Multi-Parton Interactions at the LHC

We review the recent progress in the theoretical description and experimental observation of multiple parton interactions. Subjects covered include experimental measurements of minimum bias interactions and of the underlying event, models of soft physics implemented in Monte Carlo generators, developments in the theoretical description of multiple parton interactions and phenomenological studies of double parton scattering. This article stems from contributions presented at the Helmholtz Alliance workshop on "Multi-Parton Interactions at the LHC", DESY Hamburg, 13-15 September 2010.Comment: 68 page

Parton distributions with threshold resummation

We construct a set of parton distribution functions (PDFs) in which fixed-order NLO and NNLO calculations are supplemented with soft-gluon (threshold) resummation up to NLL and NNLL accuracy respectively, suitable for use in conjunction with any QCD calculation in which threshold resummation is included at the level of partonic cross sections. These resummed PDF sets, based on the NNPDF3.0 analysis, are extracted from deep-inelastic scattering, Drell-Yan, and top quark pair production data, for which resummed calculations can be consistently used. We find that, close to threshold, the inclusion of resummed PDFs can partially compensate the enhancement in resummed matrix elements, leading to resummed hadronic cross-sections closer to the fixed-order calculation. On the other hand, far from threshold, resummed PDFs reduce to their fixed-order counterparts. Our results demonstrate the need for a consistent use of resummed PDFs in resummed calculations.Comment: 43 pages, 17 figures, accepted for publication in JHE

Study of b- and c-jets identification with quantum machine learning algorithms and application to the Higgs reconstruction

In questa tesi viene presentato un nuovo algoritmo per la classificazione del sapore dei getti adronici ad LHCb, basato su tecniche di calcolo quantistico applicate all’intelligenza artificiale. L’algoritmo è stato sviluppato per distinguere i getti prodotti dai quark bottom e charm, ed è stato applicato alla ricerca del decadimento del bosone di Higgs in due quark charm (H->c c-bar). In questo studio sono stati utilizzati i dati simulati delle collisioni protone-protone ad LHCb, ad un’energia nel centro di massa di 13 TeV. L'accuratezza della classificazione è stata testata in funzione della cinematica dei getti adronici, e le prestazioni dell’algoritmo quantistico sono state confrontate con con quelle di un classico algoritmo di intelligenza artificiale. In particolare i due tipi di algoritmi mostrano prestazioni confrontabili. Successivamente, entrambi i modelli sono stati utilizzati per studiare la sensitività di LHCb nella ricerca dei decadimenti H->bb-bar e H->cc-bar. Entrambi i metodi hanno mostrato un incremento di sensitività rispetto a quello basato sulla ricostruzione dei vertici di decadimento degli aironi b e c, utilizzato nelle precedenti analisi di LHCb. Anche in questo caso sono state ottenute prestazioni confrontabili tra l’algoritmo di intelligenza artificiale quantistico e quello classico. Nonostante lo studio degli algoritmi quantistici sia alle fasi iniziali, i risultati ottenuti sono incoraggianti per continuare la ricerca in questo campo.In this thesis, a new approach for jet flavour classification, between b- versus c-quark jets, based on Quantum Machine Learning techniques for the Higgs to ccbar studies is presented. Besides, the training of the model was carried out by using LHCb proton-proton collision simulated data at the center of mass energy of 13 TeV. The jet flavour classification accuracy was tested as a function of the jet transverse momentum and the jet pseudorapidity between the Quantum Machine Learning model and the classical machine learning model, where the b-quark jet test accuracy shows the most similar classification results between the QML method and the classical ML method. Later, this model was used to study the sensitivity of the Higgs boson decay into a pair of b-quarks and the Higgs boson decay into a pair of c-quarks using samples of LHCb simulated data corresponding to the integrated luminosity of the Run 2. Either the quantum machine learning method or the classical machine learning model gave an improvement in the significance for the Higgs to ccbar observation. At the point of maximum significance improvement, the efficiency and mistagging efficiency were evaluated as functions of the jet transverse momentum, jet pseudorapidity and dijet invariant mass obtaining comparable performances between the quantum machine learning model and the classical machine learning model. In spite of the quantum machine learning is a technique at its early stage, the obtained results are encouraging to continue with the research in this field looking for a better performance

The Hot QCD White Paper: Exploring the Phases of QCD at RHIC and the LHC

The past decade has seen huge advances in experimental measurements made in heavy ion collisions at the Relativistic Heavy Ion Collider (RHIC) and more recently at the Large Hadron Collider (LHC). These new data, in combination with theoretical advances from calculations made in a variety of frameworks, have led to a broad and deep knowledge of the properties of thermal QCD matter. Increasingly quantitative descriptions of the quark-gluon plasma (QGP) created in these collisions have established that the QGP is a strongly coupled liquid with the lowest value of specific viscosity ever measured. However, much remains to be learned about the precise nature of the initial state from which this liquid forms, how its properties vary across its phase diagram and how, at a microscopic level, the collective properties of this liquid emerge from the interactions among the individual quarks and gluons that must be visible if the liquid is probed with sufficiently high resolution. This white paper, prepared by the Hot QCD Writing Group as part of the U.S. Long Range Plan for Nuclear Physics, reviews the recent progress in the field of hot QCD and outlines the scientific opportunities in the next decade for resolving the outstanding issues in the field.Comment: 110 pages, 33 figures, 429 references. Prepared as part of the U.S. Long-Range Plan for Nuclear Physic

Heavy quarks and jets as probes of the QGP

Quark-Gluon Plasma (QGP), a QCD state of matter created in ultra-relativistic heavy-ion collisions, has remarkable properties, including, for example, a low shear viscosity over entropy ratio. By detecting the collection of low-momentum particles that arise from the collision, it is possible to gain quantitative insight into the created matter. However, its fast evolution and thermalization properties remain elusive. Only using high momentum objects as probes of QGP can unveil its constituents at different wavelengths. In this review, we attempt to provide a comprehensive picture of what was, so far, possible to infer about QGP given our current theoretical understanding of jets, heavy-flavor, and quarkonia. We will bridge the resulting qualitative picture to the experimental observations done at the LHC and RHIC. We will focus on the phenomenological description of experimental observations, provide a brief analytical summary of the description of hard probes, and an outlook on the main difficulties we will need to surpass in the following years. To benchmark QGP-related effects, we will also address nuclear modifications to the initial state and hadronization effects