Speaker
Description
The $\mbox{EXO-200}$ experiment searches for the neutrinoless double beta ($0\nu\beta\beta$) decay in $^{136}$Xe with an ultra-low background single-phase time projection chamber$~$(TPC) filled with 175$\,$kg isotopically enriched liquid xenon$~$(LXe). The detector has demonstrated good energy resolution and background rejection capabilities by simultaneously collecting scintillation light and ionization charge from the LXe and by a multi-parameter analysis. The combination of both signatures allows for complementary energy estimates and for a full 3D position reconstruction. Advances in computational performance in recent years have made novel Deep Learning techniques applicable to the physics community. In this talk, I will briefly present the concept of the detector and summarize the potential of Deep Learning based methods towards improving $\mbox{EXO-200}$ analyses with a focus on the energy reconstruction in the experiment.
