The optimization of reconstruction algorithms has become a key aspect in LHCb as it is currently undergoing a major upgrade that will considerably increase the data processing rate. Aiming to accelerate the second most time consuming reconstruction process of the trigger, we propose an alternative reconstruction algorithm for the Electromagnetic Calorimeter of LHCb. Together with the use of...
A parametric DNN algorithm is used to separate signal from background in the search for scalar top quark pair production at the LHC. This search targets a region of parameter space where the kinematics of top squark pair production and top quark pair production are very similar, because of the mass difference between the top squark and the neutralino being close to the top quark mass. The...
High energy physics analyses often encounter optimization problems in high-dimensional parameter space, and finding an optimal algorithm represents an important initial step in each analysis, where typically an optimum of a very complicated function needs to be found. We performed an optimization algorithm comparison using a number of test functions, for increasing dimensionality, and compared...
We study several simplified dark matter models and their signatures at the LHC using Neural Networks. We focus on the usual monojet plus missing transverse energy channel, but to train the algorithms we organize the data in 2D histograms instead of event-by-event arrays. This results in a huge performance boost to distinguish between standard model (SM) only and SM plus new physics signals. We...
The search for heavy resonances with diverse mass and width values is the ultimate goal of the ATLAS physics group where we are developing this
study. The analysis selects events where one of the top quarks decays hadronically and the other one semileptonically. The reconstruction of the
different components of the decay allows us to build an invariant mass distribution which would show...
