Learning to Ignore Uncertainties with Adversaries at the LHC

My project at ATLAS is focused on improving b-tagging using data intensive methods. This will involve retraining the standard basic b-tagging MVA algorithms with a focus on both weak and unsupervised learning. The results from the enhanced algorithm will be propagated to become the default ATLAS b-tagging tool, used by all analyses. Additionally, we will train a systematics aware ANN using data-corrected simulated samples. This will significantly reduce the impact of systematic uncertainties on the systematically limited H->bb analysis. The final goal of the project is to perform the world's most precise H->bb measurement.