Speaker
Mami Kuhara
Description
Pico-sec timing reconstruction is one of the hot topics of the detector development. We are working on timing reconstruction in calorimeters with utilizing hits as many as possible to be averaged. It needs precise tracking in the calorimeters to precisely calculate flight length inside the calorimeters. Since the tracks in the calorimeters are much more complicated than those in trackers, deep learning techniques such as graph neural network should be powerful. We will present the status of the current development. Our other efforts on application of deep learning for event reconstruction will be briefly discussed as well.
| 1st preferred time slot for your oral presentation | 15:30-17:30 JST (8:30-10:30 CEST, 2:30-4:30 EDT, 23:30-1:30 PDT) |
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| 2nd preferred time slot for your oral presentation | 19:00-21:00 JST (12:00-14:00 CEST, 6:00-8:00 EDT, 3:00-5:00 PDT) |
Primary authors
Kiyotomo Kawagoe
(Kyushu University)
Mami Kuhara
Taikan Suehara
(Kyushu University)
Shusaku Tsumura
(Kyushu University)
Tamaki Yoshioka
Tomoki Onoe
(Kyushu University)
