在ALICE时间投影室中使用相关性和随机过程进行数据压缩
Data compression using correlations and stochastic processes in the ALICE Time Projection chamber
摘要：
本文介绍了对欧洲核子研究中心ALICE实验的时间投影室（TPC）探测器产生的数据进行在线无损和准无损压缩的算法。第一种算法基于无损源代码建模技术，即，原始TPC信号信息可以在解压缩阶段无错误地重建。源模型利用TPC数据中存在的时间相关性来减少源的熵。第二种算法基于有损源代码建模技术。为了评估由有损压缩引入的误差的后果，分析离线处理数据的轨迹跟踪算法的结果，特别是关于由压缩引入的噪声。离线分析分为两个步骤：聚类发现和跟踪发现。这些算法是如何受到有损压缩的结果报告。在这两种压缩技术中，熵编码被应用于由源模型定义的事件集合，以将比特率降低到对应的源熵。使用TPC模拟数据，无损和有损压缩实现了数据减少到原始数据速率的49.2%，并分别在35%到30%的范围内，取决于所需的precision.In这项研究中，我们专注于方法，这是很容易实现的前端TPC电子。
英文摘要：
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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