The use of artificial neural network in discrimination between signal and background is investigated for two different processes in the ATLAS detector. For the WH signal against the WZ/Wγ∗ background the neural network shows improvement over the boosted decision trees previously used in Aad et al. [1]. Also for the pair produced stop quark process with two leptons in the final state is archieved good discrimination between the signal and the background of fake/non-prompt leptons
The aim of this thesis is to perform an analysis of Signal recognition and Background rejection to o...
A novel technique using a set of artificial neural networks to identify and split merged measurement...
The read-out from individual pixels on planar semi-conductor sensors are grouped into clusters to re...
The popularity of Machine Learning (ML) has been increasing in recent decades in almost every area, ...
The application of neural networks in high energy physics to the separation of signal from backgroun...
The CMS inner tracking system is a fully silicon-based high precision detector. Accurate knowledge o...
The goal of this thesis is to extend selection criteria for getting signal/background samples from d...
Hadronic decays of vector bosons and top quarks are increasingly important to the ATLAS physics prog...
The separation of b-quark initiated jets from those coming from lighter quark flavors (b-tagging) is...
We present techniques for the identification of hadronically-decaying W bosons and top quarks using ...
The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to ...
In this thesis we have searched for new physics phenomena predicted by Supersymmetry and Dark Matter...
We investigate the potential of using deep learning techniques to reject background events in search...
The read-out from individual pixels on planar semi-conductor sensors are grouped into clusters to re...
The Higgs boson coupling with top quarks is of great interest in particle physics research. The part...
The aim of this thesis is to perform an analysis of Signal recognition and Background rejection to o...
A novel technique using a set of artificial neural networks to identify and split merged measurement...
The read-out from individual pixels on planar semi-conductor sensors are grouped into clusters to re...
The popularity of Machine Learning (ML) has been increasing in recent decades in almost every area, ...
The application of neural networks in high energy physics to the separation of signal from backgroun...
The CMS inner tracking system is a fully silicon-based high precision detector. Accurate knowledge o...
The goal of this thesis is to extend selection criteria for getting signal/background samples from d...
Hadronic decays of vector bosons and top quarks are increasingly important to the ATLAS physics prog...
The separation of b-quark initiated jets from those coming from lighter quark flavors (b-tagging) is...
We present techniques for the identification of hadronically-decaying W bosons and top quarks using ...
The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to ...
In this thesis we have searched for new physics phenomena predicted by Supersymmetry and Dark Matter...
We investigate the potential of using deep learning techniques to reject background events in search...
The read-out from individual pixels on planar semi-conductor sensors are grouped into clusters to re...
The Higgs boson coupling with top quarks is of great interest in particle physics research. The part...
The aim of this thesis is to perform an analysis of Signal recognition and Background rejection to o...
A novel technique using a set of artificial neural networks to identify and split merged measurement...
The read-out from individual pixels on planar semi-conductor sensors are grouped into clusters to re...