Speaker
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 |
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