Search for New Resonance X->W Gamma and Standard Model W Gamma Production Using Deep Learning Techniques

The Standard Model is by far the most encompassing physics theory. With the recent discovery of the Higgs Boson, the Standard Model has performed extremely well when confronted with experimental data. However, the theory is intrinsically not complete. Extensions of the Standard Model predict new resonances decaying to a W boson and a photon. This thesis presents a search for such resonances produced in proton-proton collisions at 13 TeV using a dataset with an integrated luminosity of 36.1 fb−1 collected by the ATLAS detector at the Large Hadron Collider. The W bosons are identified through their hadronic decay channels. The data are found to be consistent with the expected background in the entire mass range investigated. Upper limits are set on the production cross section times decay branching ratio to W + gamma of new resonances with mass between 1.0 and 6.8 TeV.

The second part of this thesis looks at the extraction of the Standard Model W + gamma production. The main background noise for the extraction comes from the Standard Model production of gamma and jets. In order to streamline and improve the accuracy of the event filtering process, this thesis developed a deep neural net classifier to identify signal and background decay products and improved the signal to noise ratio.