Discrimination between Light Dark Matter and Neutrino interactions at the SHiP experiment using Machine Learning algorithms


  • Call:

    IDPASC Portugal - PHD Programme 2019

  • Academic Year:

    2019 / 2020

  • Domain:

    Experimental Particle Physics

  • Supervisor:

    Celso Franco

  • Co-Supervisor:

    Nuno Leonardo

  • Institution:

    Instituto Superior Técnico

  • Host Institution:

    Laboratório de Instrumentação e Física Experimental de Partículas

  • Abstract:

    The SHiP (Search for Hidden Particles) experiment at CERN is being proposed as a discovery experiment. It is designed to detect extremely feebly interacting, relatively light and long lived particles, which are predicted to exist in the so-called Hidden Sector of Particle Physics. However, in parallel, a rich program of Neutrino Physics is also being prepared. SHiP, as a Beam Dump Facility, will produce an enormous amount of neutrinos and photons in its primary target (over a period of 5 years): above 7x10^18 and 1x10^20, respectively. The spectrometer will be equipped with an emulsion detector, to detect neutrino interactions and, due to its combination with a muon detector, it will be possible to distinguish all six neutrinos/anti-neutrinos. Therefore, SHiP will detect for the first time interactions with tau anti-neutrinos and will provide unique data concerning neutrino-induced charm production. The knowledge about the structure of the proton will also be significantly improved as a result of probing the proton with neutrino DIS (Deep Inelastic Scattering) events. In addition, due to the micrometric precision of the emulsion detector, SHiP has the potential of separating Light Dark Matter (LDM), arising from dark photon (coupling to photons) decays, from neutrino interactions in the emulsion detector. The work plan involves the use of Machine Learning algorithms with the goal of optimising the spectrometer to maximise the separation between LDM and neutrino interactions, without compromising the SHiP exploration of the Hidden Sector. These algorithms will also be used at the analysis level, both at the low (reconstruction) and high (process selection) levels of analysis, to maximise both the efficiency and purity of each Physics sample. The selected student will be integrated as a member of the SHiP Collaboration and trips to CERN are expected.