arXiv subjects

F. Psihas

Publications and source records attributed to F. Psihas.

13 recordsLinked to original sources

arXiv2022

Xenon-Doped Liquid Argon TPCs as a Neutrinoless Double Beta Decay Platform

Searches for neutrinoless double-beta decay continue to expand our understanding of the lepton sector, with experiments now pursuing ton-scale target masses with sensitivity to $m_{\beta\beta}$ covering the allowed parameter space for the inverted neutrino mass ordering. Continued searches for this rare decay will require scalable detector technologies to achieve significant increases in the target mass beyond the ton scale, in order to probe the normal ordering region. This work explores the concept of searching for neutrinoless double-beta decay in a 10 kton scale liquid argon time projection chamber (LArTPC) located deep underground and doped with percent-level quantities of xenon. We discuss the design requirements, background mitigation and detector R&D needs, and considerations for deployment in a modified DUNE far detector module. We find that such a detector could reach $m_{\beta\beta}$ sensitivity at the 2-4 meV range with xenon doping at 2% if significant background reductions can be achieved.

hep-ex↗

arXiv2020

Cosmic Background Removal with Deep Neural Networks in SBND

In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded. In practice, this means that data from surface liquid argon time projection chambers will be dominated by cosmic particles, both as a source of event triggers and as the majority of the particle count in true neutrino-triggered events. In this work, we demonstrate a novel application of deep learning techniques to remove these background particles by applying semantic segmentation on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, at single image-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.

physics.data-an↗

arXiv2020

Neutrinoless Double Beta Decay with $^{82}$SeF$_6$ and Direct Ion Imaging

We present a new neutrinoless double beta decay concept: the high pressure selenium hexafluoride gas time projection chamber. Combining techniques pioneered in high pressure xenon gas such as topological discrimination, with the high Q-value afforded by double beta decay isotope $^{82}$Se, a promising new detection technique is outlined. Lack of free electrons in SeF$_6$ mandates the use of an ion TPC. The microphysics of ion production and drift, which have many nuances, are explored. Background estimates are produced suggesting such a detector may achieve background indices of better than 1 count per ton per year in the region of interest at the 100~kg scale, and still better at the ton-scale.

physics.ins-det↗

arXiv2019

Low-diffusion Xe-He gas mixtures for rare-event detection: Electroluminescence Yield

High pressure xenon Time Projection Chambers (TPC) based on secondary scintillation (electroluminescence) signal amplification are being proposed for rare event detection such as directional dark matter, double electron capture and double beta decay detection. The discrimination of the rare event through the topological signature of primary ionisation trails is a major asset for this type of TPC when compared to single liquid or double-phase TPCs, limited mainly by the high electron diffusion in pure xenon. Helium admixtures with xenon can be an attractive solution to reduce the electron diffusion significantly, improving the discrimination efficiency of these optical TPCs. We have measured the electroluminescence (EL) yield of Xe-He mixtures, in the range of 0 to 30% He and demonstrated the small impact on the EL yield of the addition of helium to pure xenon. For a typical reduced electric field of 2.5 kV/cm/bar in the scintillation region, the EL yield is lowered by ~ 2%, 3%, 6% and 10% for 10%, 15%, 20% and 30% of helium concentration, respectively. This decrease is less than what has been obtained from the most recent simulation framework in the literature. The impact of the addition of helium on EL statistical fluctuations is negligible, within the experimental uncertainties. The present results are an important benchmark for the simulation tools to be applied to future optical TPCs based on Xe-He mixtures.

physics.ins-det↗

arXiv2019

Energy calibration of the NEXT-White detector with 1% resolution near Q$_{ββ}$ of $^{136}$Xe

Excellent energy resolution is one of the primary advantages of electroluminescent high pressure xenon TPCs, and searches for rare physics events such as neutrinoless double-beta decay ($β\beta0ν$) require precise energy measurements. Using the NEXT-White detector, developed by the NEXT (Neutrino Experiment with a Xenon TPC) collaboration, we show for the first time that an energy resolution of 1% FWHM can be achieved at 2.6 MeV, establishing the present technology as the one with the best energy resolution of all xenon detectors for $β\beta0ν$ searches.

physics.ins-det↗

arXiv2019

Demonstration of the event identification capabilities of the NEXT-White detector

In experiments searching for neutrinoless double-beta decay, the possibility of identifying the two emitted electrons is a powerful tool in rejecting background events and therefore improving the overall sensitivity of the experiment. In this paper we present the first measurement of the efficiency of a cut based on the different event signatures of double and single electron tracks, using the data of the NEXT-White detector, the first detector of the NEXT experiment operating underground. Using a \TO\ calibration source to produce signal-like and background-like events with energies near 1.6 MeV, a signal efficiency of $71.6 \pm 1.5_{\textrm{ stat}} \pm 0.3_{\textrm{ sys}} \%$ for a background acceptance of $20.6 \pm 0.4_{\textrm{ stat}} \pm 0.3_{\textrm{ sys}} \%$ is found, in good agreement with Monte Carlo simulations. An extrapolation to the energy region of the neutrinoless double beta decay by means of Monte Carlo simulations is also carried out, and the results obtained show an improvement in background rejection over those obtained at lower energies.

physics.ins-det↗

arXiv2019

Radiogenic backgrounds in the NEXT double beta decay 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 Subterráneo 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$0.01) mHz. Based on the best-fit background model, the NEXT-White median sensitivity to the two-neutrino double beta decay is found to be 3.5$σ$ after 1 year of data taking. The background measurement in a Q$_{ββ}\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.

physics.ins-det↗

arXiv2019

Context-Enriched Identification of Particles with a Convolutional Network for Neutrino Events

Particle detectors record the interactions of subatomic particles and their passage through matter. The identification of these particles is necessary for in-depth physics analysis. While particles can be identified by their individual behavior as they travel through matter, the full context of the interaction in which they are produced can aid the classification task substantially. We have developed the first convolutional neural network for particle identification which uses context information. This is also the first implementation of a four-tower siamese-type architecture both for separation of independent inputs and inclusion of context information. The network classifies clusters of energy deposits from the NOvA neutrino detectors as electrons, muons, photons, pions, and protons with an overall efficiency and purity of 83.3% and 83.5%, respectively. We show that providing the network with context information improves performance by comparing our results with a network trained without context information.

physics.ins-det↗

arXiv2018

Electroluminescence TPCs at the thermal diffusion limit

The NEXT experiment aims at searching for the hypothetical neutrinoless double-beta decay from the ${}^{136}$Xe isotope using a high-purity xenon TPC. Efficient discrimination of the events through pattern recognition of the topology of primary ionisation tracks is a major requirement for the experiment. However, it is limited by the diffusion of electrons. It is known that the addition of a small fraction of a molecular gas to xenon reduces electron diffusion. On the other hand, the electroluminescence (EL) yield drops and the achievable energy resolution may be compromised. We have studied the effect of adding several molecular gases to xenon (CO${}_{2}$, CH${}_{4}$ and CF${}_{4}$) on the EL yield and energy resolution obtained in a small prototype of driftless gas proportional scintillation counter. We have compared our results on the scintillation characteristics (EL yield and energy resolution) with a microscopic simulation, obtaining the diffusion coefficients in those conditions as well. Accordingly, electron diffusion may be reduced from about 10 mm/$\sqrt{\mathrm{m}}$ for pure xenon down to 2.5 mm/$\sqrt{\mathrm{m}}$ using additive concentrations of about 0.05%, 0.2% and 0.02% for CO${}_{2}$, CH${}_{4}$ and CF${}_{4}$, respectively. Our results show that CF${}_{4}$ admixtures present the highest EL yield in those conditions, but very poor energy resolution as a result of huge fluctuations observed in the EL formation. CH${}_{4}$ presents the best energy resolution despite the EL yield being the lowest. The results obtained with xenon admixtures are extrapolated to the operational conditions of the NEXT-100 TPC. CO${}_{2}$ and CH${}_{4}$ show potential as molecular additives in a large xenon TPC, CH${}_{4}$ showing the best performance and stability to be used in the NEXT-100 TPC, with an extrapolated energy resolution of 0.4% at 2.45 MeV for concentrations below 0.4%.

physics.ins-det↗

arXiv2018

Initial results on energy resolution of the NEXT-White detector

One of the major goals of the NEXT-White (NEW) detector is to demonstrate the energy resolution that an electroluminescent high pressure xenon TPC can achieve for high energy tracks. For this purpose, energy calibrations with 137Cs and 232Th sources have been carried out as a part of the long run taken with the detector during most of 2017. This paper describes the initial results obtained with those calibrations, showing excellent linearity and an energy resolution that extrapolates to approximately 1% FWHM at Q$_{ββ}$.

physics.ins-det↗

arXiv2018

Measurement of radon-induced backgrounds in the NEXT double beta decay experiment

The measurement of the internal $^{222}$Rn activity in the NEXT-White detector during the so-called Run-II period with $^{136}$Xe-depleted xenon is discussed in detail, together with its implications for double beta decay searches in NEXT. The activity is measured through the alpha production rate induced in the fiducial volume by $^{222}$Rn and its alpha-emitting progeny. The specific activity is measured to be $(38.1\pm 2.2~\mathrm{(stat.)}\pm 5.9~\mathrm{(syst.)})$~mBq/m$^3$. Radon-induced electrons have also been characterized from the decay of the $^{214}$Bi daughter ions plating out on the cathode of the time projection chamber. From our studies, we conclude that radon-induced backgrounds are sufficiently low to enable a successful NEXT-100 physics program, as the projected rate contribution should not exceed 0.1~counts/yr in the neutrinoless double beta decay sample.

physics.ins-det↗

arXiv2018

High Voltage Insulation and Gas Absorption of Polymers in High Pressure Argon and Xenon Gases

High pressure gas time projection chambers (HPGTPCs) are made with a variety of materials, many of which have not been well characterized in high pressure noble gas environments. As HPGTPCs are scaled up in size toward ton-scale detectors, assemblies become larger and more complex, creating a need for detailed understanding of how structural supports and high voltage insulators behave. This includes the identification of materials with predictable mechanical properties and without surface charge accumulation that may lead to field deformation or sparking. This paper explores the mechanical and electrical effects of high pressure gas environments on insulating polymers PTFE, HDPE, PEEK, POM and UHMW in Argon and Xenon, including studying absorption, swelling and high voltage insulation strength.

physics.ins-det↗

arXiv2016

A Convolutional Neural Network Neutrino Event Classifier

Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

hep-ex↗