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. This paper 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, and cost constraints via ML. Possible related future research directions will be discussed.
Primary author
Andrew White
(U. Texas at Arlington)
