DISCOVERY@LHC

Precision Parton Distributions for New Physics Discoveries at the Large Hadron Collider

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 Obiettivo del progetto (Objective)

'In the next few years very significant progress is expected in the understanding of fundamental interactions, due to information coming from experiments at the LHC collider at CERN. The LHC will pose an unprecedented challenge: to discover new physical phenomena using methods which have so far been used for precision studies of phenomena which are already known. This challenge requires a radical rethinking of the way one arrives at predictions for collider processes. There have been recent attempts at collecting all the available information on important LHC processes (such as Higgs, W or top production): these studies have invariably found that theoretical and phenomenological uncertainties had been previously underestimated, that the impact of QCD corrections is very significant, and that the dominant uncertainty comes from ignorance of the structure of the nucleon. The goal of this project is to provide such a rethinking, starting from the bottom up, i.e. starting from a new determination of the quark and gluon substructure of the nucleon, as encoded in parton distributions (PDFs). These new PDFs, the NNPDF sets, are obtained using a completely new approach, based on neural networks combined with a Monte Carlo technique. This approach will provide not only a theoretically and phenomenologically reliable and consistent set of PDFs, but also a flexible tool which can be used to assess the impact of various theoretical ingredients (such as higher order perturbative corrections and their resummation) and of the new experimental information obtained at the LHC. The corresponding studies will be the main result of this project: we will be able to determine the combined impact of the wealth of theoretical and experimental information which has been accumulating over the last several years, and to use it for the discovery of New Physics at the LHC.'

Introduzione (Teaser)

With a Nobel prize awarded for the discovery of a Higgs-like boson, the main challenge now is to understand its properties in detail. Parton distribution functions (PDFs) play a vital role in this, and in the prediction of new physics scenarios.

Descrizione progetto (Article)

Vast quantities of collisions and interactions take place in the Large Hadron Collider (LHC) at the European organisation for nuclear research (also called CERN) in Switzerland. Data analysis of these events is crucial for the discovery of new particles. PDFs are tools used to encode the dynamics that determine how the energy of a proton is split among its constituents in each collision.

These PDFs have to be extracted from the experimental data. The EU-funded project, 'Precision parton distribution for new physics discoveries at the Large Hadron Collider' (DISCOVERY@LHC), has devised a novel approach to doing so.

The new method uses artificial neural networks, machine learning techniques and genetics algorithms to extract the PDFs from experimental data. This obviates the need for the imposition of a prior theory.

The project researcher published the first PDF set to include the direct constraints from the LHC data, denoted Neural Network Parton Distribution Functions NNPDF2.3. Other scientists working at the LHC have increasingly started to use these NNPDF sets.

Later in 2013, the researcher presented the first NNPDF set with quantum electrodynamics effects. The project has achieved its principal goal of providing a new generation of parton distributions for use in the study of LHC phenomena.