Speakers
Description
The IDEA detector designed for the FCC-ee experiment has a cilindrical drift chamber as a main tracking device, surrounded by a spaghetti calorimeter exploiting a dual readout technology.
The fibers are positioned 1.5 mm far from each other giving to the detector an high granularity of about 50 sensitive element per cm2.
The Dual Readout technology, using scintillating and Cherenkov based fibers gives good particle identification properties to the system. In the present talk an attempt to use all the informations provided by the detector using a Machine Learning approach will be shown. The ML algorithm will complement classical reconstruction techniques in order to obtain the best performance in particle reconstruction and identification. The performances of the reconstruction for several particles will be reported.
Project funded by AIDAInnova Task 12.5.2
