Performance of the distributed central analysis in BaBar

Abstract

The total dataset produced by the BaBar experiment at the Stanford Linear Accelerator Center (SLAC) currently comprises roughly$3times 10^9$data events and an equal amount of simulated events, corresponding to 23 Tbytes of real data and 51 Tbytes simulated events. Since individual analyses typically select a very small fraction of all events, it would be extremely inefficient if each analysis had to process the full dataset. A first, centrally managed analysis step is therefore a common pre-selection (‘skimming’) of all data according to very loose, inclusive criteria to facilitate data access for later analysis. Usually, there are common selection criteria for several analysis. However, they may change over time, e.g., when new analyses are developed. Currently,$cal O