Probing for leptophilic gauge boson Zl at ILC with $\sqrt{s} = 1~{\rm TeV}$ by using ANN-Reference-Cited by-同舟云学术

Probing for leptophilic gauge boson Zl at ILC with $\sqrt{s} = 1~{\rm TeV}$ by using ANN

Published:2014-12-08 Issue:30 Volume:29 Page:1450171
ISSN:0217-751X
Container-title:International Journal of Modern Physics A
language:en
Short-container-title:Int. J. Mod. Phys. A

Author:

Kara S. Okan¹,Akkoyun Serkan²,Bayram Tuncay³

Affiliation:

1. Nigde University, Bor Vocational School, 51700 Nigde, Turkey

2. Cumhuriyet University, Vocational School of Health, 58140 Sivas, Turkey

3. Physics Department, Sinop University, 57000 Sinop, Turkey

Abstract

We search for leptophilic gauge boson Zl via the process e+e- → μ+μ- at ILC with [Formula: see text]. In the leptonic extension of SM [Formula: see text] we have predicted that ILC with [Formula: see text] will enable searching Zl with masses up to the center-of-mass energy if the related coupling constant gl exceeds 10-3 for 3σ observations and 5σ discovery. Furthermore similar results have been obtained by using artificial neural network (ANN) method.

Publisher

World Scientific Pub Co Pte Lt

Subject

Astronomy and Astrophysics,Nuclear and High Energy Physics,Atomic and Molecular Physics, and Optics

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0217751X14501711

Reference47 articles.

1. Conservation of Heavy Particles and Generalized Gauge Transformations

2. Compensation of lepton charge of matter with relic anti-sneutrinos

3. Leptons and photons

4. LEPTONIC PHOTON AND LIGHT ELEMENT ABUNDANCIES

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