Multipoint scintillation detectors are used to measure the dose of radiation deposited simultaneously at many locations in space and they have the advantage to allow real-time measurements. However, this detector must be precisely calibrated to provide accurate dose measurements.

The goal of this project is to develop an automated routine for the calibration of multipoint scintillation detectors under the beam of a linear accelerator such as the ones used for cancer treatments, by representing the calibration data in the principal component space.

A multipoint scintillation detector measures the spectrum of the light produced within the detector. Indeed, light is produced within the detector proportional to the radiation deposited in the detector. From a calibration dataset, a Non-Negative Matrix Factorisation algorithm (NMF) is used with the aim to retrieve the pure spectral components of the measurements. To simplify the visualization of the calibration dataset, the dataset is transformed using the Principal Component Analysis algorithm (PCA), and this transformed dataset is then represented graphically in the principal component space. This space allows to visualize the spectral composition of the data, relative to the pure spectra.

Many datasets can therefore be built, represented into this space, and used with the NMF algorithm with the aim to evaluate the performance of this algorithm for different calibration datasets.

In the end, this will allow to determine the experimental datasets that have to be acquired to perform an accurate calibration of the multipoint scintillation detectors.