Combining Hough Transform and Optimal Filtering for Efficient Cosmic Ray Detection with a Hadronic Calorimeter

Abstract

The hadronic calorimeter of ATLAS, TileCal, comprises a large number of readout channels (approximately 10,000), enabling the detection of tracks when cosmic muons traverse the detector. Muon track detection has been widely used during the TileCal commissioning phase for energy and timing calibration and remains important for background noise suppression during standard LHC operation. This work presents a cosmic ray detection algorithm based on TileCal data. The method employs the Hough Transform to map activated calorimeter cell data into a parameter space corresponding to straight-line tracks, where detection is efficiently performed. Due to the inherently low signal-to-noise ratio in cosmic ray detection with TileCal, a preprocessing stage based on a matched filter applied to time-sampled signals is introduced to enhance detection performance. Experimental results demonstrate that the proposed method outperforms previously used straight-line fitting approaches in TileCal.