Vacancies

Here we list possible job opportunities within my group, as well as available bachelor and master projects.

Master projects UvA/VU Physics & Astronomy 2019-2020

The Effective Field Theory Pathway to New Physics at the LHC

A very promising framework to parametrise in a robust and model-independent way deviations from the Standard Model (SM) induced by new heavy particles is the Standard Model Effective Field Theory (SMEFT). In this formalism, Beyond the SM effects are encapsulated in higher-dimensional operators constructed from SM fields respecting their symmetry properties. In this project, we aim to carry out a global analysis of the SMEFT from high-precision LHC data, including Higgs boson production, flavour observables, and low-energy measurements. This analysis will be carried out in the context of the recently developed SMEFiT approach [1] based on Machine Learning techniques to efficiently explore the complex theory parameter space. The ultimate goal is either to uncover glimpses of new particles or interactions at the LHC, or to derive the most stringent model-independent bounds to date on general theories of New Physics.

[1] https://arxiv.org/abs/1901.05965

Pinning down the initial state of heavy-ion collisions with Machine Learning

It has been known for more than three decades that the parton distribution functions (PDFs) of nucleons bound within heavy nuclei are modified with respect to their free-nucleon counterparts. Despite active experimental and theoretical investigations, the underlying mechanisms that drive these in-medium modifications of nucleon substructure have yet to be fully understood. The determination of nuclear PDFs is a topic of high relevance in order both to improve our fundamental understanding of the strong interactions in the nuclear environment, as well as and for the interpretation of heavy ion collisions at RHIC and the LHC, in particular for the characterization of the Quark-Gluon Plasma. The goal of this project is to exploit Machine Learning and Artificial Intelligence tools [1,2] (neural networks trained by stochastic gradient descent) to pin down the initial state of heavy ion collisions by using recent measurements from proton-lead collisions at the LHC. Emphasis will be put on the poorly-known nuclear modifications of the gluon PDFs, which are still mostly terra incognita and highly relevant for phenomenological applications. In addition to theory calculations, the project will also involve code development using modern AI/ML tools such as TensorFlow and Keras.

[1] https://arxiv.org/abs/1811.05858 [2] https://arxiv.org/abs/1410.8849

The High-Energy Muon Crisis and Perturbative QCD

The production of charmed meson from the collision of high-energy cosmic rays with air nucleons in the upper atmosphere provides an important component of the flux of high-energy muons and neutrinos that can be detected at cosmic ray experiments such as AUGER and neutrino telescopes such as KM3NET or IceCube. The production of forward muons from charmed meson decays is usually predicted from QCD models tuned to the data, rather than from first principles QCD calculation. Interestingly, the number of such high-energy muons observed by AUGER seems to differ markedly from current theory predictions. In this project we aim to exploit state-of-the-art perturbative and non-perturbative QCD techniques to compute the flux of high-energy muons from charm decays and make predictions for a number of experiments sensitive to them


[1] https://arxiv.org/abs/1904.12547 [2] https://arxiv.org/abs/1808.02034 [3] https://arxiv.org/abs/1511.06346