ترغب بنشر مسار تعليمي؟ اضغط هنا

Phonon Pulse Shape Discrimination in SuperCDMS Soudan

140   0   0.0 ( 0 )
 نشر من قبل Scott Hertel
 تاريخ النشر 2011
  مجال البحث فيزياء
والبحث باللغة English




اسأل ChatGPT حول البحث

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.



قيم البحث

اقرأ أيضاً

156 - S. Oguri , Y. Inoue , M. Minowa 2010
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 ents. In the recoil energy region above 20 keVee, we found that the decay time is independent of the recoil energy.
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 nfirmed the low background performance of the setup. The statistical rejection power for electron recoil events using the pulse shape discrimination method was estimated using data from a Cf-252 neutron calibration source. Electron and nuclear recoil band profiles were found to be well described by Gaussian distributions. Employing such a model we derive values for the electron recoil statistical rejection power of more than 10$^8$ in the tonne-scale liquid argon target for events with more than 50 detected photons at a 50% acceptance for nuclear recoils. The Rn-222 emanation rate of the ArDM cryostat at room temperature was found to be 65.6$pm$0.4 $mu$Hz/l, and the Ar-39 specific activity from the employed atmospheric argon to be 0.95$pm$0.05 Bq/kg. The cosmic muon flux at the Canfranc underground site was determined to be between 2 and 3.5$times 10^{-3}m^{2}s^{-1}$ . These results pave the way for the next physics run of ArDM in the double-phase operational mode.
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 ed. The latter type is used in this field of research for the first time. All detectors are made from material with enriched ^{76}Ge fraction. The experimental sensitivity can be improved by analyzing the pulse shape of the detector signals with the aim to reject background events. This paper documents the algorithms developed before the data of Phase I were unblinded. The double escape peak (DEP) and Compton edge events of 2.615 MeV gamma rays from ^{208}Tl decays as well as 2 ubetabeta decays of ^{76}Ge are used as proxies for 0 ubetabeta decay. For BEGe detectors the chosen selection is based on a single pulse shape parameter. It accepts 0.92$pm$0.02 of signal-like events while about 80% of the background events at Q_{betabeta}=2039 keV are rejected. For semi-coaxial detectors three analyses are developed. The one based on an artificial neural network is used for the search of 0 ubetabeta decay. It retains 90% of DEP events and rejects about half of the events around Q_{betabeta}. The 2 ubetabeta events have an efficiency of 0.85pm0.02 and the one for 0 ubetabeta decays is estimated to be 0.90^{+0.05}_{-0.09}. A second analysis uses a likelihood approach trained on Compton edge events. The third approach uses two pulse shape parameters. The latter two methods confirm the classification of the neural network since about 90% of the data events rejected by the neural network are also removed by both of them. In general, the selection efficiency extracted from DEP events agrees well with those determined from Compton edge events or from 2 ubetabeta decays.
119 - F. C. E. Teh , J. -W. Lee , K. Zhu 2020
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 ube (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.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا