Data compression using correlations and stochastic processes in the ALICE Time Projection chamber

In this paper lossless and a quasi lossless algorithms for the online compression of the data generated by the Time Projection Chamber (TPC) detector of the ALICE experiment at CERN are described. The first algorithm is based on a lossless source code modelling technique, i.e. the original TPC signal information can be reconstructed without errors at the decompression stage. The source model exploits the temporal correlation that is present in the TPC data to reduce the entropy of the source. The second algorithm is based on a lossy source code modelling technique. In order to evaluate the consequences of the error introduced by the lossy compression, the results of the trajectory tracking algorithms that process data offline are analyzed, in particular, with respect to the noise introduced by the compression. The offline analysis has two steps: cluster finder and track finder. The results on how these algorithms are affected by the lossy compression are reported. In both compression technique entropy coding is applied to the set of events defined by the source model to reduce the bit rate to the corresponding source entropy. Using TPC simulated data, the lossless and the lossy compression achieve a data reduction to 49.2% of the original data rate and respectively in the range of 35% down to 30% depending on the desired precision.In this study we have focused on methods which are easy to implement in the frontend TPC electronics.