IRIS-HEP Fellow: Max Zhao



Fellowship dates: May – Aug, 2022

Home Institution: University of California, Berkeley


Project: Efficient implementation of algorithms to reconstruct charged particles trajectories

Kalman filter based algorithms are used for many applications in the track reconstruction process primarily for its noise reduction properties. This project will investigate the possibility of creating a machine learning algorithm that embeds the properties of a Kalman filter. We will explore various neural network architectures to test their efficacy in the context of current algorithms. Achieving higher performance through machine learning based Kalman filters would contribute to tracking software’s capability to handle expected increases in data flow for high energy experiments.

More information: My project proposal

Mentors:
  • Johannes Wagner (UC Berkeley, LBNL)

  • Heather Gray (UC Berkeley, LBNL)

Presentations and Publications

Current Status


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