1 sujet /DACM/LEDA

Dernière mise à jour : 06-12-2021


 

ADVANCED AND ARTIFICIAL INTELLIGENCE TECHNIQUES TO MITIGATE LINEAR AND NON-LINEAR IMPERFECTIONS IN FUTURE CIRCULAR COLLIDERS

SL-DRF-22-0514

Research field : Accelerators physics
Location :

Département des Accélérateurs, de Cryogénie et de Magnétisme (DACM)

Laboratoire d’Etudes et de Développements pour les Accélérateurs (LEDA)

Saclay

Contact :

Barbara Dalena

Starting date : 01-10-2022

Contact :

Barbara Dalena
CEA - DRF/IRFU/DACM


Thesis supervisor :

Barbara Dalena
CEA - DRF/IRFU/DACM


Personal web page : http://dalena.web.cern.ch/dalena/

Laboratory link : http://irfu.cea.fr/dacm/index.php

After the discovery of the Higgs boson at the LHC, particle physics community is exploring and proposing next accelerators, to address the remaining open questions on the underlying mechanisms and on the constituents of the present universe. One of the studied possibilities is FCC (Future Circular Collider), a 100-km-long collider at CERN. The hadron version of FCC (FCC-hh) seems to be the only approach to reach energy levels far beyond the range of the LHC, in the coming decades, providing direct access to new particles with masses up to tens of TeV. The electron version of FCC brings a tremendous increase of production rates for phenomena in the sub-TeV mass range, making precision physics studies possible. A first study has shown no major showstopper in the colliders’ feasibility but has identified several specific challenges for the beam dynamics: large circumference (civil engineering constraints), beam stability with high current, the small geometric emittance, unprecedented collision energy and luminosity, the huge amount of energy stored in the beam, large synchrotron radiation power, plus the injection scenarios. This thesis will focus on the optimization of the hadron option of the future circular collider against linear and non-linear imperfections (i.e. magnets alignments and their field quality). A key point of this thesis is the comparison of current advanced correction schemes to techniques based on machine learning. The application of these techniques to accelerators is one of current hot topics in the field and pursued worldwide.

• Accelerators physics

 

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