225 research outputs found
Disentangling electroweak effects in Z-boson production
No full textParton 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
Modeling NNLO jet corrections with neural networks
No full textWe 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
Towards an unbiased determination of parton distributions with QED corrections
No full textElectroweak corrections to hadron collider processes become relevant at the level of precision reached by present-day LHC experiments. We provide a preliminary discussion of the impact of electroweak corrections to parton distributions, concentrating on electrodynamics corrections to parton evolution equations, and showing a preliminary assessment of their impact. Furthermore, we determine the parton distribution function of the photon from deep inelastic scattering data using the NNPDF methodology
Machine learning challenges in theoretical HEP
No full textIn 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
APFEL Web: a web-based application for the graphical visualization of parton distribution functions
No full textWe present APFEL Web , a Web-based application designed to provide a flexible user-friendly tool for the graphical visualization of parton distribution functions. In this note we describe the technical design of the APFEL Web application, motivating the choices and the framework used for the development of this project. We document the basic usage of APFEL Web and show how it can be used to provide useful input for a variety of collider phenomenological studies. Finally we provide some examples showing the output generated by the application
Sampling the Riemann-Theta Boltzmann Machine
No full textWe show that the visible sector probability density function of the Riemann-Theta Boltzmann machine corresponds to a Gaussian mixture model consisting of an infinite number of component multi-variate Gaussians. The weights of the mixture are given by a discrete multi-variate Gaussian over the hidden state space. This allows us to sample the visible sector density function in a straight-forward manner. Furthermore, we show that the visible sector probability density function possesses an affine transform property, similar to the multi-variate Gaussian density
Perturbative QCD description of jet data from LHC Run-I and Tevatron Run-II
No full textWe 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 LFIC, 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
Parton Distribution Functions at LHC and the SMPDF web-based application
No full textWe present SMPDF Web, a web interface for the construction of parton distribution functions (PDFs) with a minimal number of error sets needed to represent the PDF uncertainty of specific processes (SMPDF).We present SMPDF Web, a web interface for the construction of parton distribution functions (PDFs) with a minimal number of error sets needed to represent the PDF uncertainty of specific processes (SMPDF)
Towards the determination of the photon parton distribution function constrained by LHC data
No full textWe 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
Towards the compression of parton densities through machine learning algorithms
No full textOne 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.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
- …
