scholarly journals AGN jet physics and apparent opening angles

2016 ◽  
Author(s):  
Eric Clausen-Brown ◽  
Tuomas Savolainen ◽  
Alexander Pushkarev ◽  
Yuri Y. Kovalev ◽  
Matt L. Lister
Keyword(s):  
Jet Physics

scholarly journals Tag N’ Train: a technique to train improved classifiers on unlabeled data

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Oz Amram ◽  
Cristina Mantilla Suarez
Keyword(s):  
Real Data ◽  
Unlabeled Data ◽  
Machine Learning Techniques ◽  
Jet Physics ◽  
Classification Problems ◽  
Weak Classifier ◽  
Potential Applications ◽  
Substantial Progress ◽  
Resonance Search ◽  
Class Labels

Abstract There has been substantial progress in applying machine learning techniques to classification problems in collider and jet physics. But as these techniques grow in sophistication, they are becoming more sensitive to subtle features of jets that may not be well modeled in simulation. Therefore, relying on simulations for training will lead to sub-optimal performance in data, but the lack of true class labels makes it difficult to train on real data. To address this challenge we introduce a new approach, called Tag N’ Train (TNT), that can be applied to unlabeled data that has two distinct sub-objects. The technique uses a weak classifier for one of the objects to tag signal-rich and background-rich samples. These samples are then used to train a stronger classifier for the other object. We demonstrate the power of this method by applying it to a dijet resonance search. By starting with autoencoders trained directly on data as the weak classifiers, we use TNT to train substantially improved classifiers. We show that Tag N’ Train can be a powerful tool in model-agnostic searches and discuss other potential applications.

scholarly journals Jet physics in e+e− interactions

1984 ◽  
Author(s):  
Frank E. Paige
Keyword(s):  
Jet Physics

Probing the accretion-jet physics with the radio∕X-ray correlation

2008 ◽  
Author(s):  
Xue-Bing Wu ◽  
R. Wang ◽  
Z. Y. Li ◽  
M. Z. Kong ◽  
Ye-Fei Yuan ◽  
...  
Keyword(s):  
Jet Physics ◽  
X Ray

Jet physics at the CERN intersecting storage rings

1990 ◽  
Vol 13 (7) ◽  
pp. 1-42
Author(s):  
V. Cavasinni
Keyword(s):  
Storage Rings ◽  
Jet Physics

Soft-Collinear Effective Theory

2020 ◽  
pp. 307-361 ◽  
Author(s):  
Thomas Becher
Keyword(s):  
Cross Sections ◽  
Vector Current ◽  
Jet Physics ◽  
Effective Theory ◽  
Event Shape ◽  
Sudakov Form Factor ◽  
Shape Variable ◽  
Soft Collinear Effective Theory ◽  
Jet Cross Sections ◽  
Event Shape Variable

The lectures that appear within this chapter provide an introduction to soft-collinear effective theory (SCET). It begins by discussing resummation for soft-photon effects in QED, including soft photons in electron–electron scattering and the expansion of loop integrals and the method of regions event-shape variables. It then covers SCET specifically, including the method of regions for the Sudakov form factor, effective Lagrangians, the vector current in SCET, and resummation by renormalization group (RG) evolution. It covers applications of SCET in jet physics, describes the characteristic feature in jet processes of Sudakov logarithms, and discusses factorization for the event-shape variable thrust and factorization and resummation for jet cross sections.

scholarly journals QCD-aware recursive neural networks for jet physics

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Gilles Louppe ◽  
Kyunghyun Cho ◽  
Cyril Becot ◽  
Kyle Cranmer
Keyword(s):  
Neural Networks ◽  
Jet Physics ◽  
Recursive Neural Networks

scholarly journals Recent results on jet physics from ALICE at the LHC

2013 ◽  
Vol 458 ◽  
pp. 012025
Author(s):  
Alexandre Shabetai ◽  
Keyword(s):  
Jet Physics
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