Speaker
Description
The upcoming silicon-based sampling calorimeters, such as the high-granularity calorimeter of the CMS experiment, will have unprecedented granularity in both the lateral and longitudinal dimensions. We expect these calorimeters to greatly benefit from machine learning-based reconstruction techniques. With the novel idea of interpreting the multiple sampling layers of calorimeters in the $\eta$ -- $\phi$ plane as colors in an RGB image. A convolutional neural network-based object detection framework, You Only Look Once, in short YOLO, was used for particle reconstruction in a fast (~1 ms on NVIDIA RTX 4090) and efficient manner. This study goes over the excellent performance of the model in reconstructing particles, e.g., muons, electrons/photons, and their direction in the $\eta$ -- $\phi$ plane, with excellent pileup rejection at 200 pileup interactions. The presentation also goes over the future perspectives of energy reconstruction with minimal modifications.
