5–9 Sept 2011
Europe/London timezone

Gibbs sampler for background discrimination in particle physics

6 Sept 2011, 16:10
25m
Parallel talk Track 2 : Data Analysis - Algorithms and Tools Tuesday 06th - Data Analysis – Algorithms and Tools

Speaker

Dr Federico Colecchia (University College London)

Description

Background properties in experimental particle physics are typically estimated from large collections of events. This usually provides precise knowledge of average background distributions, but inevitably hides fluctuations. To overcome this limitation, an approach based on statistical mixture model decomposition is presented. Events are treated as heterogeneous populations comprising particles originating from different processes, and individual particles are mapped to a process of interest on a probabilistic basis. When used to discriminate against background, the proposed technique based on the Gibbs sampler allows some features of the background distributions to be estimated directly from the data without training on high-statistics samples. A feasibility study on Monte Carlo is presented, together with a comparison with existing techniques. Finally, the prospects for the development of the Gibbs sampler into a tool for intensive offline analysis of interesting events at the Large Hadron Collider are discussed.

Primary author

Dr Federico Colecchia (University College London)

Presentation materials

Peer reviewing

Paper