Measurement of electron fluxes in a Low Earth Orbit with SATRAM and comparison to EPT data

AbstraktAbstract

We present the determination of electron fluxes measured by the Space Application of Timepix Radiation Monitor (SATRAM), a pixelated single-layer particle detector and the comparison with the Energetic Particle Telescope (EPT), a science-class radiation spectrometer. Both are attached to the Proba-V satellite of the European Space Agency. SATRAM hosts a Timepix chip with a 300 µm thick silicon sensor divided into a 256 × 256 pixel matrix with 55 µm pixel pitch. Simulations were conducted to determine the geometric factor of the sensor and the effective area that includes the shielding effects from all directions. The simulation was further used to study the influence of secondary particle production, track interruption, and backscattering on the number of detected particles. Particle identification is performed using two different methods. The first method requires that particle tracks are individually identifiable. A neural network was developed for this purpose, achieving an accuracy of 90.2% for particle identification. If individual particle tracks could not be identified, a statistical approach was used utilizing the energy deposition, average cluster energy, and the last known fraction of electrons. A comparison of the two instruments shows good agreement within one order of magnitude for the majority of the data.

We present the determination of electron fluxes measured by the Space Application of Timepix Radiation Monitor (SATRAM), a pixelated single-layer particle detector and the comparison with the Energetic Particle Telescope (EPT), a science-class radiation spectrometer. Both are attached to the Proba-V satellite of the European Space Agency. SATRAM hosts a Timepix chip with a 300 µm thick silicon sensor divided into a 256 × 256 pixel matrix with 55 µm pixel pitch. Simulations were conducted to determine the geometric factor of the sensor and the effective area that includes the shielding effects from all directions. The simulation was further used to study the influence of secondary particle production, track interruption, and backscattering on the number of detected particles. Particle identification is performed using two different methods. The first method requires that particle tracks are individually identifiable. A neural network was developed for this purpose, achieving an accuracy of 90.2% for particle identification. If individual particle tracks could not be identified, a statistical approach was used utilizing the energy deposition, average cluster energy, and the last known fraction of electrons. A comparison of the two instruments shows good agreement within one order of magnitude for the majority of the data.