Understanding the basic building blocks of Nature has led to the Standard Model, a non abelian quantum field theory with particles and
glue that explains within a single framework the forces between the basic constituents of matter. However, the description of strongly
coupled emergent phenomena has remained a hard problem to solve, especially with traditional methods. In recent years, new techniques
have challenged this difficulty by showing that an optimized knowledge of symmetries and consistency conditions actually leads to
unprecedented quantitative results. Both the conformal and the amplitude bootstrap have proven this idea to be successful. A first objective
of the High-energy Intelligence -HeI- project is to extend the horizon of applicability of bootstrap methods by finding better constraints
and more rigorous predictions, eg. as path towards quantum chromodynamics (QCD) study the conformal window of QCD-like theories,
study integrable and supersymmetric theories, and for quantum gravity, study those theories that have a gravitational dual within string
theory. A second objective of the HeI project, specific and original, is to push the boundaries of our understanding of QCD physics, by
obtaining the most refined partonic distribution functions of quarks and gluons in nuclear matter. A third objective, timely and novel in
the proposed approach, is to combine an Artificial Intelligence and Machine Learning training with cutting-edge research in theoretical
physics, having in mind neural networks designs that can be trained on partial data sets, and at the same time, solve the non-perturbative
constraint equations coming from theory. The HeI project, for the first time, brings together many scientists working on related aspects
of high-energy physics but with different areas of specializations, to make a collaborative scientific breakthrough, through secondments
to leading research institutes in Brazil, Canada, Switzerland, and the Jefferson Laboratories. |
