Possibilità di tesi presso il CNAO Marco Pullia per conto del gruppo acceleratori Dottorato in Fisica “Sapienza” Università di Roma, 4 ottobre 2018 1

CNAO More precise, more efficient A centre dedicated to tumour treatment with protons and carbon ions located in Pavia IMRT Hadrontherapy More precise, more efficient Carbon ions Electrons Photons Pag. 2 4/10/2018 Implementation of RFKO Extraction System

Resonant multi-turn extraction Slow Extraction   A single turn extraction (<1μs) not possible Resonant multi-turn extraction Create stable area in phase space → slowly drive particles into resonance → long spill over many thousand turns Pag. 3 4/10/2018 Implementation of RFKO Extraction System

Extraction methods Beam can be driven to the resonance condition by three methods: Amplitude-momentum selection Amplitude selection RF-KO Q or Δp/p A (Δp/p)resonance separatrix (Δp/p)stack Un-stable region Stable region Q or Δp/p A (Δp/p)resonance separatrix (Δp/p)stack Q or Δp/p A (Δp/p)resonance separatrix (Δp/p)stack Un-stable region Stable region The beam is wide in momentum with respect to the stop band. The tune of the particles is then varied by accelerating them with a betatron core while leaving untouched the tune of the machine. Everything stays constant apart from the beam which is subject to a transverse RF perturbation (noise or single frequency swept through the betatron frequencies) that blows its emittance up. When the particle’s amplitude reaches the separatrix, they become unstable andare extracted. Not used at CNAO Pag. 4 4/10/2018 Implementation of RFKO Extraction System

Project aim RF-KO extraction implementation (both theoretical and hardware aspects) Installation and commissioning of the new extraction system Comparison between RF-KO and Amplitude-momentum systems Study of implementation of a multi-energy extraction process The work will be carried out in Pavia Pag. 5 4/10/2018 Implementation of RFKO Extraction System

Neural networks for accelerator control Accelerators should be able to run for extended periods of time with minimal interruptions. Traditional control techniques sometimes are not sufficient. One promising pathi s to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields.  The use of neural network-based modelling and control techniques could be of significant benefit to particle accelerators. Looking for a collaboration on the subject with groups with previous experience and a candidate with some preliminary knowledge to setup the study.

Thanks “The important thing is not to stop questioning” Albert Einstein