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Register a new userNew approach to 3D electrostatic calculations for micro-pattern detectors
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We demonstrate practically approximation-free electrostatic calculations of micromesh detectors that can be extended to any other type of micropattern detectors. Using newly developed Boundary Element Method called Robin Hood Method we can easily handle objects with huge number of boundary elements (hundreds of thousands) without any compromise in numerical accuracy. In this paper we show how such calculations can be applied to Micromegas detectors by comparing electron transparencies and gains for four different types of meshes. We demonstrate inclusion of dielectric material by calculating the electric field around different types of dielectric spacers.
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In the context of the 2013 APS-DPF Snowmass summer study conducted by the U.S. HEP community, this white paper outlines a roadmap for further development of Micro-pattern Gas Detectors for tracking and muon detection in HEP experiments. We briefly discuss technical requirements and summarize current capabilities of these detectors with a focus of operation in experiments at the energy frontier in the medium-term to long-term future. Some key directions for future R&D on Micro-pattern Gas Detectors in the U.S. are suggested.
Low Gain Avalanche Diodes (LGADs) are thin (20-50 $mu m$)silicon di ode sensors with modest internal gain (typically 5 to 50) and exceptional time resolution (17 $ps$ to 50 $ps$). However, the granularity of such devices is limited to the millimeter scale due to the need to include protection structures at the boundaries of the readout pads to avoid premature breakdown due to large local electric fields. In this paper we present a new approach -- the Deep-Junction LGAD (DJ-LGAD) -- that decouples the high-field gain region from the readout plane. This approach is expected to improve the achievable LGAD granularity to the tens-of-micron scale while maintaining direct charge collection on the segmented electrodes.
Photomultiplier tubes (PMTs) are widely used in neutrino and other experiments for the detection of weak light. To date PMTs are the most sensitive single photon detector per unit area. In addition to the quantum efficiency for photon detection, there are a number of other specifications, such as rate and amplitude of after-pulses, dark noise rate, transit time spread, radioactive background of glass, peak-to-valley ratio, etc. All affect the photon detection and hence the physics goals. In addition, cost is another major factor for large experiments. It is important to know how to properly take into account all these parameters and choose the most appropriate PMTs. In this paper, we present an approach to quantify the impact of all parameters on the physics goals, including cost and risk. This method has been successfully used in the JUNO experiment. It can be applied to other experiments with large number of PMTs.
A centenary after the invention of the basic principle of gas amplification, gaseous detectors - are still the first choice whenever the large area coverage with low material budget is required. Advances in photo-lithography and micro-processing techniques in the chip industry during the past two decades triggered a major transition in the field of gas detectors from wire structures to Micro-Pattern Gas Detector (MPGD) concepts, revolutionizing cell-size limitations for many gas detector applications. The high radiation resistance and excellent spatial and time resolution make them an invaluable tool to confront future detector challenges at the frontiers of research. The design of the new micro-pattern devices appears suitable for industrial production. In 2008, the RD51 collaboration at CERN has been established to further advance technological developments of MPGDs and associated electronic-readout systems, for applications in basic and applied research. This review provides an overview of the state-of-the-art of the MPGD technologies and summarizes recent activities for the next generation of colliders within the framework of the RD51 collaboration.
The response of RPC detectors is highly sensitive to environmental variables. A novel approach is presented to model the response of RPC detectors in a variety of experimental conditions. The algorithm, based on Artificial Neural Networks, has been developed and tested on the CMS RPC gas gain monitoring system during commissioning.
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