Parton Distributions in the Higgs Boson Era

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.