IRIS-HEP Fellow: Layan AlSarayra

Fellowship dates: Jun – Aug, 2023

Home Institution: Stanford visiting scholar

Project: Adapting a Machine Learning Algorithm for Enhanced Performance in ACTS

The goal of this project is to adapt a machine learning algorithm for primary vertex identification within the ACTS framework. The primary vertex identification is a crucial step in High Energy Physics research, as it provides important information about particle trajectories and interactions. By enhancing the efficiency and accuracy of this identification process through machine learning, the project aims to improve the quality of data analysis and interpretation in HEP. The project involves generating Kernel Density Estimation (KDE) code and processing the output using the UNet/UNet++ Neural Network. The performance of the adapted algorithm will be evaluated, and the developed code and documentation will be made available for future contributions hoping to impact broader scientific fields that require tracking and vertex reconstruction beyond HEP.

More information: My project proposal

  • Lauren Tompkins (Stanford University)

  • Rocky Bala Garg (Stanford University)

Presentations and Publications

Current Status

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