𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Fast track finding with neural networks

✍ Scribed by Georg Stimpfl-Abele; Lluís Garrido

Publisher
Elsevier Science
Year
1991
Tongue
English
Weight
813 KB
Volume
64
Category
Article
ISSN
0010-4655

No coin nor oath required. For personal study only.

✦ Synopsis

We have used a neural network (NN) technique for track reconstruction in a realistic environment. An algorithm based on an Hopfield-style recurrent NN was developed and tested on the track coordinates measured by the TPC of the ALEPH detector at LEP. The efficiency and time consumption are given and are compared with a conventional pattern-recognition method. The performance of the algorithm for large numbers of tracks (up to 200), as expected for LHC and SSC detectors, is discussed.

📜 SIMILAR VOLUMES

Behaviour of feed-forward neural network
Behaviour of feed-forward neural networks in invariant track finding
✍ A. Lanza; P. Vitulo; C. Cattaneo; M. Caresana; F. Panetsos 📂 Article 📅 1994 🏛 Elsevier Science 🌐 English ⚖ 719 KB

We performed several simulations with feed-forward neural networks using an idealized tracking apparatus with tracks invariant under translation and roto-translation transformations. Input information was provided to the networks without any preprocessing. We implemented 2 and 3 layer architectures

Neural networks for tracking
Neural networks for tracking
✍ Å Eide; Th Lindblad; T Lindén; C.S Lindsey 📂 Article 📅 1996 🏛 Elsevier Science 🌐 English ⚖ 399 KB
Recognition of decays of charged tracks
Recognition of decays of charged tracks with neural network techniques
✍ Georg Stimpfl-Abele 📂 Article 📅 1991 🏛 Elsevier Science 🌐 English ⚖ 798 KB

We developped neural-network learning techniques for the recognition of decays of charged tracks using a feed-forward network with error back-propagation. Two completely different methods are described in detail and their efficiencies for several NN architectures are compared with conventional metho