Speaker
Yongbin Feng
(Texas Tech University)
Description
Hadron calorimeters are central to jet energy measurement, particle identification, and overall physics sensitivity at modern collider experiments, including CEPC and FCC. Over the past two decades, substantial progress has been made in both high-granularity and dual-readout calorimetry. Building on these advances, AI/ML techniques combined with precision timing information offer significant potential to further improve calorimeter performance.
In this talk, I will present AI/ML-based reconstruction studies for high-granularity dual-readout calorimeters. I will also report results from beam tests of a high-granularity dual-readout calorimeter prototype at the CERN H8 beamline, focusing on energy calibration and timing performance.
Primary author
Yongbin Feng
(Texas Tech University)
