A PATTERN RECOGNITION APPROACH TO LEARNING TRACKS OF HEAVY-ION PARTICLES IN TIMEPIX DETECTORS

The rapid development in semiconductor detector technology at CERN has provided the capability to develop an active personal dosimeter for use in space radiation environments. The work reported here is based on the Timepix chip, which when coupled with an Si sensor, can function as an active nuclear emulsion, allowing the visualization of the individual tracks created as the different incident particles traverse the detector. The Timepix chip provides the capability of measuring the energy deposited by each incident particle that traverses the sensor layer. Together with the capability for online readout, this detector opens the door to a completely new generation of active Space Radiation Dosimeters. Although recent advances in hardware technology promise a major step forward in the development of such active portable space radiation dosimeters, little effort has been devoted toward software tools for analysis and classification of sources of radiation. Coupling radiation dosimeter hardware with pattern recognition techniques and machine learning tools has the potential to greatly improve current applications on space dosimeter projects. Our focus is not only to measure dosimetric endpoints directly such as dose-equivalent, but also to determine the physical nature of the radiation field with sufficient precision to allow characterization of the radiation composition and energy spectrum.