A deep neural network method for analyzing the CMS HGCal events

For the High Luminosity LHC, the CMS Collaboration made the ambitious choice of a high granularity design to replace the existing endcap calorimeters. The thousands of particles coming from the multiple interactions create showers in the calorimeters, depositing energy simultaneously in adjacent cells. The data are analog to 3D gray-scale image that should be properly reconstructed. In this paper, we will investigate how to localize and identify the thousands of showers in such events with a Deep Neural Network model. This problem is well-known in the Vision domain, it belongs to the challenging class Object Detection. Our project presents a lot of similarities with the ones treated in Industry but accumulates several technological challenges like the 3D treatment. We will present the Mask R-CNN model which has already proven its efficiency in Industry (for 2D images). To conclude we will present the first results of this challenge and how we plan to extend it to tackle 3D HGCAL data.