126 research outputs found
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
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