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Register a new userCharge and current-sensitive preamplifiers for pulse shape discrimination techniques with silicon detectors
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New charge and current-sensitive preamplifiers coupled to silicon detectors and devoted to studies in nuclear structure and dynamics have been developed and tested. For the first time shapes of current pulses from light charged particles and carbon ions are presented. Capabilities for pulse shape discrimination techniques are demonstrated.
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Events near the cathode and anode surfaces of a coplanar grid CdZnTe detector are identifiable by means of the interaction depth information encoded in the signal amplitudes. However, the amplitudes cannot be used to identify events near the lateral surfaces. In this paper a method is described to identify lateral surface events by means of their pulse shapes. Such identification allows for discrimination of surface alpha particle interactions from more penetrating forms of radiation, which is particularly important for rare event searches. The effectiveness of the presented technique in suppressing backgrounds due to alpha contamination in the search for neutrinoless double beta decay with the COBRA experiment is demonstrated.
The light output, $alpha/beta$ ratio, and pulse shape have been investigated at $-25^circ$ C with PbWO$_4$ crystal scintillators undoped, and doped by F, Eu, Mo, Gd and S. The fast $0.01-0.06 mu$s and middle $0.1-0.5 mu$s components of scintillation decay were observed for all the samples. Slow components of scintillation signal with the decay times $1-3 mu$s and $13-28 mu$s with the total intensity up to $approx50%$ have been recognized for several samples doped by Molybdenum. We found some indications of a pulse-shape discrimination between $alpha$ particles and $gamma$ quanta with PbWO$_4$ (Mo doped) crystal scintillators.
The method of pulse-shape analysis (PSA) for particle identification (PID) was applied to a double-sided silicon strip detector (DSSD) with a strip pitch of 300 {mu}m. We present the results of test measurements with particles from the reactions of a 70 MeV 12C beam impinging on a mylar target. Good separation between protons and alpha particles down to 3 MeV has been obtained when excluding the interstrip events of the DSSD from the analysis.
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 tube (PMT) at each end. The new improved method, called the ``valued-assigned PSD (VPSD), assigns a normalized fitting residual to every waveform as the PSD value. This procedure then facilitates the incorporation of longitudinal position dependence of the scintillator, which further enhances the PSD capability of the detector system. In this paper, we use radiation emitted from an AmBe neutron source to demonstrate that the resulting neutron-gamma identification has been much improved when compared to the traditional technique that uses the geometric mean of light outputs from both PMTs. The new method has also been modified and applied to a recent experiment at the National Superconducting Cyclotron Laboratory (NSCL) that uses an analog electronic system.
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 recognize one of the most abundant classes of background events in these experiments. By combining a neural network for feature extraction with a smaller classification network, our method can be trained with only a small number of labeled events. To validate our method, we use signals from a broad-energy germanium detector irradiated with a $^{228}$Th gamma source. We find that it matches the performance of state-of-the-art algorithms commonly used for this detector type. However, it requires less tuning and calibration and shows potential to identify certain types of background events missed by other methods.
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