Measurement of ttbar production differential cross section at \sqrt{s}=8 TeV at ATLAS

Top quark studies play an important role in the physics program of the Large Hadron Collider (LHC). The energy and luminosity reached allow the acquisition of a large amount of data especially in kinematic regions never studied before. In this thesis is presented the measurement of the ttbar production differential cross section on data collected by ATLAS in 2012 in proton proton collisions at \sqrt{s} = 8 TeV, corresponding to an integrated luminosity of 20.3 fb^{−1}. The measurement is performed for ttbar events in the semileptonic channel where the hadronically decaying top quark has a transverse momentum above 300 GeV. The hadronic top quark decay is reconstructed as a single large radius jet and identified using jet substructure properties. The final differential cross section result has been compared with several theoretical distributions obtaining a discrepancy of about the 25% between data and predictions, depending on the MC generator. Furthermore the kinematic distributions of the ttbar production process are very sensitive to the choice of the parton distribution function (PDF) set used in the simulations and could provide constraints on gluons PDF. In particular in this thesis is performed a systematic study on the PDF of the protons, varying several PDF sets and checking which one better describes the experimental distributions. The boosted techniques applied in this measurement will be fundamental in the next data taking at \sqrt{s}=13 TeV when will be produced a large amount of heavy particles with high momentum.

A deep learning model for controlling the galvanizing process in a production line

In the industry of steelmaking, the process of galvanizing is a treatment which is applied to protect the steel from corrosion. The air knife effect (AKE) occurs when nozzles emit a steam of air on the surfaces of a steel strip to remove excess zinc from it. In our work we formalized the problem to control the AKE and we implemented, with the R&D dept.of MarcegagliaSPA, a DL model able to drive the AKE. We call it controller. It takes as input the tuple : a tuple of the physical conditions of the process line (t,h,s) with the target value of the zinc coating (c); and generates the expected tuple of (pres and dist) to drive the mechanical nozzles towards the (c). According to the requirements we designed the structure of the network. We collected and explored the data set of the historical data of the smart factory. Finally, we designed the loss function as sum of three components: the minimization between the coating addressed by the network and the target value we want to reach; and two weighted minimization components for both pressure and distance. In our solution we construct a second module, named coating net, to predict the coating of zinc resulting from the AKE when the conditions are applied to the prod. line. Its structure is made by a linear and a deep nonlinear “residual” component learned by empirical observations. The predictions made by the coating nets are used as ground truth in the loss function of the controller. By tuning the weights of the different components of the loss function, it is possible to train models with slightly different optimization purposes. In the tests we compared the regularization of different strategies with the standard one in condition of optimal estimation for both; the overall accuracy is ± 3 g/m^2 dal target for all of them. Lastly, we analyze how the controller modeled the current solutions with the new logic: the sub-optimal values of pres and dist can be optimize of 50% and 20%.