9-12 February 2021
Virtual Meeting
America/Toronto timezone

Machine Learning for Energy Reconstruction in ATLAS Calorimeters

12 Feb 2021, 13:00
Virtual Meeting

Virtual Meeting


Luke Polson (University of Victoria)


A crucial task of the ATLAS calorimeter is energy measurement of detected particles. In the liquid argon (LAr) calorimeter subdetector of ATLAS, electromagnetically and hadronically interacting particles are detected through LAr ionization. Special electronics convert drifting electrons into a measurable current. The analytical technique presently used to extract energy from the measured current is known as optimal filtering. While this technique is sufficient for contemporary pile-up conditions in the LHC, it has been shown to suffer some degradation of performance with the increased luminosity expected at the High Luminosity LHC. This presentation will explore machine learning techniques as a substitute for optimal filtering, examining the strengths, weaknesses, and limitations of both energy reconstruction methods.

Please select: Experiment or Theory Experiment
email address lpolson@uvic.ca

Primary author

Luke Polson (University of Victoria)

Presentation Materials

Your browser is out of date!

Update your browser to view this website correctly. Update my browser now