Vascelli, Francesco
 
(2023)
Study of the impact of out-of-target fragmentation in cross section measurements with the FOOT experiment.
[Laurea magistrale], Università di Bologna, Corso di Studio in 
Physics [LM-DM270]
   
  
  
        
        
	
  
  
  
  
  
  
  
    
  
    
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      Abstract
      Hadrontherapy is an innovative cancer treatment technique that utilizes beams of charged
particles, specifically protons and heavier ions, to target tumors deep within the body.
These particles have a unique depth-dose distribution in tissue, with low doses at the
entry point and a sharp peak of maximum dose (Bragg peak) near the end
of their path. This makes them effective at targeting tumors while minimizing damage
to surrounding healthy tissues. Carbon and oxygen ions, in particular, have been found
to have an enhanced biological effect, making them useful for treating tumors. However, it is important to consider nuclear interactions in these treatments. As beam particles travel through the body, they can fragment and release
dose beyond the Bragg peak. Additionally, fragments of nuclei present in the human
body can modify the dose released in healthy tissues. The lack of comprehensive data on
cross sections makes it difficult to predict the effects of these interactions. To fill these
gaps in experimental data, the FOOT experiment has been designed to detect, track
and identify charged fragments produced in ion collisions with different targets. Its final
goal is to measure double differential cross sections both in angle and kinetic energy with
an uncertainty lower than 5%. However, cross section measurements can be spoiled by
out-of-target fragmentations that could occur anywhere along the experimental apparatus, such as in the detectors’ planes or in the air surrounding them. In this thesis, a
straightforward algorithm for event classification, based on the zone of fragmentation,
is presented and studied. First, the accuracy of the classification is analyzed to ensure
the proper functioning of the algorithm. Then, the effectiveness of a selection on events,
based on the classification algorithm, is evaluated through the study of reconstructed
tracks. To do so, a sample of simulated data of oxygen at 400 MeV/n impinging on a target
of graphite is employed.
     
    
      Abstract
      Hadrontherapy is an innovative cancer treatment technique that utilizes beams of charged
particles, specifically protons and heavier ions, to target tumors deep within the body.
These particles have a unique depth-dose distribution in tissue, with low doses at the
entry point and a sharp peak of maximum dose (Bragg peak) near the end
of their path. This makes them effective at targeting tumors while minimizing damage
to surrounding healthy tissues. Carbon and oxygen ions, in particular, have been found
to have an enhanced biological effect, making them useful for treating tumors. However, it is important to consider nuclear interactions in these treatments. As beam particles travel through the body, they can fragment and release
dose beyond the Bragg peak. Additionally, fragments of nuclei present in the human
body can modify the dose released in healthy tissues. The lack of comprehensive data on
cross sections makes it difficult to predict the effects of these interactions. To fill these
gaps in experimental data, the FOOT experiment has been designed to detect, track
and identify charged fragments produced in ion collisions with different targets. Its final
goal is to measure double differential cross sections both in angle and kinetic energy with
an uncertainty lower than 5%. However, cross section measurements can be spoiled by
out-of-target fragmentations that could occur anywhere along the experimental apparatus, such as in the detectors’ planes or in the air surrounding them. In this thesis, a
straightforward algorithm for event classification, based on the zone of fragmentation,
is presented and studied. First, the accuracy of the classification is analyzed to ensure
the proper functioning of the algorithm. Then, the effectiveness of a selection on events,
based on the classification algorithm, is evaluated through the study of reconstructed
tracks. To do so, a sample of simulated data of oxygen at 400 MeV/n impinging on a target
of graphite is employed.
     
  
  
    
    
      Tipologia del documento
      Tesi di laurea
(Laurea magistrale)
      
      
      
      
        
      
        
          Autore della tesi
          Vascelli, Francesco
          
        
      
        
          Relatore della tesi
          
          
        
      
        
          Correlatore della tesi
          
          
        
      
        
          Scuola
          
          
        
      
        
          Corso di studio
          
          
        
      
        
          Indirizzo
          NUCLEAR AND SUBNUCLEAR PHYSICS
          
        
      
        
      
        
          Ordinamento Cds
          DM270
          
        
      
        
          Parole chiave
          hadrontherapy,nuclear cross section,FOOT,nuclear fragmentation
          
        
      
        
          Data di discussione della Tesi
          31 Marzo 2023
          
        
      
      URI
      
      
     
   
  
    Altri metadati
    
      Tipologia del documento
      Tesi di laurea
(NON SPECIFICATO)
      
      
      
      
        
      
        
          Autore della tesi
          Vascelli, Francesco
          
        
      
        
          Relatore della tesi
          
          
        
      
        
          Correlatore della tesi
          
          
        
      
        
          Scuola
          
          
        
      
        
          Corso di studio
          
          
        
      
        
          Indirizzo
          NUCLEAR AND SUBNUCLEAR PHYSICS
          
        
      
        
      
        
          Ordinamento Cds
          DM270
          
        
      
        
          Parole chiave
          hadrontherapy,nuclear cross section,FOOT,nuclear fragmentation
          
        
      
        
          Data di discussione della Tesi
          31 Marzo 2023
          
        
      
      URI
      
      
     
   
  
  
  
  
  
    
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