DOMAIN: Technologies associated to the Portuguese participation at CERN and their transfer to society
SUPERVISOR: Rute Pedro
CO-SUPERVISOR: Amelia Maio
HOST INSTITUTION: Laboratório de Instrumentação e Física Experimental de Partículas
DEGREE INSTITUTION: Universidade de Lisboa
ABSTRACT
Currently, the field of Particle Physics is planning the next generation experiments with options for CERN-based accelerators, namely the FCC cicular collider, under consideration by the update of the European Strategy for collider HEP. *On the other hand*, the main technological challenges in the R&D for the future detectors are already identified and are input for the decision. Calorimeters are indispensable instruments to measure the energy of the collision products. For sampling hadronic calorimeters, choices relying on organic scintillators and wavelength-shifting (WLS) fibres read by photodetectors are successfull due to the low cost and are strong options for the future. Their operation under the expected harsher radiation conditions must meet crucial requirements of high light yield, fast response and radiation hardness. Although recent developments in organic scintillators/WLS indicate a breakthrough on light emission and time response, these emergent materials are lacking in R&D to scrutinise their radiation tolerance. This proposal includes R&D on the new organic scintillators/WLS, with the characterization of the light yield, attenuation length and resistance to ionising radiation. The work will be carried on at the LIP Laboratory of Optics and Scintillating Materials (LoMAC) and collaboration with national and CERN partners is expected. This research exploits also the current operation of the ATLAS Tile hadronic calorimeter to model the radiation damage of scintillators and WLS fibres using calibration data acquired in the real experimental environment. Several factors contribute to the total light output of scintillator+WLS fibre calorimeters, such as fibre length, scintillator plate/tile sizes, dose, dose rate and others. The plan will explore how these factors correlate with the light yield degradation using regression techniques based on modern machine learning and build tools to optimise the design of future detectors.