A new neural network learning algorithm, called the Umbrella Algorithm, is developed and analysed. Its generalization, which does not exhibit over-specialisation, is observed in the EXOR problem and in an artificial data discrimination (Toy Data) problem. The learning time is found to be about 1/15 of backpropagation learning time for the parity problem. The algorithm is applied to cosmic high energy gamma ray detection and is further used for determining the spectral index of the Markarian 421 source
Nowadays the implementation of artificial neural networks in high-energyphysics has obtained excelle...
In this paper two different approaches to provide information from events by high energy physics exp...
This work was developed in the context of space-born gamma-ray astronomy, with particular focus on a...
We employ neural networks for classification of data of the TUS fluorescence telescope, the world’s ...
A neural algorithm was developed to separate electromagnetic and hadronic showers detected with an a...
The sensitivity of a Cherenkov imaging telescope, is strongly dependent on the rejection of the cos...
In this work, we present a new, high performance algorithm for background rejection in imaging atmos...
The potential of two Kohonen artificial neural networks (ANNs) - linear vector quantisa- tion(LVQ)an...
We apply a machine learning algorithm, the artificial neural network, to the search for gravitationa...
Artificial neural network (ANN) has recently been used for the analysis of gamma-ray spectrum. The A...
In this work, we present a new, high performance algorithm for background rejection in imaging atmos...
Radionuclide identification is an important part of the nuclear material identification system. The ...
The HESS project is a major international experiment currently performed in gamma astronomy. This pr...
International audienceThe rapid and accurate identification of radionuclides brings crucial informat...
The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory and will be th...
Nowadays the implementation of artificial neural networks in high-energyphysics has obtained excelle...
In this paper two different approaches to provide information from events by high energy physics exp...
This work was developed in the context of space-born gamma-ray astronomy, with particular focus on a...
We employ neural networks for classification of data of the TUS fluorescence telescope, the world’s ...
A neural algorithm was developed to separate electromagnetic and hadronic showers detected with an a...
The sensitivity of a Cherenkov imaging telescope, is strongly dependent on the rejection of the cos...
In this work, we present a new, high performance algorithm for background rejection in imaging atmos...
The potential of two Kohonen artificial neural networks (ANNs) - linear vector quantisa- tion(LVQ)an...
We apply a machine learning algorithm, the artificial neural network, to the search for gravitationa...
Artificial neural network (ANN) has recently been used for the analysis of gamma-ray spectrum. The A...
In this work, we present a new, high performance algorithm for background rejection in imaging atmos...
Radionuclide identification is an important part of the nuclear material identification system. The ...
The HESS project is a major international experiment currently performed in gamma astronomy. This pr...
International audienceThe rapid and accurate identification of radionuclides brings crucial informat...
The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory and will be th...
Nowadays the implementation of artificial neural networks in high-energyphysics has obtained excelle...
In this paper two different approaches to provide information from events by high energy physics exp...
This work was developed in the context of space-born gamma-ray astronomy, with particular focus on a...