New generation high-energy physics experiments demand high precision tracking and accurate measurements of a large number of particles produced in the collisions of lementary particles and heavy-ions. Silicon-tungsten (Si-W) calorimeters provide the
most viable technological option to meet the requirements of particle detection in high multiplicity environments. We report a novel Si-W calorimeter design, which is optimized for $gamma/pi^0$ discrimination up to high momenta. In order to test the feasibility of the calorimeter, a prototype mini-tower was constructed using silicon pad detector arrays and tungsten layers. The performance of the mini-tower was tested using pion and electron beams at the CERN Proton Synchrotron (PS). The experimental results are compared with the results from a detailed GEANT-4 simulation. A linear relationship between the observed energy deposition and simulated response of the mini-tower has been obtained, in line with our expectations.
In high energy physics experiments, calorimetric data reconstruction requires a suitable clustering technique in order to obtain accurate information about the shower characteristics such as position of the shower and energy deposition. Fuzzy cluster
ing techniques have high potential in this regard, as they assign data points to more than one cluster,thereby acting as a tool to distinguish between overlapping clusters. Fuzzy c-means (FCM) is one such clustering technique that can be applied to calorimetric data reconstruction. However, it has a drawback: it cannot easily identify and distinguish clusters that are not uniformly spread. A version of the FCM algorithm called dynamic fuzzy c-means (dFCM) allows clusters to be generated and eliminated as required, with the ability to resolve non-uniformly distributed clusters. Both the FCM and dFCM algorithms have been studied and successfully applied to simulated data of a sampling tungsten-silicon calorimeter. It is seen that the FCM technique works reasonably well, and at the same time, the use of the dFCM technique improves the performance.
