The DAQ system of the 12,000 Channel CMS High Granularity Calorimeter Prototype

TL;DR

This paper presents a scalable data acquisition system for a high-channel-count silicon calorimeter prototype tested with high-energy particle beams at CERN, designed to support the upgraded CMS detector at HL-LHC.

Contribution

It introduces a cost-effective, scalable DAQ system utilizing FPGA mezzanines and Raspberry Pi computers for high-channel silicon calorimeter prototypes.

Findings

Successful operation with approximately 12,000 channels

Effective data collection during beam tests with electrons, pions, and muons

Demonstrated scalability and cost-efficiency of the DAQ system

Abstract

The CMS experiment at the CERN LHC will be upgraded to accommodate the 5-fold increase in the instantaneous luminosity expected at the High-Luminosity LHC (HL-LHC). Concomitant with this increase will be an increase in the number of interactions in each bunch crossing and a significant increase in the total ionising dose and fluence. One part of this upgrade is the replacement of the current endcap calorimeters with a high granularity sampling calorimeter equipped with silicon sensors, designed to manage the high collision rates. As part of the development of this calorimeter, a series of beam tests have been conducted with different sampling configurations using prototype segmented silicon detectors. In the most recent of these tests, conducted in late 2018 at the CERN SPS, the performance of a prototype calorimeter equipped with ${\approx}12,000\rm{~channels}$ of silicon sensors was studied with beams of high-energy electrons, pions and muons. This paper describes the custom-built scalable data acquisition system that was built with readily available FPGA mezzanines and low-cost Raspberry PI computers.