✦ LIBER ✦

Using a neural network approach for muon reconstruction and triggering

✍ Scribed by E. Etzion; H. Abramowicz; Y. Benhammou; G. Dror; D. Horn; L. Levinson; R. Livneh

Publisher: Elsevier Science
Year: 2004
Tongue: English
Weight: 375 KB
Volume: 534
Category: Article
ISSN: 0168-9002
DOI: 10.1016/j.nima.2004.07.091

No coin nor oath required. For personal study only.

✦ Synopsis

The extremely high rate of events that will be produced in the future Large Hadron Collider requires the triggering mechanism to take precise decisions in a few nano-seconds. We present a study which used an artificial neural network triggering algorithm and compared it to the performance of a dedicated electronic muon triggering system. Relatively simple architecture was used to solve a complicated inverse problem. A comparison with a realistic example of the ATLAS first level trigger simulation was in favour of the neural network. A similar architecture trained after the simulation of the electronics first trigger stage showed a further background rejection.

📜 SIMILAR VOLUMES

A neural network approach for grinding p

A neural network approach for grinding processes: Modelling and optimization

✍ T. Warren Liao; L.J. Chen 📂 Article 📅 1994 🏛 Elsevier Science 🌐 English ⚖ 1016 KB

Noise modeling of heterojunction bipolar

Noise modeling of heterojunction bipolar transistors using neural network approach

✍ V. Marković; S. Prasad; A. Stoǐć 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 141 KB

## Abstract The advantages of heterojunction bipolar transistors (HBTs) make them very promising for modern RF communication systems and therefore their noise performance becomes an important issue as well. A procedure for HBT noise modeling based on the neural network approach is presented in this

Building neural network models for time

Building neural network models for time series: a statistical approach

✍ Marcelo C. Medeiros; Timo Teräsvirta; Gianluigi Rech 📂 Article 📅 2005 🏛 John Wiley and Sons 🌐 English ⚖ 360 KB

This paper is concerned with modelling time series by single hidden layer feedforward neural network models. A coherent modelling strategy based on statistical inference is presented. Variable selection is carried out using simple existing techniques. The problem of selecting the number of hidden un

Fast image vector quantization using a m

Fast image vector quantization using a modified competitive learning neural network approach

✍ Robert Li; Earnest Sherrod; Jung Kim; Gao Pan 📂 Article 📅 1997 🏛 John Wiley and Sons 🌐 English ⚖ 259 KB 👁 2 views

The basic goal of image compression through vector generates the address of the codevector specified by Q(x); and quantization (VQ) is to reduce the bit rate for transmission or data a decoder, which uses this address to generate the codevector y. storage while maintaining an acceptable fidelity or

Experience of using the neural network a

Experience of using the neural network approach for identification of MSF desalination plants

✍ Ayman F. Abdulbary; L.L. Lai; Darwish M.K. Al-Gobaisi; A. Husain 📂 Article 📅 1993 🏛 Elsevier Science 🌐 English ⚖ 504 KB

A stacked neural network approach for yi

A stacked neural network approach for yield prediction of propylene polymerization

✍ Seyed Ali Monemian; Hamed Shahsavan; Oberon Bolouri; Shahrouz Taranejoo; Vahabod 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 442 KB 👁 1 views