Machine learning for particle physics using R, Budapest BI Forum, October 2016

Search strategies for new subatomic particles often depend on being able to efficiently discriminate between signal and background processes. Particle physics experiments are expensive, the competition between rival experiments is intense, and the stakes are high. This has lead to increased interest in advanced statistical methods to extend the discovery reach of experiments. This talk will present a walk-through of the development of a prototype machine learning classifier for differentiating between decays of quarks and gluons at experiments like those at the Large Hadron Collider at CERN. The power to discriminate between these two types of particle would have a huge impact on many searches for new physics at CERN and beyond. I will discuss why I chose to perform this analysis in R, how switching to R has helped my work and enabled me to adopt a more efficient reproducible research workflow, and how I have overcome the problems that I encountered when working with large datasets in R.

This is a talk that I gave at the?Budapest BI Forum in 2016, and follows the successful performance given the previous year at the same conference.

The slides for this talk can be found here.