Nuclear fission is an extreme process during which a heavy nucleus deforms until it reaches a point of no return leading to its separation into two fragments. The process goes with a significant release of energy, mainly as kinetic energy of the newly formed fragments, but also as excitation energy (about 15 MeV/fragment). In addition, the fragments are also produced with a high angular momentum. It is through the emission of neutrons and photons that fission fragments evacuate their energy and angular momentum. The ultimate experiment in fission would consist of identifying each fragment in mass and charge; measuring their kinetic energy; and characterize in energy and multiplicity the neutrons and photons they emit. This data set would make it possible to access the global energy of the fission process and to completely characterize the deexcitation of the fragments. Due to the significant complexity of such an exclusive measurement, this data set is always missing.

Our team is moving towards such measurement and this thesis work aims to explore the benefits that machine learning techniques can bring in this perspective.
The thesis will consist of taking advantage of all the experimentally accessible multi-correlated data in order to feed machine learning algorithms whose purpose will be to identify fission fragments and determine their properties.
The developed techniques will be applied to a first data set using a twin ionization chamber for the detection of fission fragments coupled to a set of neutron detectors. The data will be acquired at the beginning of the thesis.
In a second step, a more exploratory study will consist of applying the same techniques to data obtained during the thesis using a temporal projection chamber as a fission fragment detector. It will be a matter of demonstrating that the energy resolution is compatible with the study of fission.