A novel layered reconstruction framework for longitudinal segmented electromagnetic calorimeter

In future high-energy physics experiments, the electromagnetic calorimeter (ECAL) will operate in exceptionally high-luminosity. An ECAL featuring layered readout in the longitudinal direction and precise time-stamped information offers a multi-dimensional view, enriching our comprehension of the showering process of electromagnetic particles in high-luminosity environments. And it is taken as the baseline design for several new experiments, including the planned upgrades of the current running experiments. Reconstructing and matching the multi-dimensional information across different layers poses new challenges in utilizing layered data effectively. This work introduces a novel layered reconstruction framework for the ECAL with a layered readout information structure and develops the layered clustering algorithm. It expands the concept of clusters from planes to multiple layers. Additionally, this work presents the corresponding layered cluster correction methods, investigates the transverse shower profile, which is utilized for overlapping clusters splitting, and develops the layered merged $π^0$ reconstruction algorithm based on this framework. By incorporating energy and time information in 3-dimension, this framework provides a suitable software platform for the preliminary research of longitudinal segmented ECAL and new perspectives in physics analysis.