May 19 – 21, 2025
Pacific/Honolulu timezone

5-D calorimeter design issues with an integrated online/offline AI/ML approach

May 20, 2025, 10:15 AM
15m
Contributed talk (12'+3') Physics, Contributed

Speaker

Andrew White (U. Texas at Arlington)

Description

Many physics analyses use some form of AI/ML to identify physics objects such as jets and electrons and/or for whole event classification. However, such an approach has generally been taken a long time after the detector was designed and constructed. It is therefore relevant to question whether a proposed design of a future calorimeter is optimal for the application of AI/ML techniques and, if such techniques are also to be used onboard the detector, what are the potential benefits of an integrated online/offline AI/ML approach. This talk raises a number of relevant related questions in areas such as granularity vs. confusion, ML online/offline compatibility, ML and on-detector logic, ML and timing, ML-assisted PFA, and cost constraints via ML. The issues associated with an integrated ML comprehensive approach and possible related future research directions will be discussed.

Preferred session (remote speakers) Either

Author

Andrew White (U. Texas at Arlington)

Presentation materials