132 research outputs found
Towards the determination of the photon parton distribution function constrained by LHC data
We provide a discussion of the impact of a subset of Drell-Yan data from LHC
on the determination of the photon parton distribution function (PDF), using
the NNPDF methodology. In previous work we have shown that the photon PDF
determined from deep-inelastic scattering (DIS) data only has large
uncertainties, suggesting the need for more data from other processes such as
Drell-Yan, which unlike DIS, includes photon-induced contributions at leading
order in QED. We describe the inclusion of ATLAS Drell-Yan W, Z data, which is
a subset of the LHC data used in a final photon PDF determination, by means of
a reweighting procedure. We show the impact of such data by comparing the
reweighted photon PDF with the photon PDF from DIS, highlighting the reduction
of uncertainties at medium/small-x. We conclude that the Drell-Yan data from
LHC allows a reasonably accurate determination of the photon PDF.Comment: 5 pages, 10 figures, to appear in the proceedings of the XXI
International Workshop on Deep-Inelastic Scattering and Related Subjects
(DIS2013), Marseille, 22-26 April 201
Machine learning challenges in theoretical HEP
In these proceedings we perform a brief review of machine learning (ML)
applications in theoretical High Energy Physics (HEP-TH). We start the
discussion by defining and then classifying machine learning tasks in
theoretical HEP. We then discuss some of the most popular and recent published
approaches with focus on a relevant case study topic: the determination of
parton distribution functions (PDFs) and related tools. Finally, we provide an
outlook about future applications and developments due to the synergy between
ML and HEP-TH.Comment: 7 pages, 3 figures, in proceedings of the 18th International Workshop
on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017
Disentangling electroweak effects in Z-boson production
Parton distributions with QED corrections open new scenarios for high
precision physics. We recall the need for accurate and improved predictions
which keeps into account higher order QCD corrections together with electroweak
effects. We study predictions obtained with the improved Born approximation and
the scheme by using two public codes: DYNNLO and HORACE. We focus our
attention on the Drell-Yan Z-boson invariant mass distribution at low- and
high-mass regions, recently measured by the ATLAS experiment and we estimate
the impact of each component of the final prediction. We show that electroweak
corrections are larger than PDF uncertainties for modern PDF sets and therefore
such corrections are necessary to improve the extraction of future PDF sets.Comment: 5 pages, 4 figures, to appear in the proceedings of the Les
Rencontres de Physique de la Vall\'ee d'Aoste, La Thuile 201
Modeling NNLO jet corrections with neural networks
We present a preliminary strategy for modeling multidimensional distributions
through neural networks. We study the efficiency of the proposed strategy by
considering as input data the two-dimensional next-to-next leading order (NNLO)
jet k-factors distribution for the ATLAS 7 TeV 2011 data. We then validate the
neural network model in terms of interpolation and prediction quality by
comparing its results to alternative models.Comment: Proceedings for the Cracow Epiphany Conference 2017, final versio
Perturbative QCD description of jet data from LHC Run-I and Tevatron Run-II
We present a systematic comparison of jet predictions at the LHC and the
Tevatron, with accuracy up to next-to-next-to-leading order (NNLO). The exact
computation at NNLO is completed for the gluons-only channel, so we compare the
exact predictions for this channel with an approximate prediction based on
threshold resummation, in order to determine the regions where this
approximation is reliable at NNLO. The kinematic regions used in this study are
identical to the experimental setup used by recently published jet data from
the ATLAS and CMS experiments at the LHC, and CDF and D0 experiments at the
Tevatron. We study the effect of choosing different renormalisation and
factorisation scales for the NNLO exact prediction and as an exercise assess
their impact on a PDF fit including these corrections. Finally we provide
numerical values of the NNLO k-factors relevant for the LHC and Tevatron
experiments.Comment: 51 pages, 13 figures, 35 tables. Final version, matches published
version in JHE
Towards the compression of parton densities through machine learning algorithms
One of the most fascinating challenges in the context of parton density
function (PDF) is the determination of the best combined PDF uncertainty from
individual PDF sets. Since 2014 multiple methodologies have been developed to
achieve this goal. In this proceedings we first summarize the strategy adopted
by the PDF4LHC15 recommendation and then, we discuss about a new approach to
Monte Carlo PDF compression based on clustering through machine learning
algorithms.Comment: 4 pages, 4 figures, to appear in the proceedings of 50th Rencontres
de Moriond, QCD and High Energy Interactions, La Thuile, Italy, March 201
Jet grooming through reinforcement learning
We introduce a novel implementation of a reinforcement learning (RL)
algorithm which is designed to find an optimal jet grooming strategy, a
critical tool for collider experiments. The RL agent is trained with a reward
function constructed to optimize the resulting jet properties, using both
signal and background samples in a simultaneous multi-level training. We show
that the grooming algorithm derived from the deep RL agent can match
state-of-the-art techniques used at the Large Hadron Collider, resulting in
improved mass resolution for boosted objects. Given a suitable reward function,
the agent learns how to train a policy which optimally removes soft wide-angle
radiation, allowing for a modular grooming technique that can be applied in a
wide range of contexts. These results are accessible through the corresponding
GroomRL framework.Comment: 11 pages, 10 figures, code available at
https://github.com/JetsGame/GroomRL, updated to match published versio
Parton distribution functions with QED corrections
We present the first unbiased determination of parton distribution functions
(PDFs) with electroweak corrections. The aim of this thesis is to provide an
exhaustive description of the theoretical framework and the technical
implementation which leads to the determination of a set of PDFs which includes
the photon PDF and quantum electrodynamics (QED) contributions to parton
evolution. First, we introduce and motivate the need of including electroweak
corrections to PDFs, providing phenomenological examples and presenting an
overview of the current state of the art in PDF fits. The theoretical
implications of such corrections are then described through the implementation
of the combined QCD+QED evolution in APFEL, a public code for the solution of
the PDF evolution developed particularly for this thesis. We proceed by
presenting the new structure of the Neural-Network PDF (NNPDF) methodology used
for the extraction of this set of PDFs with QED corrections. We then provide a
first determination of the full set of PDFs based on deep-inelastic scattering
data and LHC data for and Drell-Yan production, using
leading-order QED and NLO or NNLO QCD: the so-called NNPDF2.3QED set of PDFs.
We perform a preliminary investigation of the phenomenological implications of
NNPDF2.3QED set, in particular, focusing on the photon-induced corrections to
direct photon production at HERA, high-mass dilepton and pair production at
the LHC and finally, providing a first determination of lepton PDFs through the
APFEL evolution. We conclude with a summary of the technological upgrades
required for the improvement of future PDF determinations with electroweak
corrections.Comment: 152 pages, PhD thesi
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