Abstract

Due to the so-called 3He shortage crisis, many detection techniques for thermal neutrons are currently based on alternative converters. There are several possible ways of increasing the detection efficiency for thermal neutrons using the solid neutron-to-charge converters 10B or 10B4C. Here, we present an investigation of the Micromegas technology. The micro-pattern gaseous detector Micromegas was developed in the past years at Saclay and is now used in a wide variety of neutron experiments due to its combination of high accuracy, high rate capability, excellent timing properties, and robustness. A large high-efficiency Micromegas-based neutron detector is proposed for thermal neutron detection, containing several layers of 10B4C coatings that are mounted inside the gas volume. The principle and the fabrication of a single detector unit prototype with overall dimension of ~15 × 15 cm2 and its possibility to modify the number of 10B4C neutron converter layers are described. We also report results from measurements that are verified by simulations, demonstrating that typically five 10B4C layers of 1–2 μm thickness would lead to a detection efficiency of 20% for thermal neutrons and a spatial resolution of sub-mm. The high potential of this novel technique is given by the design being easily adapted to large sizes by constructing a mosaic of several such detector units, resulting in a large area coverage and high detection efficiencies. An alternative way of achieving this is to use a multi-layered Micromegas that is equipped with two-side 10B4C-coated gas electron multiplier (GEM)-type meshes, resulting in a robust and large surface detector. Another innovative and very promising concept for cost-effective, high-efficiency, large-scale neutron detectors is by stacking 10B4C-coated microbulk Micromegas. A prototype was designed and built, and the tests so far look very encouraging.

Highlights

  • Neutron detectors with good performance are urgently needed to take full benefit of the high-intensity neutron beams produced by sources like the two large-scale neutron facilities, SNS (Spallation Neutron Source) in the United States (US) and J-PARC (Japan Proton AcceleratorResearch Complex) in Japan, as well as the future European Spallation Source (ESS) which will produce its first neutrons in 2022

  • The detection mechanism involves an atom of 10 B capturing a neutron, thereby producing two ionizing particles, 7 Li and 4 He, which are emitted in opposite directions via the following reactions: n + 10 B → 7 Li (0.84 MeV/r = 1.9 μ) + 4 He (1.47 MeV/r = 3.6 μ) + γ (0.48 MeV) (94%)

  • Compared to multi-wire proportional chambers (MWPCs), micro-pattern gas detectors (MPGDs) that are widely used in high-energy physics (HEP)

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Summary

Introduction

Neutron detectors with good performance are urgently needed to take full benefit of the high-intensity neutron beams produced by sources like the two large-scale neutron facilities, SNS (Spallation Neutron Source) in the United States (US) and J-PARC The applied electric detection efficiency is given by the number of entries in the energy deposition histograms inside the field should be properly configured in order to drift the produced charges to the detector through gas volume divided by the total number of thermal neutrons hitting the detector. The alternative approach is to build a tower of converter layers for each design of a detection unit can be made of a 20-mm-thick stack consisting of seven 10 B4 C layers that detector, having 10B4C deposited on thin metallic meshes placed in the drift region, resulting in less are deposited on two sides of three meshes and on the inner side of the entrance window. The applied electric field should be properly configured in order to drift neutron detection efficiency of such a unit is simulated to be 25%, but goes up to 39% with two units the produced charges to the detector through the holes in the meshes. [18]. efficiencies of a two-mesh detector unit

A Prototype
Its frame cm zone isonto
Data during
Using the Microbulk Technology
Data measured during s for two runs with
Findings
Conclusions

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