Event reconstruction and background modelling in the LZ Dark Matter experiment


  • Call:

    IDPASC Portugal - PHD Programme 2017

  • Academic Year:

    2017 / 2018

  • Domains:

    Experimental Particle Physics | Astroparticle Physics

  • Supervisor:

    Vladimir Solovov

  • Co-Supervisor:

    Claudio Frederico Pascoal da Silva

  • Institution:

    Universidade de Coimbra

  • Host Institution:

    Laboratório de Instrumentação e Física Experimental de Partículas

  • Abstract:

    A major goal of the modern experimental astrophysics is to find a direct proof to the existence of dark matter and an indication to its exact physical nature. The LUX-ZEPLIN (LZ) detector, currently under construction, will employ 10 tons of liquid xenon as a target for the hypothetical dark matter candidate - weakly interacting massive particles (WIMPs). It is expected to improve the current best WIMP cross section limit by two orders of magnitude. LZ detector will be sensitive to other rare processes (for example double beta decay), and as such may contribute to the areas of neutrino and nuclear physics as well. The search for rare events can be compared with the proverbial needle in a haystack problem as the events of interest must be separated from huge amount of background ones. One of the main methods of background rejection is by event position, as great majority of background event occur near detector walls. In this project the student will work on two topics extremely important for analysis of the experimental data: - technique for precise reconstruction of event position, - realistic model of the spatial distribution of the background events. The work, performed in collaboration with leading American and UK research institutions (LZ Collaboration) will be based on the currently available data from the LUX experiment and has potential to have considerable impact on the overall performance of the LZ data analysis. The student will become familiar with: Nuclear and astroparticle physics; State-of-the-art data processing techniques; High-performance Monte Carlo simulation; Use of machine learning algorithms for data analysis and learn to work in dynamic and collaborative environment through the use of the modern team communication and development tools.