Scheda programma d'esame
MONTECARLO METHODS IN EXPERIMENTAL PHYSICS
SERGIO GIUDICI
Academic year2023/24
CoursePHYSICS
Code185BB
Credits6
PeriodSemester 2
LanguageItalian

ModulesAreaTypeHoursTeacher(s)
METODI MONTECARLO NELLA FISICA SPERIMENTALEFIS/01LEZIONI36
SERGIO GIUDICI unimap
Obiettivi di apprendimento
Learning outcomes
Conoscenze

basi matematiche del campionamento statistico coem strumento per affrontare problemi computazionali.  Abilità nell'applicare le tecniche basate sui numeri pseudo-casuali nelle simulazioni di interesse in fisica. abilità nel mettere a punto algoritmi basati sui numer pseudo-casuali

Knowledge

Students are expected to acquire: The mathematical basis of statistical sampling as a tool for computational problem. Skillness in applying pseudo-random number techiniques for simulation in physics. Skill in the realization of algorithms based on pseudo-random number

Modalità di verifica delle conoscenze

Durante l'esame orale, lo studente deve dimostrare la capacità di realizzare una simulazione e organizzare una esposizione efficace dei risultati.

Metodi:

Esame orale finale Relazione scritta Ulteriori informazioni: Durante l'esame, lo studente deve essere in grado di dimostrare di conoscere le basi matematiche del metodo di campionamento statistico (peso del 50%) e deve essere in grado di presentare e discutere con adeguatezza espressiva un problema specifico (peso del 50%) assegnato dall'insegnante, tenendo conto degli interessi dello studente

Assessment criteria of knowledge

During the oral exam the student must demonstrate the ability to approach a circumscribed research problem, and organise an effective exposition of the results.

Methods:

  • Final oral exam
  • Written report

Further information:
During the exame student must be able to demonstrate to Know the mathematical basis of the statistical sampling method (50 % weight) and must be able to present and discuss with property of expression a specific problem (50% weight) assigned by the teacher taking into account the interests of the student.

Teaching methods

Delivery: face to face

Learning activities:

  • attending lectures
  • participation in seminar
  • preparation of oral/written report
  • individual study
  • group work

Attendance: Advised

Teaching methods:

  • Lectures
  • Seminar
  • project work
Syllabus

Statistical sampling, Von Neumann optimization Algorithm, Numerical integration, Application in High Energy Physics and Medical Physics: A study of cerenkov counter, simulation of a spectrometer, Bragg Peak simulation, positron range in water, delta rays simulation, simulation of limiting resolution effect in the case of PET (positron emission tomography). Since Montecarlo Methods play role in a wide range of application, students are invited to attend seminars illustrating how the methods is used in other Physics fields (Astro-physics, Astro-particle physics, bio-physics)

Bibliography

A. Rotondi et al. , "Probabilita`, Statistica e Simulazione " Ed. Springer. and various Articles taken form the Scientific literature.

Updated: 14/11/2023 16:36