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7–9 Aug 2024
Fermilab - Wilson Hall
US/Central timezone

Muon beam-induced background mitigation with neuromorphic AI

Not scheduled
20m
Ramsey Auditorium (Fermilab - Wilson Hall)

Ramsey Auditorium

Fermilab - Wilson Hall

Fermi National Accelerator Laboratory Batavia, IL

Speaker

Micah Hillman (University of Tennessee, Knoxville)

Description

In any muon collider detector, abundant beam-induced backgrounds will challenge reconstruction algorithms. Cell-specific minimum-energy thresholds have shown some effectiveness in reducing diffuse, low-energy BIB contributions during digitization in a simulated 10TeV muon collider detector, but further research is needed to optimize these methods. This project aims to explore the use of timing-sensitive energy thresholding by neuromorphic computing algorithms to mitigate beam-induced backgrounds in electromagnetic calorimeter (ECAL) readout. The performance of several other thresholding methods will be examined for comparison.

Primary authors

Alexander Tuna (Harvard University) Lawrence Lee (University of Tennessee, Knoxville) Micah Hillman (University of Tennessee, Knoxville) Catherine D. Schuman (University of Tennessee, Knoxville) Tova Holmes (University of Tennessee, Knoxville)

Presentation materials

There are no materials yet.