Related papers: Measuring QCD Splittings with Invertible Networks
For simulations where the forward and the inverse directions have a physics meaning, invertible neural networks are especially useful. A conditional INN can invert a detector simulation in terms of high-level observables, specifically for…
Understanding the interactions between elementary particles and mapping out the internal structure of the hadrons are of fundamental importance in high energy nuclear and particle physics. This thesis concentrates on the strong interaction,…
The nucleon structure functions probed in deep-inelastic scattering at large virtualities form an important tool to test Quantum Chromdynamics (QCD) through precision measurements of the strong coupling constant $\alpha_s(M_Z^2)$ and the…
We make the connection between certain deep learning architectures and the renormalisation group explicit in the context of QCD by using a deep learning network to construct a toy parton shower model. The model aims to describe…
The splitting function is a universal property of quantum chromodynamics (QCD) which describes how energy is shared between partons. Despite its ubiquitous appearance in many QCD calculations, the splitting function cannot be measured…
We present the basic aspects of deep inelastic phenomena in the framework of the QCD parton model. After recalling briefly the standard kinematics, we discuss the physical interpretation of unpolarized and polarized structure functions in…
We discuss QCD studies that will be possible at LEP2. We examine both experimental and theoretical aspects of jets, fragmentation functions, multiplicities and particle spectra.
With the great promise of deep learning, discoveries of new particles at the Large Hadron Collider (LHC) may be imminent. Following the discovery of a new Beyond the Standard model particle in an all-hadronic channel, deep learning can also…
We summarise the latest experimental QCD studies based on data from LEP. Measurements of the quark and gluon jet fragmentation functions are discussed, including a new algorithm to infer the properties of unbiased gluon jets. We describe a…
Machine learning methods incorporating deep neural networks have been the subject of recent proposals for new hadronic resonance taggers. These methods require training on a dataset produced by an event generator where the true class labels…
This review describes the current status of precision QCD studies at the LHC. We introduce the main experimental and theoretical methods, discussing also their cross-stimulated developments and recent advances. The different types of QCD…
Measurements of jet substructure provide precise tests of Quantum Chromodynamics (QCD) and offer a distinct way to study hadronization mechanisms, compared to measurements of hadrons alone. QCD predicts that jet radiation patterns depend on…
Hadronic jets are extremely abundant at the LHC, and testing QCD in various corners of phase-space is important to understand backgrounds and some specific signatures of new physics. In this article, various measurements aiming at probing…
Studies of fully-reconstructed jets in heavy-ion collisions aim at extracting thermodynamical and transport properties of hot and dense QCD matter. Recently, a plethora of new jet substructure observables have been theoretically and…
Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top…
We study the issue of separating hadronic jets that contain bottom quarks ($b$-jets) from jets featuring light partons only. We develop a novel approach to $b$-tagging that exploits the application of QCD-inspired jet substructure…
We investigate the behaviour of jets at high-multiplicity using analytic techniques. We consider in detail the rates, areas and the intermediate splitting scales as a function of the number of jets. In each case, we are able to characterise…
Using deep neural networks for identifying physics objects at the Large Hadron Collider (LHC) has become a powerful alternative approach in recent years. After successful training of deep neural networks, examining the trained networks not…
Lepto-nuclear colliders offer unique experimental opportunities to probe QCD in an extended medium. Of the many possibilities, three experiments are described here that are clearly feasible and of high scientific importance. First, a direct…
An improved analysis method to extract quark helicity distributions in leading order (LO) QCD from semi-inclusive double spin asymmetries in deep inelastic scattering is presented. The method relies on the fact that fragmentation functions,…