TAG Based Skimming In ATLAS

Abstract

The ATLAS detector at the LHC takes data at 200–500 Hz for several months per year accumulating billions of events for hundreds of physics analyses. TAGs are event-level metadata allowing a quick search for interesting events based on selection criteria defined by the user. They are stored in a file-based format as well as in relational databases. The overall TAG system architecture encompasses a range of interconnected services that provide functionality for the required use cases such as event selection, display, extraction and skimming. Skimming can be used to navigate to any of the pre-TAG data products. The services described in this paper address use cases that range in scale from selecting a handful of interesting events for an analysis specific study to creating physics working group samples on the ATLAS production system. This paper will focus on the workflow aspects involved in creating pre and post TAG data products from a TAG selection using the Grid in the context of the overall TAG system architecture. The emphasis will be on the range of demands that the implemented use cases place on these workflows and on the infrastructure. The tradeoffs of various workflow strategies will be discussed including scalability issues and other concerns that occur when integrating with data management and production systems.