A novel reconstruction framework for an imaging calorimeter for HL-LHC

To sustain the harsher conditions of high luminosity LHC in 2026, the CMS experiment has designed a novel endcap calorimeter that uses silicon sensors to achieve sufficient radiation tolerance, with the additional benefit of a very high readout granularity. In regions characterised by lower radiation levels, small scintillator tiles with individual SiPM readout are employed. A novel reconstruction approach is being developed to fully exploit the granularity and other significant features of the detector such as precision timing to mitigate high pileup rate at the HL-LHC. An iterative reconstruction framework (TICL) has been put in place, and is being actively developed. The inputs to the framework are clusters of energy deposited in individual calorimeter layers delivered by a density-based algorithm which has recently been developed and tuned. In view of the expected pressure on the computing capacity in the HL-LHC era, the algorithms and their data structure are being designed with GPUs in mind. Preliminary results show that significant speed-up can be obtained running the clustering algorithms on GPUs. Moreover, machine learning (ML) techniques are being investigated and integrated into the reconstruction framework. This talk will describe the approaches being considered and show selected first results.