ETD

Digital archive of theses discussed at the University of Pisa

 

Thesis etd-08252021-162134


Thesis type
Tesi di laurea magistrale
Author
GIOVANNELLI, TOMMASO
URN
etd-08252021-162134
Thesis title
Unveling Fermi unidentified sources with machine learning
Department
FISICA
Course of study
FISICA
Supervisors
relatore Razzano, Massimiliano
Keywords
  • Fermi
  • machine learning
  • gamma-ray sources
Graduation session start date
15/09/2021
Availability
None
Summary
The study of astrophysical sources of gamma rays can be very useful for analyzing the behavior of matter in extreme conditions, which are difficult to reproduce in the laboratory.
The Fermi telescope is currently the most sensitive gamma-ray telescope in orbit. Its technological improvements have led to a large increase in the number of detected sources.
However, in many cases these sources are still unidentified, i.e. we do not know what its nature is.
The identification process requires long and expensive observation campaigns, which can however be accelerated using new data analysis techniques, such as Machine Learning.
In this thesis we therefore analyze the potential and limits of various Machine Learning techniques for the classification of the unidentified sources of the Fermi telescope.
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