Parton Distributions in the Higgs Boson Era

Lead Research Organisation: University of Oxford
Department Name: Oxford Physics

Abstract

The Large Hadron Collider (LHC) is the most powerful particle accelerator ever build by mankind: in its 30 km long tunnel below Geneva in Switzerland, protons are accelerated to almost the speed to light and then collide. These high-energy collisions generate a state similar in terms of energy and temperature to what happened shortly after the Big Bang. In these extreme conditions, physicists study the laws of nature at the smallest distances ever probed, and try to answer some of the ultimate questions that man has always wondered about, like the origin of the universe, the nature of forces and the quest for the ultimate constituents of matter.

With the recent discovery of a Higgs-like boson at the LHC, particle physics has entered a new era. The challenge now is to understand in detail the properties of this new particle, and in particular, to assess whether it is the boson that completes the Standard Model of particle physics or if, on the other hand, it has a different nature, for example, if it is a supersymmetric particle. In addition, the LHC will continue the search for even more exotic heavy particles at the highest energies ever probed, scrutinizing for new forces or extra dimensions. The LHC program has also profound connections with exciting open problems in astronomy and cosmology, for example, some theories predict that dark matter particles can be produced and studied at the LHC, which would have enormous implications for our understanding of the universe formation and evolution. It is therefore of utmost importance to provide extremely accurate theoretical predictions for the most relevant processes at the LHC, including Higgs production, as well as for a variety of new physics scenarios.

Crucial ingredients of these predictions are Parton Distribution Functions (PDFs), which encode the dynamics determining how the proton's energy is split among its constituents, quarks and gluons, in each LHC collision. PDFs are intrinsically non-perturbative, and thus need to be extracted from data. In the last years, I have developed a novel approach to PDF determination based on artificial neural networks, machine learning techniques and genetic algorithms, so that PDFs are "learnt" from the wide variety of experimental data without the need of imposing a prior theory, just as we know how to score a penalty without the need of solving Newton's equations of motion, and they "adapt" towards the physically more meaningful solution.

In this project, we will use the most updated experimental data from the LHC, together with the best theoretical information available, to impose stringent constraints and achieve extremely precise PDF determinations, which will then improve our prospects to better characterize the Higgs boson properties and to search for new heavy particles. We will also provide PDFs specific for Monte Carlo "event generators", software tools that allow simulating real LHC collisions with a remarkable degree of accuracy. These improved neural network PDFs will also be used to devise new ways of measuring LHC processes with enhanced sensitivity to yet unknown heavy particles.

In summary, this project aims to fully exploit the LHC potential to achieve the ultimate experimental and theoretical precision in the determination of parton distributions to make essential contributions to our understanding of the laws of nature at the TeV scale at the LHC.

Publications

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Ball R (2015) Parton distributions for the LHC run II in Journal of High Energy Physics

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Ball R (2016) Intrinsic charm in a matched general-mass scheme in Physics Letters B

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Ball RD (2016) The asymptotic behaviour of parton distributions at small and large . in The European physical journal. C, Particles and fields

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Ball RD (2016) A determination of the charm content of the proton: The NNPDF Collaboration. in The European physical journal. C, Particles and fields

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Beenakker W (2016) NLO+NLL squark and gluino production cross sections with threshold-improved parton distributions. in The European physical journal. C, Particles and fields

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Behr JK (2016) Boosting Higgs pair production in the [Formula: see text] final state with multivariate techniques. in The European physical journal. C, Particles and fields

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Bertone V (2014) aMCfast: automation of fast NLO computations for PDF fits in Journal of High Energy Physics

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Bonvini M (2015) Parton distributions with threshold resummation in Journal of High Energy Physics

 
Description I have developed a novel strategy to determine with high-precision the structure of the proton. This strategy includes the use of experimental data from the Large Hadron Collider, as well as new state-of-the-art calculations in Quantum Field Theory. I have explored the unexpected connections between collider physics and astrophysical measurements, with new calculations relevant for the cosmological interpretation of the high-energy events at IceCube, the neutrino telescope based in the South Pole. In addition, In addition, I have developed a number of advanced statistical techniques, Multivariate Analysis, Artificial Neural Networks and Genetic Algorithms with the potential for applications in industrial fields related to Data Science, such as targeted marketing or forecasting.
Exploitation Route The parton distribution functions of the proton that I have determined will be used extensively the the community of high-energy physics exploiting the Large Hadron Collider, and will thus provide a unique contribution to the characterization of the Higgs sector and the search for new particles in the energy frontier. In addition, the advanced statistical techniques that I have developed, in particular the Multivariate Analysis methods, have potential applications in industrial fields related to Data Science, such as targeted marketing or forecasting.
Sectors Education,Financial Services, and Management Consultancy

 
Description The developement of the statistics techniques that lie at the core of this project has direct relation with applications in many other sectors, from big data managing to machine learning. For example, I am part of an European Union Marie Curie Initial Training Network (ITN) AMVA4NewPhysics, which aims to use advanced multivariate techniques for the exploitation of the LHC physics program, and to explore how this know can be extended to industrial sectors. This network has a very active blog, to which I am an active contributor https://amva4newphysics.wordpress.com/ where general public can learn and ask questions about how advanced multivariate techniques can affect and improve various aspects of our lives.
First Year Of Impact 2015
Sector Education
Impact Types Cultural

 
Description AMVA4NewPhysics Marie Curie Initial Training Network of the European Comission 
Organisation European Commission
Country European Union (EU) 
Sector Public 
PI Contribution Exploitation of advanced multivariate techniques for the search for New Physics at the Large Hadron Collider
Collaborator Contribution General organisation of the network and research activities.
Impact .
Start Year 2015