Can New Physics hide inside the proton?

Modern global analyses of the structure of the proton include collider measurements which probe energies well above the electroweak scale. While these provide powerful constraints on the parton distribution functions (PDFs), they are also sensitive to beyond the Standard Model (BSM) dynamics if these affect the fitted distributions. Here we present a first simultaneous determination of the PDFs and BSM effects from deep-inelastic structure function data by means of the NNPDF framework. We consider representative four-fermion operators from the SM Effective Field Theory (SMEFT), quantify to which extent their effects modify the fitted PDFs, and assess how the resulting bounds on the SMEFT degrees of freedom are modified. Our results demonstrate how BSM effects that might otherwise be reabsorbed into the PDFs can be systematically disentangled

The strangest proton?

Abstract: We present an improved determination of the strange quark and antiquark parton distribution functions of the proton by means of a global QCD analysis that takes into account a comprehensive set of strangeness-sensitive measurements: charm-tagged cross sections for fixed-target neutrino–nucleus deep-inelastic scattering, and cross sections for inclusive gauge-boson production and W-boson production in association with light jets or charm quarks at hadron colliders. Our analysis is accurate to next-to-next-to-leading order in perturbative QCD where available, and specifically includes charm-quark mass corrections to neutrino–nucleus structure functions. We find that a good overall description of the input dataset can be achieved and that a strangeness moderately suppressed in comparison to the rest of the light sea quarks is strongly favored by the global analysis

Parton distributions in the SMEFT from high-energy Drell-Yan tails

Abstract: The high-energy tails of charged- and neutral-current Drell-Yan processes provide important constraints on the light quark and anti-quark parton distribution functions (PDFs) in the large-x region. At the same time, short-distance new physics effects such as those encoded by the Standard Model Effective Field Theory (SMEFT) would induce smooth distortions to the same high-energy Drell-Yan tails. In this work, we assess for the first time the interplay between PDFs and EFT effects for high-mass Drell-Yan processes at the LHC and quantify the impact that the consistent joint determination of PDFs and Wilson coefficients has on the bounds derived for the latter. We consider two well-motivated new physics scenarios: 1) electroweak oblique corrections (Ŵ, Ŷ) and 2) four-fermion interactions potentially related to the LHCb anomalies in R(K(*)). We account for available Drell-Yan data, both from unfolded cross sections and from searches, and carry out dedicated projections for the High-Luminosity LHC. Our main finding is that, while the interplay between PDFs and EFT effects remains moderate for the current dataset, it will become a significant challenge for EFT analyses at the HL-LHC

An open-source machine learning framework for global analyses of parton distributions

Abstract: We present the software framework underlying the NNPDF4.0 global determination of parton distribution functions (PDFs). The code is released under an open source licence and is accompanied by extensive documentation and examples. The code base is composed by a PDF fitting package, tools to handle experimental data and to efficiently compare it to theoretical predictions, and a versatile analysis framework. In addition to ensuring the reproducibility of the NNPDF4.0 (and subsequent) determination, the public release of the NNPDF fitting framework enables a number of phenomenological applications and the production of PDF fits under user-defined data and theory assumptions

Precision QCD and effective field theories with machine learning

The Standard Model (SM) serves as one the best descriptions of fundamental physics we have and the quest for its falsification has led to it being tested to an unprecedented degree. Despite its flawless performance, there are many theoretical and phenomenological indications that the SM cannot be a complete description of nature; though, so far, no direct evidence for new physics at the TeV scale has been gathered at colliders. Far from being discouraging, the precision level reached by current experiments gives us the unique opportunity to investigate the effects of new particles whose masses are far above the TeV scale, but still produce observable effects at the scales within the direct kinematical reach of the Large Hadron Collider (LHC). Unlike for direct searches, which are limited by the energy reach of the collider, indirect searches are limited only by the theoretical and experimental control over the processes under inspection. A robust understanding of Quantum Chromodynamics (QCD) is crucial in order to achieve precision theoretical predictions in the era of initial state hadron colliders such as the LHC. An important ingredient therein are the parton distribution functions (PDFs) which parameterize the proton structure in terms of its elementary quark and gluon constituents. These quantities are non-perturbative and obtained from data using a global QCD analysis. In tandem, Effective Field Theories (EFTs), provide a convenient framework to capture the indirect effects of possible BSM resonances in low energy observables. Constraints on the EFT then translate to constraints on the nature of BSM physics. This manuscript serves to marry these two endeavours. We present machine learning-based approaches to PDF determination and specifically highlight how deep learning algorithms form ideal candidates to parameterize the PDFs in an unbiased fashion. We present the NNPDF4.0 PDF set which serves as the latest and most precise determination of proton structure delivered by such a methodology. We show how a precise determination of the PDFs has important consequences on LHC phenomenology by presenting a precision determination of the strange content of the proton and a number of key phenomenological applications. We then discuss the interplay between EFT dynamics and the PDFs; analysing the extent to which the fit of PDFs may absorb possible BSM signals and assess the implications a consistent treatment of PDFs in EFT fits has on phenomenological studies. For this, we use legacy deep inelastic scattering data from HERA and later some more modern measurements from high-mass Drell-Yan observables at the Large Hadron Collider (LHC) to investigate the back-reaction of EFT dynamics on the PDFs. The considerations presented in the above study then act as an impetus to develop a methodology that is capable of simultaneously determining proton structure alongside BSM dynamics in a consistent framework. We present a novel methodology, SIMUnet, which delivers a robust and accurate determination of PDFs and general theory parameters, of which BSM dynamics are a subset. We show how this state-of-the-art methodology can, for the first time, extract and disentangle the PDFs from BSM dynamics from a global dataset paving the way for a truly global and simultaneous interpretation of indirect searches in the context of precision physics

The strangest proton?

Abstract: We present an improved determination of the strange quark and antiquark parton distribution functions of the proton by means of a global QCD analysis that takes into account a comprehensive set of strangeness-sensitive measurements: charm-tagged cross sections for fixed-target neutrino–nucleus deep-inelastic scattering, and cross sections for inclusive gauge-boson production and W-boson production in association with light jets or charm quarks at hadron colliders. Our analysis is accurate to next-to-next-to-leading order in perturbative QCD where available, and specifically includes charm-quark mass corrections to neutrino–nucleus structure functions. We find that a good overall description of the input dataset can be achieved and that a strangeness moderately suppressed in comparison to the rest of the light sea quarks is strongly favored by the global analysis

The path to proton structure at 1% accuracy

Abstract: We present a new set of parton distribution functions (PDFs) based on a fully global dataset and machine learning techniques: NNPDF4.0. We expand the NNPDF3.1 determination with 44 new datasets, mostly from the LHC. We derive a novel methodology through hyperparameter optimization, leading to an efficient fitting algorithm built upon stochastic gradient descent. We use NNLO QCD calculations and account for NLO electroweak corrections and nuclear uncertainties. Theoretical improvements in the PDF description include a systematic implementation of positivity constraints and integrability of sum rules. We validate our methodology by means of closure tests and “future tests” (i.e. tests of backward and forward data compatibility), and assess its stability, specifically upon changes of PDF parametrization basis. We study the internal compatibility of our dataset, and investigate the dependence of results both upon the choice of input dataset and of fitting methodology. We perform a first study of the phenomenological implications of NNPDF4.0 on representative LHC processes. The software framework used to produce NNPDF4.0 is made available as an open-source package together with documentation and examples