Add to ORKGThe aim of this thesis is to perform an analysis of Signal recognition and Background rejection to outline a framework for data analysis based on Artificial Neural Network (ANN) suitable to the discrimination of samples from the GERDA experiment. More precisely, this work will focus on setting up an ANN that, through Pulse Shape Analysis techniques, can recognize a possible 0vbb decay from background events
International audienceGRAND is a Ultra High Energy (UHE) cosmic particles detection project, consist...
Abstract—Conventional peak detection algorithms are not designed to include information on the expec...
The new generation of nuclear physics detectors that used to study nuclear reactions is considering ...
The GERDA experiment located at the Laboratori Nazionali del Gran Sasso of INFN searches for neutrin...
A pulse-shape discrimination method based on artificial neural networks was applied to pulses simula...
The Gerda experiment located at the Laboratori Nazionali del Gran Sasso of INFN searches for neutrin...
A pulse-shape discrimination method based on artificial neural networks was applied to pulses simula...
Experiments searching for rare processes like neutrinoless double beta decay heavily rely on the ide...
The Heidelberg-Moscow Experiment is presently the most sensitive experiment looking for neutrinoless...
Abstract – To reduce background in experiments looking for rare events, such as the GERDA double bet...
The use of artificial neural network in discrimination between signal and background is investigated...
The Germanium Detector Array (Gerda) experiment, located underground at the INFN Laboratori Nazional...
We investigate the potential of using deep learning techniques to reject background events in search...
KamLAND-Zen is a neutrinoless double beta decay $(0\nu\beta\beta)$ search experiment using $^{136}$...
The application of neural networks in high energy physics to the separation of signal from backgroun...
International audienceGRAND is a Ultra High Energy (UHE) cosmic particles detection project, consist...
Abstract—Conventional peak detection algorithms are not designed to include information on the expec...
The new generation of nuclear physics detectors that used to study nuclear reactions is considering ...
The GERDA experiment located at the Laboratori Nazionali del Gran Sasso of INFN searches for neutrin...
A pulse-shape discrimination method based on artificial neural networks was applied to pulses simula...
The Gerda experiment located at the Laboratori Nazionali del Gran Sasso of INFN searches for neutrin...
A pulse-shape discrimination method based on artificial neural networks was applied to pulses simula...
Experiments searching for rare processes like neutrinoless double beta decay heavily rely on the ide...
The Heidelberg-Moscow Experiment is presently the most sensitive experiment looking for neutrinoless...
Abstract – To reduce background in experiments looking for rare events, such as the GERDA double bet...
The use of artificial neural network in discrimination between signal and background is investigated...
The Germanium Detector Array (Gerda) experiment, located underground at the INFN Laboratori Nazional...
We investigate the potential of using deep learning techniques to reject background events in search...
KamLAND-Zen is a neutrinoless double beta decay $(0\nu\beta\beta)$ search experiment using $^{136}$...
The application of neural networks in high energy physics to the separation of signal from backgroun...
International audienceGRAND is a Ultra High Energy (UHE) cosmic particles detection project, consist...
Abstract—Conventional peak detection algorithms are not designed to include information on the expec...
The new generation of nuclear physics detectors that used to study nuclear reactions is considering ...