# Machine Learning for Jet Physics

11-13 December 2017
Lawrence Berkeley National Laboratory
US/Pacific timezone

13 Dec 2017, 10:20
20m
2-100 (Lawrence Berkeley National Laboratory)

### Speaker

Kyle Cranmer (NYU)

### Description

We use an adversarial neural network to train a jet classifier that remains largely uncorrelated with the jet mass --- a nuisance parameter that is highly correlated with the observed features. This adversarial training strategy balances the dual objectives of classification accuracy and decorrelation, reducing the deleterious effect of systematic uncertainties in the background modeling. The result is a robust classifier with improved discovery significance relative to existing jet classification strategies.

 Slides