skip to main content

Title: Material radiopurity control in the XENONnT experiment
Abstract The selection of low-radioactive construction materials is of the utmost importance for rare-event searches and thus critical to the XENONnT experiment. Results of an extensive radioassay program are reported, in which material samples have been screened with gamma-ray spectroscopy, mass spectrometry, and $$^{222}$$ 222 Rn emanation measurements. Furthermore, the cleanliness procedures applied to remove or mitigate surface contamination of detector materials are described. Screening results, used as inputs for a XENONnT Monte Carlo simulation, predict a reduction of materials background ( $$\sim $$ ∼ 17%) with respect to its predecessor XENON1T. Through radon emanation measurements, the expected $$^{222}$$ 222 Rn activity concentration in XENONnT is determined to be 4.2 ( $$^{+0.5}_{-0.7}$$ - 0.7 + 0.5 )  $$\upmu $$ μ Bq/kg, a factor three lower with respect to XENON1T. This radon concentration will be further suppressed by means of the novel radon distillation system.  more » « less
Award ID(s):
2046549 2112803 2112802 2112851 2112801 2112796 1719286
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more » ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; « less
Date Published:
Journal Name:
The European Physical Journal C
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    The selection of low-radioactive construction materials is of utmost importance for the success of low-energy rare event search experiments. Besides radioactive contaminants in the bulk, the emanation of radioactive radon atoms from material surfaces attains increasing relevance in the effort to further reduce the background of such experiments. In this work, we present the$$^{222}$$222Rn emanation measurements performed for the XENON1T dark matter experiment. Together with the bulk impurity screening campaign, the results enabled us to select the radio-purest construction materials, targeting a$$^{222}$$222Rn activity concentration of$$10\,\mathrm{\,}\upmu \mathrm{Bq}/\mathrm{kg}$$10μBq/kgin$$3.2\,\mathrm{t}$$3.2tof xenon. The knowledge of the distribution of the$$^{222}$$222Rn sources allowed us to selectively eliminate problematic components in the course of the experiment. The predictions from the emanation measurements were compared to data of the$$^{222}$$222Rn activity concentration in XENON1T. The final$$^{222}$$222Rn activity concentration of$$(4.5\pm 0.1)\,\mathrm{\,}\upmu \mathrm{Bq}/\mathrm{kg}$$(4.5±0.1)μBq/kgin the target of XENON1T is the lowest ever achieved in a xenon dark matter experiment.

    more » « less
  2. Abstract

    Dynamic hydrologic exchange flows in river beds and banks are important for many ecosystem functions throughout river corridors. Here we test whether the exchanges and the associated mixing between a flooding river and groundwater within the river’s bank can be effectively traced by Radon‐222 (222Rn), a naturally occurring, inert, radiogenic, and radioactive gas that can be analyzed and monitored in situ. The assessment was done by simulation of groundwater flow and reactive transport of222Rn in the bank following a single, relatively rapid (hours long) flood wave and auxiliary field observations of222Rn, temperature and total dissolved solids (a surrogate for any ionic conservative tracer). Results illustrate that222Rn is more effective than temperature and total dissolved solids in tracing dynamic hyporheic exchange.222Rn variations in space and time are larger than the analytical uncertainty of common measurement methods. The individual effects of aquifer hydraulic conductivity, dispersivity, river water222Rn concentration, and bank topography were analyzed through sensitivity analysis. Larger hydraulic conductivity and dispersivity, lower222Rn concentration in river water relative to groundwater, and gentler bank slopes resulted in a more prominent and traceable222Rn signal. The transport and residence time of exchanged water may be estimated and interpreted using reactive transport models such as those implemented here. However, such application is sensitive to fluctuations in river water222Rn, requiring it to be well characterized. The assessment provides guidance for using222Rn as a tracer for groundwater and surface water interactions in dynamic settings.

    more » « less
  3. Abstract

    Submarine groundwater discharge (SGD) is an important driver of coastal biogeochemical budgets worldwide. Radon (222Rn) has been widely used as a natural geochemical tracer to quantify SGD, but field measurements are time consuming and costly. Here, we use deep learning to predict coastal seawater radon in SGD‐impacted regions. We hypothesize that deep learning could resolve radon trends and enable preliminary insights with limited field observations of groundwater tracers. Two deep learning models were trained on global coastal seawater radon observations (n = 39,238) with widely available inputs (e.g., salinity, temperature, water depth). The first model used a one‐dimensional convolutional neural network (1D‐CNN‐RNN) framework for site‐specific gap filling and producing short‐term future predictions. A second model applied a fully connected neural network (FCNN) framework to predict radon across geographically and hydrologically diverse settings. Both models can predict observed radon concentrations withr2 > 0.76. Specifically, the FCNN model offers a compelling development because synthetic radon tracer data sets can be obtained using only basic water quality and meteorological parameters. This opens opportunities to attain radon data from regions with large data gaps, such as the Global South and other remote locations, allowing for insights that can be used to predict SGD and plan field experiments. Overall, we demonstrate how field‐based measurements combined with big‐data approaches such as deep learning can be utilized to assess radon and potentially SGD beyond local scales.

    more » « less
  4. Abstract The MicroBooNE liquid argon time projection chamber (LArTPC) maintains a high level of liquid argon purity through the use of a filtration system that removes electronegative contaminants in continuously-circulated liquid, recondensed boil off, and externally supplied argon gas. We use the MicroBooNE LArTPC to reconstruct MeV-scale radiological decays. Using this technique we measure the liquid argon filtration system's efficacy at removing radon. This is studied by placing a 500 kBq 222 Rn source upstream of the filters and searching for a time-dependent increase in the number of radiological decays in the LArTPC. In the context of two models for radon mitigation via a liquid argon filtration system, a slowing mechanism and a trapping mechanism, MicroBooNE data supports a radon reduction factor of greater than 97% or 99.999%, respectively. Furthermore, a radiological survey of the filters found that the copper-based filter material was the primary medium that removed the 222 Rn. This is the first observation of radon mitigation in liquid argon with a large-scale copper-based filter and could offer a radon mitigation solution for future large LArTPCs. 
    more » « less
  5. Abstract

    Groundwater discharge flux into rivers (riverine groundwater discharge or RGD) is essential information for the conservation and management of aquatic ecosystems and resources. One way to estimate area‐integrated groundwater discharge into surface water bodies is to measure the concentration of a groundwater tracer within the water body. We assessed groundwater discharge using222Rn, a tracer common in many surface water studies, through field measurements, surface water222Rn mass balance model, and groundwater flow simulation, for the seldom studied but ubiquitous setting of a flooding river corridor. The investigation was conducted at the dam‐regulated Lower Colorado River (LCR) in Austin, Texas, USA. We found that222Rn in both the river water and groundwater in the river bank changed synchronously over a 12‐hour flood cycle. A222Rn mass balance model allowed for estimation of groundwater discharge into a 500‐m long reach of the LCR over the flood. The groundwater discharge ranged between negative values (indicating recharge) to 1570 m3/h; groundwater discharge from groundwater flow simulations corroborated these estimates. However, for the dynamic groundwater discharge estimated by the222Rn box model, assuming whether the groundwater222Rn endmember was constant or dynamic led to notably different results. The resultant groundwater discharge estimates are also highly sensitive to river222Rn values. We thus recommend that when using this approach to accurately characterize dynamic groundwater discharge, the222Rn in near‐stream groundwater should be monitored at the same frequency as river222Rn. If this is not possible, the222Rn method can still provide reasonable but approximate groundwater discharge given background information on surface water‐groundwater exchange time scales.

    more » « less