Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userBoosting background suppression in the NEXT experiment through Richardson-Lucy deconvolution
176
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Next-generation neutrinoless double beta decay experiments aim for half-life sensitivities of ~$10^{27}$ yr, requiring suppressing backgrounds to <1 count/tonne/yr. For this, any extra background rejection handle, beyond excellent energy resolution and the use of extremely radiopure materials, is of utmost importance. The NEXT experiment exploits differences in the spatial ionization patterns of double beta decay and single-electron events to discriminate signal from background. While the former display two Bragg peak dense ionization regions at the opposite ends of the track, the latter typically have only one such feature. Thus, comparing the energies at the track extremes provides an additional rejection tool. The unique combination of the topology-based background discrimination and excellent energy resolution (1% FWHM at the Q-value of the decay) is the distinguishing feature of NEXT. Previous studies demonstrated a topological background rejection factor of ~5 when reconstructing electron-positron pairs in the $^{208}$Tl 1.6 MeV double escape peak (with Compton events as background), recorded in the NEXT-White demonstrator at the Laboratorio Subterraneo de Canfranc, with 72% signal efficiency. This was recently improved through the use of a deep convolutional neural network to yield a background rejection factor of ~10 with 65% signal efficiency. Here, we present a new reconstruction method, based on the Richardson-Lucy deconvolution algorithm, which allows reversing the blurring induced by electron diffusion and electroluminescence light production in the NEXT TPC. The new method yields highly refined 3D images of reconstructed events, and, as a result, significantly improves the topological background discrimination. When applied to real-data 1.6 MeV $e^-e^+$ pairs, it leads to a background rejection factor of 27 at 57% signal efficiency.
rate research
Read More
Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in $^{136}$Xe. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6-MeV gamma rays from a $^{228}$Th calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offer significant improvement in signal efficiency/background rejection when compared to previous non-CNN-based analyses.
We use the Richardson-Lucy deconvolution algorithm to extract one dimensional (1D) spectra from LAMOST spectrum images. Compared with other deconvolution algorithms, this algorithm is much more fast. The practice on a real LAMOST image illustrates that the 1D resulting spectrum of this method has a higher SNR and resolution than those extracted by the LAMOST pipeline. Furthermore, our algorithm can effectively depress the ringings that are often shown in the 1D resulting spectra of other deconvolution methods.
New Experiments with Spheres-Gas (NEWS-G) is a dark matter direct detection experiment that will operate at SNOLAB (Canada). Similar to other rare-event searches, the materials used in the detector construction are subject to stringent radiopurity requirements. The detector features a 140-cm diameter proportional counter comprising two hemispheres made from commercially sourced 99.99% pure copper. Such copper is widely used in rare-event searches because it is readily available, there are no long-lived Cu radioisotopes, and levels of non-Cu radiocontaminants are generally low. However, measurements performed with a dedicated 210Po alpha counting method using an XIA detector confirmed a problematic concentration of 210Pb in bulk of the copper. To shield the proportional counters active volume, a low-background electroforming method was adapted to the hemispherical shape to grow a 500-$mu$m thick layer of ultra-radiopure copper to the detectors inner surface. In this paper the process is described, which was prototyped at Pacific Northwest National Laboratory (PNNL), USA, and then conducted at full scale in the Laboratoire Souterrain de Modane in France. The radiopurity of the electroplated copper was assessed through Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Measurements of samples from the first (second) hemisphere give 68% confidence upper limits of <0.58 $mu$Bq/kg (<0.24 $mu$Bq/kg) and <0.26 $mu$Bq/kg (<0.11 $mu$Bq/kg) on the 232Th and 238U contamination levels, respectively. These results are comparable to previously reported measurements of electroformed copper produced for other rare-event searches, which were also found to have low concentration of 210Pb consistent with the background goals of the NEWS-G experiment.
Natural radioactivity represents one of the main backgrounds in the search for neutrinoless double beta decay. Within the NEXT physics program, the radioactivity-induced backgrounds are measured with the NEXT-White detector. Data from 37.9 days of low-background operations at the Laboratorio Subterraneo de Canfranc with xenon depleted in $^{136}$Xe are analyzed to derive a total background rate of (0.84$pm$0.02) mHz above 1000 keV. The comparison of data samples with and without the use of the radon abatement system demonstrates that the contribution of airborne-Rn is negligible. A radiogenic background model is built upon the extensive radiopurity screening campaign conducted by the NEXT Collaboration. A spectral fit to this model yields the specific contributions of $^{60}$Co, $^{40}$K, $^{214}$Bi and $^{208}$Tl to the total background rate, as well as their location in the detector volumes. The results are used to evaluate the impact of the radiogenic backgrounds in the double beta decay analyses, after the application of topological cuts that reduce the total rate to (0.25$pm$0.01) mHz. Based on the best-fit background model, the NEXT-White median sensitivity to the two-neutrino double beta decay is found to be 3.5$sigma$ after 1 year of data taking. The background measurement in a Q$_{betabeta}pm$100 keV energy window validates the best-fit background model also for the neutrinoless double beta decay search with NEXT-100. Only one event is found, while the model expectation is (0.75$pm$0.12) events.
We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
