UC Irvine

We demonstrate techniques to improve the performance of data driven methods used in collider experiments, through the use of neural networks. First, using a simulated muon dataset, we probe the discriminating power of the typically used isolation observable by comparing it to neural networks trained on full event details. By performing a search over the space of Energy Flow Polynomials (EFPs), a set of scalar observables which performs similarly to the full information is identified. This methodology is then applied to real collider data obtained from CMS Open Data. The CMS data lacks event level class labels, necessitating the use of CWoLa, a weakly supervised training method, along with an sPlotsbased performance evaluation method. Once again, we successfully identify a minimal set of scalar observables capable of outperforming isolation. Finally, a novel data-augmentation scheme is introduced. Symmetries present in an ideal dataset may be broken by detector effects, leading to lower quality augmented copies. We perform augmentation pre-detection in simulation, and further encourage invariance across augmented copies during training. We find that synthesizing examples this way leads to faster convergence, and that encouraging invariance yields further performance gains.