To improve precision measurements of the Standard Model and to search for New Physics, our group also develops new analysis methods. We explore new ideas in statistical methods, machine learning and electronics in order to maximize our measurement and discovery potential as well as minimize our systematic uncertainties.
Quark/Gluon tagging on trigger jets
The analysis of dijet events can give us important information about new possible models beyond the Standard Model. In some models the massive mediator can be observed as an excess in the invariant mass spectrum of the decay product. We therefore study how to tag quark vs. gluon jets. To do this, we use boosted decision trees and other machine learning methods.
Autoencoders and Higgs to Invisible signatures
The search for Higgs decays to invisible particles is powerful search for New Physics, since the Standard Model prediction for this process is close to zero. Modern machine learning such as autoencoders offer attractive solutions in enhancing Higgs to invisible signatures.
Image Source: CERN
For many physics measurements in ATLAS, precise knowledge of the jet energy scale (JES) and its uncertainty is important. One way to validate the JES correction and to determine the uncertainty, is the jet pseudorapidity intercalibration. We developed a new method to select an increased statistical data sample of dijet events.