Add to ORKGIn this project we study the use of neural networks as a tool for particle track pattern recognition with the possibility of its implementation in the Trigger system at the ATLAS experiment [1]. By using a method named Hough transform we created a Convolutional Neural Network (CNN) that is able to train on the transformed images of muons merged with minimum bias. We give an overview of how the CNN works and compare the results from the CNN with the old cut based method. We believe to have managed to find an alternative to the previously used algorithm, that is faster and more efficient
The hit signals read out from pixels on planar semi-conductor sensors are grouped into clusters, to ...
A neural network is proposed for the recognition of partially overlapped particle images in the anal...
Pattern recognition problems in high energy physics are notably different from traditional machine l...
In this project we study the use of neural networks as a tool for particle track pattern recognition...
Particle track reconstruction is a challenging problem in modern high-energy physics detectors where...
A neural network solution for a complicated experimental high energy physics problem is described. T...
The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to ...
Nowadays the implementation of artificial neural networks in high-energyphysics has obtained excelle...
The physics reach of the HL-LHC will be limited by how efficiently the experiments can use the avail...
In this paper, machine learning techniques are used to reconstruct particle collision pathways. CERN...
A neural network solution for a complicated experimental High Energy Physics problem is described. T...
A neural-network based algorithm to identify fake tracks in the LHCb pattern recognition is presente...
High Energy Physics experiments require fast and efficient methods toreconstruct the tracks of charg...
High energy physics experiments require fast and efficient methods for reconstructing the tracks of...
Two neural network algorithms for data analysis in relativistic nuclear physics are presented. A neu...
The hit signals read out from pixels on planar semi-conductor sensors are grouped into clusters, to ...
A neural network is proposed for the recognition of partially overlapped particle images in the anal...
Pattern recognition problems in high energy physics are notably different from traditional machine l...
In this project we study the use of neural networks as a tool for particle track pattern recognition...
Particle track reconstruction is a challenging problem in modern high-energy physics detectors where...
A neural network solution for a complicated experimental high energy physics problem is described. T...
The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to ...
Nowadays the implementation of artificial neural networks in high-energyphysics has obtained excelle...
The physics reach of the HL-LHC will be limited by how efficiently the experiments can use the avail...
In this paper, machine learning techniques are used to reconstruct particle collision pathways. CERN...
A neural network solution for a complicated experimental High Energy Physics problem is described. T...
A neural-network based algorithm to identify fake tracks in the LHCb pattern recognition is presente...
High Energy Physics experiments require fast and efficient methods toreconstruct the tracks of charg...
High energy physics experiments require fast and efficient methods for reconstructing the tracks of...
Two neural network algorithms for data analysis in relativistic nuclear physics are presented. A neu...
The hit signals read out from pixels on planar semi-conductor sensors are grouped into clusters, to ...
A neural network is proposed for the recognition of partially overlapped particle images in the anal...
Pattern recognition problems in high energy physics are notably different from traditional machine l...