Identification of gluon jets with a neural network technique

Olof Barring; Torsten Akesson; Vincent Hedberg

doi:10.1117/12.205109

6 April 1995 Identification of gluon jets with a neural network technique

Olof Barring, Torsten Akesson, Vincent Hedberg

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205109
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

The separation of jets originating from quarks and gluons in three-jet events at LEP by using a neural network program has been studied. The analysis has been done in two steps, one in which only the jet-energy is used for the identification and a second step in which jet fragmentation variables are used as well. A new training procedure of the neural network combined with a new normalization of the fragmentation variables gives a method of identifying gluon jets from their fragmentation properties without distorting the energy spectrum of the identified jets. Since the fragmentation process can only be studied with phenomenological models it is important that the identification procedure is to a large extent model independent and the study has been made with two different jet fragmentation models.

Citation Download Citation

Olof Barring, Torsten Akesson, and Vincent Hedberg "Identification of gluon jets with a neural network technique", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205109

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available