Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSignal recognition efficiencies of artificial neural-network pulse-shape discrimination in HPGe $0 ubetabeta$-decay searches
110
0
0.0
(
0
)
Added by
Dimitrios Palioselitis
Publication date
2014
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
A pulse-shape discrimination method based on artificial neural networks was applied to pulses simulated for different background, signal and signal-like interactions inside a germanium detector. The simulated pulses were used to investigate variations of efficiencies as a function of used training set. It is verified that neural networks are well-suited to identify background pulses in true-coaxial high-purity germanium detectors. The systematic uncertainty on the signal recognition efficiency derived using signal-like evaluation samples from calibration measurements is estimated to be 5%. This uncertainty is due to differences between signal and calibration samples.
rate research
Read More
We report a study of the CUORE sensitivity to neutrinoless double beta ($0 ubetabeta$) decay. We used a Bayesian analysis based on a toy Monte Carlo (MC) approach to extract the exclusion sensitivity to the $0 ubetabeta$ decay half-life ($T_{1/2}^{0 u}$) at $90%$ credibility interval (CI) -- i.e. the interval containing the true value of $T_{1/2}^{0 u}$ with $90%$ probability -- and the $3 sigma$ discovery sensitivity. We consider various background levels and energy resolutions, and describe the influence of the data division in subsets with different background levels. If the background level and the energy resolution meet the expectation, CUORE will reach a $90%$ CI exclusion sensitivity of $2cdot10^{25}$ yr with $3$ months, and $9cdot10^{25}$ yr with $5$ years of live time. Under the same conditions, the discovery sensitivity after $3$ months and $5$ years will be $7cdot10^{24}$ yr and $4cdot10^{25}$ yr, respectively.
Pulse shape discrimination plays a key role in improving the signal-to-background ratio in NEOS analysis by removing fast neutrons. Identifying particles by looking at the tail of the waveform has been an effective and plausible approach for pulse shape discrimination, but has the limitation in sorting low energy particles. As a good alternative, the convolutional neural network can scan the entire waveform as they are to recognize the characteristics of the pulse and perform shape classification of NEOS data. This network provides a powerful identification tool for all energy ranges and helps to search unprecedented phenomena of low-energy, a few MeV or less, neutrinos.
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 deployed. 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.
Radiation background studies pertaining to $0 ubetabeta$ decay in $^{124}$Sn have been carried out. A TiLES setup has been installed at TIFR for this purpose. Neutron-induced background is studied in the TIN.TIN detector materials using fast neutron activation technique. The neutron flux ($E_nleq15$ MeV) resulting from SF and ($alpha, n$) interactions for the rock in the INO cavern is estimated using MC simulations. A two layer composite shield of borated paraffin (20 cm) + Pb (5 cm) is proposed for the reduction of neutron flux.
A new package to simulate the formation of electrical pulses in segmented true-coaxial high purity germanium detectors is presented. The computation of the electric field and weighting potentials inside the detector as well as of the trajectories of the charge carriers is described. In addition, the treatment of bandwidth limitations and noise are discussed. Comparison of simulated to measured pulses, obtained from an 18-fold segmented detector operated inside a cryogenic test facility, are presented.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
