Speaker
Joao Victor Da Fonseca Pinto
(Univ. Federal do Rio de Janeiro (BR))
Description
After the successful operation of the Large Hadron Collider resulting with the
discovery of the Higgs boson, a new data-taking period (Run 2) has
started. For the first time, collisions are produced with
energies of 13 TeV in the centre of mass. It is foreseen the
luminosity increase, reaching values as high as $10^{34}cm^{-2}s^{-1}$
yet in 2015. These changes in experimental conditions bring a proper
environment for possible new physics key-findings.
ATLAS is the largest LHC detector and was designed for general-purpose
physics studies. Many potential physics channels have electrons or
photons in their final states. For efficient studies on these
channels precise measurement and identification
of such particles is necessary. The identification task consists of
disentangling those particles (signal) from collimated hadronic jets
(background). Reported work concerns the identification process based on the calorimetric quantities.
We propose the usage of ring-shaped calorimetry information, which
explores the shower shape propagation throughout the calorimeter. This
information is fed into a multivariate discriminator, currently
an artificial neural network, responsible for hypothesis testing. The proposal
is taken into account for both the Offline Reconstruction environment performed
after data storage as well as the Online Trigger, used for reducing storage
rate into viable levels while preserving collision events containing desired signals. .
Specifically, this ring description for calorimeter data may be used
in the ATLAS High-Level Trigger.
Specifically, this ring description for calorimeter data may be used in the ATLAS High-Level Trigger as a calorimeter-based preselection at the first step in the trigger chain. Preliminary studies on Monte Carlo suggest that the fake rate can be reduced by as much as 50% over the current methods used in the High-Level Trigger, allowing for high-latency reconstruction algorithms such as tracking to run over regions of interest at a later stage of the trigger.
Author
Ryan Mackenzie White
(Federico Santa Maria Technical University (CL))
