions ($<\mu>=60$) and benchmark it against other reconstruction algorithms, including the adaptive multi-vertex finder (AVMF) and earlier PV-Finder iterations. The results demonstrate its potential to improve vertex reconstruction efficiency and accuracy in high-pileup environments.
