A packet-based precise timing and synchronous DAQ network for the LHAASO project

A packet-based precise timing and synchronous DAQ network for the LHAASO project
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用于 LHAASO 项目的基于数据包的精确定时和同步 DAQ 网络

DOI:
10.1016/j.nima.2013.05.135
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发表时间:
2013-12
期刊:
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
影响因子:
--
通讯作者:
on behalf of the LHAASO Collaboration
on behalf of the LHAASO Collaboration
中科院分区:
其他
文献类型:
--
作者:
Qiang Du;Guanghua Gong;Weibin Pan;on behalf of the LHAASO Collaboration

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大型高空空气簇射观测站(LHAASO)项目的目标是高灵敏度和宽光谱的宇宙线探测,该项目由10,000多个不同类型的探测器组成,分布在1.2平方公里的范围内。为了以高角度分辨率精确重建空气簇射事件,所有探测器电子设备和数字化仪都应在同步采集模式下工作,全局定时误差小于5 ns(rms)。这种大规模和高精度的定时要求超过了诸如GPS和回波延迟校准的传统方法的能力或可行性。最近,一种名为白色兔子(WR)的新兴方法被提出并被证明是一种具有成本效益的解决方案,该解决方案将千兆以太网数据传输和相同光纤介质上的亚纳秒精度定时传输相结合。此外,白色兔子网络还为每个探测器提供直接数字化和无触发数据采集模式,这将大大降低数据采集(DAQ)系统的复杂性并提高其性能。在本文中,我们将展示在LHAASO项目的WR定时和DAQ网络的设计和开发状态。
Aiming to high sensitivity and wide spectrum of cosmic ray detection, the Large High Altitude Air Shower Observatory (LHAASO) project consists of over 10,000 detectors of different types, spreading over 1.2 km 2. To precisely reconstruct the air shower events with high angular resolution, all detector electronics and digitizers should work in synchronous acquisition mode with a global timing error less than< 5 ns (rms). This large scale and high precision timing requirement exceeds the capability or feasibility of traditional methods such as GPS and echo-delay calibration. Recently an emerging method named White Rabbit (WR) was proposed and demonstrated as a cost-effective solution that combines Gigabit Ethernet data transfer and subnanosecond precision timing transfer over the same fiber media. Furthermore, the White Rabbit network also enables direct digitization and trigger-less data acquisition mode for each detector, which will greatly reduce the complexity and improve the performance of the data acquisition (DAQ) system. In this paper we will demonstrate the design and development status of a WR timing and DAQ network in the LHAASO project.
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