The EXO-200 experiment
EXO-200 searches for the neutrinoless double beta decay of 136Xe with at a Q-value of 2458 keV. The detector consists of a single-phase time projection chamber filled with ca. 200 kg of liquid xenon enriched to 80 % in 136Xe. The discovery of this hypothetical decay implies physics beyond the Standard Model of particle physics and would expand the knowledge of the neutrino nature. The EXO-200 experiment is located underground in the Waste Isolation Pilot Plant (WIPP), NM, USA. The detector is shown in the figure during commissioning.
If an event occurs within the detector, ionizing particles deposit their energy in the liquid xenon. Released electrons from the xenon atoms may recombine and produce UV scintillation light which is detected by APDs. If they do not recombine, they drift in an electric field towards the end caps of the detector where the induced current is measured by two crossed wire planes. The signals from both scintillation light and charge carriers are combined to reconstruct the energetical and topological information of each event. These can be used to separate signal from background.
Results have been published in 2012 , 2014  and 2017  among others. The most recent half-life limit of 1.8x 1025 yrs (at 90% C.L.) was deduced for the neutrinoless double beta decay of 136Xe corresponding to an upper limit on the effective Majorana mass mββ of (147 – 398) meV. The relative energy resolution has been improved to 1.23% at the Q-value.
Research at Erlangen
Since August 2015, the Erlangen Centre for Astroparticle Physics is a member of the EXO-200 collaboration. ECAP supports the operation of the experiment by remote monitoring in a Remote Control Centre. Research topics include works on improving the Monte Carlo simulation of the EXO-200 detector with special interest in the charge drift and event position reconstruction. For this purpose, dedicated COMSOL simulations of the electric field in the detector are applied. Another topic is the application of Deep Learning methods where Convolutional Neural Networks are investigated to reconstruct the deposited charge energy of an event.