125 research outputs found

    Towards the determination of the photon parton distribution function constrained by LHC data

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    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

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    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

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    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 GÎŒG_{\mu} 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

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    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

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    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

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    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

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    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

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    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 WW and Z/γ∗Z/\gamma^* 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 WW 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