Master Projects Juan Rojo VU Amsterdam & Theory group, Nikhef - - PowerPoint PPT Presentation

Juan Rojo VU Amsterdam & Theory group, Nikhef

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Master Projects

for more information:

https://juanrojo.com/vacancies/

https://inspirehep.net/authors/1019897

current group: 2 postdocs, 1 PhD student (2 more joining in Oct), several master students

My research in a nutshell

Proton substructure from machine learning embedded in Nikhef’s Theory Group, close connection with experimental groups Effective Theories and model-independent searches for new physics precision calculation

• f LHC processes

Machine Learning algorithms for and beyond particle physics Astroparticle physics & neutrino telescopes

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Bottom-up approach to new physics beyond the Standard Model (BSM)

The EFT pathway to New Physics

Effective Field Theories parametrise the space of possible BSM theories in terms of higher-dimensional operators to be constrained from data

Quark and gluon substructure of nucleons and nuclei determine the initial-state of proton and heavy-ion collisions Determine in a model-independent manner by means of deep learning techniques (n)PDFs also required for ultra-high-energy astrophysics

g(x, Q0, A) =

x ln 1/x

ξ(3)

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ξ(1)

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ξ(1)

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ξ(2)

1

ξ(2)

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ξ(2)

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A

ξ(1)

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Bgx−αg(1 − x)βg ξ(3)

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nNNPDF1.0

ξ(3)

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ξ(3)

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Σ(x, Q0, A) = x−αΣ(1 − x)βΣ ξ(3)

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T8(x, Q0, A) = x−αT8(1 − x)βT8 ξ(3)

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Proton and nuclear structure from machine learning

Machine Learning for Material Science

Recent instrumentation progress in electron microscopy (EM) characterisation of quantum materials requires the deployment of machine learning algorithms for data interpretation Apply our HEP-based ML expertise to the EM analysis of nanomaterials in collaboration with researches at the Kavli Institute of Nanosience Delft