Würzburg 2018 – scientific programme
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T: Fachverband Teilchenphysik
T 28: Suche nach dunkler Materie II
T 28.4: Talk
Tuesday, March 20, 2018, 17:20–17:35, Philo-HS5
Deep learning techniques and field simulations for the DARWIN dark matter search — •Anna-Sophie Frick, Guido Drexlin, Ferenc Glück, and Daniel Hilk — Karlsruhe Institute of Technology, Karlsruhe
In the last years, dual phase noble gas detectors like XENON1T have been probing WIMP-nucleon cross sections down to 10−46 cm2. The DARWIN (DARk matter WImp search with liquid xenoN) project aims to push the sensitivity to the ultimate limit for the spin-independent WIMP-nucleon cross section of O(10−49cm2) at the neutrino floor which will require a total mass of 50 tons of LXe.
As an experiment searching for rare events, DARWIN requires a good discrimination of all possible background sources from potential WIMP signals. To enhance the signal recognition, convolutional network methods which have successfully been applied to the KATRIN experiment have been transferred to the DARWIN detector to guide the layout of a LXe prototype to be built at KIT. This talk summarizes current results of the performance of deep learning techniques regarding different patterns.
Additionally, to further improve the understanding of signals and patterns, it is indispensable to simulate the electrostatic field with high precision within the DARWIN detector. This talk gives a brief overview of the current results of field simulations.