Thesis

Disentangling and Quantifying Jet-Quenching With Generative Deep Learning

• Call:

  PT-CERN Call 2021/1

• Academic Year:

  2021

• Domain:

  Astroparticle Physics

• Supervisor:

  José Guilherme Milhano

• Co-Supervisor:

• Institution:

  Instituto Superior Técnico (Universidade de Lisboa)

• Host Institution:

  Laboratório de Instrumentação e Física Experimental de Partículas

• Abstract:

  At very high energies or densities, quarks and gluons can produce a new state of matter known as Quark-Gluon Plasma (QGP), which is observed in heavy-ion collisions at modern colliders. Due to the non-perturbative nature of Quantum Chromodynamics (QCD), which is responsible for the interactions and dynamics of quarks and gluons, it remains a challenge to fully describe the QGP. One way of probing its nature is to study the properties of jets initiated inside the QGP medium with which they will interact at early times, a phenomenon known as Jet-Quenching. The interaction between a jet and the QGP will considerably change the jet's properties and characteristics when compared to jets initiated in the vacuum, such as those happening in proton-proton collisions, making quenched jets an ideal probe into the QGP. The study of quenched jets provides a comprehensive testbed to develop data-driven methods with wide applicability to LHC analyses. Of particular interest, are recent developments of generative methods, such as Generative Adversarial Networks, which can harness the underlying statistical distribution of the data with a lower-dimensional statistical parameterization. In this space, we will be exploring how we can identify the degrees of freedom responsible for the phenomenon of Jet Quenching, and quantify it, which could enable the development of a viable quenched jet tagger. Furthermore we will extend and optimize the techniques as a generic analysis strategy in High Energy Physics that can connect explicitly purely data-driven quantities with theory calculations.