dorsal/arxiv
View SchemaData compression using correlations and stochastic processes in the ALICE Time Projection chamber
| Authors | M. Ivanov, A. Nicolaucig, A. Krechtchouk |
|---|---|
| Categories | |
| ArXiv ID | physics/0306133 |
| URL | https://arxiv.org/abs/physics/0306133 |
Abstract
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.
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"date_modified": "2026-03-02T18:00:46.810000Z",
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"abstract": "In this paper lossless and a quasi lossless algorithms for the online\ncompression of the data generated by the Time Projection Chamber (TPC) detector\nof the ALICE experiment at CERN are described. The first algorithm is based on\na lossless source code modelling technique, i.e. the original TPC signal\ninformation can be reconstructed without errors at the decompression stage. The\nsource model exploits the temporal correlation that is present in the TPC data\nto reduce the entropy of the source. The second algorithm is based on a lossy\nsource code modelling technique. In order to evaluate the consequences of the\nerror introduced by the lossy compression, the results of the trajectory\ntracking algorithms that process data offline are analyzed, in particular, with\nrespect to the noise introduced by the compression. The offline analysis has\ntwo steps: cluster finder and track finder. The results on how these algorithms\nare affected by the lossy compression are reported. In both compression\ntechnique entropy coding is applied to the set of events defined by the source\nmodel to reduce the bit rate to the corresponding source entropy. Using TPC\nsimulated data, the lossless and the lossy compression achieve a data reduction\nto 49.2% of the original data rate and respectively in the range of 35% down to\n30% depending on the desired precision.In this study we have focused on methods\nwhich are easy to implement in the frontend TPC electronics.",
"arxiv_id": "physics/0306133",
"authors": [
"M. Ivanov",
"A. Nicolaucig",
"A. Krechtchouk"
],
"categories": [
"physics.data-an",
"physics.ins-det"
],
"title": "Data compression using correlations and stochastic processes in the ALICE Time Projection chamber",
"url": "https://arxiv.org/abs/physics/0306133"
},
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