scholarly journals LHC PHYSICS AND COSMOLOGY

2007 ◽  
Author(s):  
N. E. MAVROMATOS
Keyword(s):  
Lhc Physics

scholarly journals Power corrections to event shapes using Eikonal dressed gluon exponentiation

2021 ◽  
Vol 2021 (3) ◽  
Author(s):  
Neelima Agarwal ◽  
Ayan Mukhopadhyay ◽  
Sourav Pal ◽  
Anurag Tripathi
Keyword(s):  
Cross Section ◽  
Established Method ◽  
Hadronic Event ◽  
Power Corrections ◽  
Electron Positron Annihilation ◽  
Electron Positron ◽  
Lhc Physics ◽  
Event Shapes ◽  
Jet Shapes

AbstractEvent shapes are classical tools for the determination of the strong coupling and for the study of hadronization effects in electron-positron annihilation. In the context of analytical studies, hadronization corrections take the form of power-suppressed contributions to the cross section, which can be extracted from the perturbative ambiguity of Borel-resummed distributions. We propose a simplified version of the well-established method of Dressed Gluon Exponentiation (DGE), which we call Eikonal DGE (EDGE), which determines all dominant power corrections to event shapes by means of strikingly elementary calculations. We believe our method can be generalized to hadronic event shapes and jet shapes of relevance for LHC physics.

scholarly journals Deep Learning and Its Application to LHC Physics

2018 ◽  
Vol 68 (1) ◽  
pp. 161-181 ◽  
Author(s):  
Dan Guest ◽  
Kyle Cranmer ◽  
Daniel Whiteson
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Energy Physics ◽  
High Energy ◽  
Learning Tools ◽  
Future Prospects ◽  
Higher Dimensional ◽  
Lhc Physics ◽  
Core Concepts ◽  
Energy Physics

Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex problems than previously feasible. This review is aimed at the reader who is familiar with high-energy physics but not machine learning. The connections between machine learning and high-energy physics data analysis are explored, followed by an introduction to the core concepts of neural networks, examples of the key results demonstrating the power of deep learning for analysis of LHC data, and discussion of future prospects and concerns.

scholarly journals R-Axion: A New LHC Physics Signature Involving Muon Pairs

2009 ◽  
Vol 180 ◽  
pp. 27-34 ◽  
Author(s):  
Hock-Seng Goh ◽  
Masahiro Ibe
Keyword(s):  
Muon Pairs ◽  
Lhc Physics

scholarly journals LHC physics of extra gauge bosons in the 4D Composite Higgs Model

2013 ◽  
Vol 60 ◽  
pp. 20049
Author(s):  
D. Barducci ◽  
A. Belyaev ◽  
S. De Curtis ◽  
S. Moretti ◽  
G. M. Pruna
Keyword(s):  
Higgs Model ◽  
Gauge Bosons ◽  
Composite Higgs ◽  
Lhc Physics
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