Improving Energy Reconstruction and Background Rejection of EXO-200
Final Report Abstract
This project addressed several aspects of EXO-200 data analysis and simulation that could in principle lead to an improvement of energy resolution or background reduction and thus to sensitivity improvements to detect neutrinoless double beta decay with EXO-200. Due to a gain in understanding of the properties of liquid xenon detectors with this project, it also contributes indirectly to the next generation liquid xenon neutrinoless double beta decay experiment nEXO. We have improved the simulation of the EXO-200 detector by investigating the origin of slight discrepancies between simulation and measurement that occur at the mantle edge of the EXO-200 TPC and by generating a more detailed and higher dimensional electric field map for simulating electron drift. We investigated the influence of electron drift onto the recombination of electrons with xenon ions after high-energy electron impact in liquid xenon. We found - in experiment and simulation - slight indications that the recombination yield is slightly increased for electron tracks starting perpendicularly to the electric field lines which is in contrast to the naïve expectation following the principle of columnar recombination. Unfortunately we have only hints and are not able to draw a definitive conclusion due to the limited data base and light resolution of EXO-200. Nevertheless, this is an interesting hint that deserves further investigation. We measured the angular dependent reflectivity of the scintillation light detectors (large area photodiodes) in liquid xenon in a dedicated setup at the University of Muenster. These results can be used to improve the light simulation of EXO-200. In this project we have extensively used several Deep Neural Networks and managed to improve energy resolution and background suppression with real data. Thus, we were able to improve the sensitivity to neutrinoless double beta decay of EXO-200 compared to the traditional EXO-200 analysis. Furthermore, we investigated the use of General Adversarial Networks to refine the EXO-200 Monte Carlo simulation.
Publications
- “Deep Neural Networks for Energy and Position Reconstruction in EXO-200”. In: JINST 13 (2018), P08023
S. Delaquis et al.
(See online at https://doi.org/10.1088/1748-0221/13/08/P08023) - “Reflectance of VUV-sensitive SiPM surfaces in liquid xenon”. In: Nucl. Instrum. Meth. A 936 (2019)
M. Wagenpfeil, T. Ziegler et al.
(See online at https://doi.org/10.1016/j.nima.2018.09.142) - “Search for Neutrinoless Double-β Decay with the Complete EXO-200 Dataset”. In: Phys. Rev. Lett. 123 (2019), p. 161802
G. Anton et al.
(See online at https://doi.org/10.1103/PhysRevLett.123.161802)
