Physics – Data Analysis – Statistics and Probability
Scientific paper
2010-10-28
Physics
Data Analysis, Statistics and Probability
Substantial changes with respect to the previous version of the algorithm in order to overcome limitations observed in v3; the
Scientific paper
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 features of the background distributions to be estimated directly from the data without training on high-statistics control 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.
No associations
LandOfFree
Gibbs sampler for background discrimination in particle physics does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Gibbs sampler for background discrimination in particle physics, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Gibbs sampler for background discrimination in particle physics will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-584270