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SuperCDMS is the next phase of the Cryogenic Dark Matter Search experiment, which measures both phonon and charge signals generated by particle recoils within a germanium target mass. Charge signals are employed both in the definition of a fiducial volume and in the rejection of electron recoil background events. Alternatively, phonons generated by the charge carriers can also be used for the same two goals. This paper describes preliminary efforts to observe and quantify these contributions to the phonon signal and then use them to reject background events. A simple analysis using only one pulse shape parameter shows bulk electron recoil vs. bulk nuclear recoil discrimination to the level of 1:10^3 (limited by the statistics of the data), with little degradation in discrimination ability down to at least 7 keV recoil energy. Such phonon-only discrimination can provide a useful cross-check to the standard discrimination methods, and it also points towards the potential of a device optimized for a phonon-only measurement.
We measured the decay time of the scintillation pulses produced by electron and nuclear recoils in CaF2(Eu) by a new fitting method. In the recoil energy region 5-30 keVee, we found differences of the decay time between electron and nuclear recoil ev
The ArDM experiment completed a single-phase commissioning run in 2015 with an active liquid argon target of nearly one tonne in mass. The analysis of the data and comparison to simulations allowed for a test of the crucial detector properties and co
The GERDA experiment located at the LNGS searches for neutrinoless double beta (0 ubetabeta) decay of ^{76}Ge using germanium diodes as source and detector. In Phase I of the experiment eight semi-coaxial and five BEGe type detectors have been deploy
Using the waveforms from a digital electronic system, an offline analysis technique on pulse shape discrimination (PSD) has been developed to improve the neutron-gamma separation in a bar-shaped NE-213 scintillator that couples to a photomultiplier t
Experiments searching for rare processes like neutrinoless double beta decay heavily rely on the identification of background events to reduce their background level and increase their sensitivity. We present a novel machine learning based method to