A general-purpose digital data acquisition system (GDDAQ) at Peking University

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Abstract

A general-purpose digital data acquisition system (GDDAQ) has been developed at Peking University. This GDDAQ, composed of 16-channel Digital Pulse Processor Pixie-16 modules from XIA LLC, is a versatile, flexible, and easily expandable data acquisition system for nuclear physics research in China. The software used by this GDDAQ is based on the CERN ROOT framework and developed and tested in CentOS 7 LINUX operating platform. A flexible trigger system has also been developed to accommodate different experimental settings. A user-friendly software GUI helps users monitor and debug the detection system in real timer or offline. Many offline analysis tools have been developed to help users quickly optimize parameters for various types of detectors without the need for time-consuming tests and measurements. This GDDAQ has been successfully implemented in several nuclear physics experiments and its versatility and high efficiency have been demonstrated.

Introduction

With their much higher pulse processing flexibilities and easier communication with computer control systems, Digital Data Acquisition Systems (DDAQs) have been used extensively in recent years and have demonstrated significant advantages over conventional analog systems in nuclear physics research [1], [2], [3], [4], [5], like studies of short-lived charged particle emitters which involve overlapping ion–particle or particle–particle signals [6], [7], [8], [9] and studies of sub-microsecond isomers observed in fragmentation reactions [10]. With sufficient accuracy to recover the original information of the detector pulses and offering precise control of many experimental parameters, DDAQs have been successfully implemented for nuclear spectroscopy in a wide variety of detector systems: HPGe arrays [11], [12], [13], [14], [15], [16], neutron detection arrays [17], [18], [19], charge-particle silicon detection arrays [20], [21], [22] etc. In general, nuclear spectroscopic information is derived from coincidence measurements between various types of detectors. Consequently, there have been continuous efforts to apply DDAQs to experiments that utilize a large mix of detectors [23], [24], [25].

For a general-purpose digital data acquisition system (GDDAQ), the following capabilities are important and necessary:

  • Extensible framework. The system should support large numbers of channels and be easily expanded;

  • Flexible triggering mode. The trigger setup is flexible enough to accommodate not only large detector arrays but also any ancillary detectors;

  • User-friendly parameters adjustment. The system should be remotely controllable and can assist users to quickly optimize experimental parameters;

  • Efficient online-monitoring.

The present paper reports a GDDAQ that has been developed at Peking University. Possessing the capabilities mentioned above, this GDDAQ has been successfully implemented in γ-spectroscopy experiments at both the China Institute of Atomic Energy (CIAE) and iThemba LABS in South Africa, at the Back-n Beamline of China Spallation Neutron Source (CSNS) and the β-decay experiments at the Institute of Modern Physics (IMP) in China. This GDDAQ is easy to use without requiring special technical expertise. The present paper is organized as follows: Section 2 presents the DAQ system, Section 3 describes the triggering system. The graphical user interface is presented in Section 4. Lastly, offline optimization is described in Section 5 and a summary is given in Section 6.

Section snippets

Hardware

The principal components of the hardware for this GDDAQ are Pixie-16 modules and support cards manufactured by XIA LLC [26] and others: (See arrows in Fig. 1)

  • (A)

    Pixie-16 6U CompactPCI/PXI chassis

  • (B)

    PCI-8366/PXI-8368, chassis controller [27]

  • (C)

    Pixie-16, 12/14/16 bits, 100/250/500 MSPS ADC

  • (D)

    Pixie-16 MicroZed-based Trigger I/O (MZTIO), a programmable trigger module

  • (E)

    Pixie-16 clock and trigger rear I/O module

Fig. 1 shows a typical example of this GDDAQ. The Pixie-16 modules reside in slots in the Pixie-16

Trigger

The recent introduction of triggerless DDAQs, which record all live events without event selection, has provided an attractive option for users [31]. However, while they bring great flexibility for offline data analysis, they can also generate significant data streams in the experiments involving high counting rates, which may then be beyond the DDAQs’ I/O capabilities. Therefore, a proper triggering system, which is flexible enough to accommodate different experimental circumstances, is

Graphical user interface (GUI)

Monitoring and debugging the detection system at all times is important for the success of an experiment, and a user-friendly GUI can help users achieve that goal. Fig. 5 shows a snapshot of the main window of the GDDAQ’s GUI. The data collection program is based on an earlier version of the NSCL DDAS Nscope program [35]. As shown in Fig. 5, it consists of two parts: control and monitor. To get the useful information and update the experimental parameters efficiently, the present control GUI (

Offline optimization

Precise adjustment of experimental parameters is one of the key requirements for the GDDAQ. For most DDAQs currently in use, optimization of experimental parameters is done in online mode, i.e. with the module on, users adjust the parameters, observe their resulting effects, and repeat until optimized parameters are obtained. For experiments involving a large number of channels, this approach to parameter tuning becomes a time-consuming task. In GDDAQ, we have developed offline tools that can

Summary

We reported a general-purpose digital data acquisition system (GDDAQ) developed at Peking University. This GDDAQ is based on Pixie-16 modules from XIA LLC and can be easily expanded to fit acquisition systems of a variety of sizes. Software based on the CERN ROOT framework was developed in LINUX and tested under CentOS 7 platform. A trigger system that is flexible enough to accommodate different experimental circumstances was also developed. A user-friendly GUI helps users to monitor and debug

CRediT authorship contribution statement

H.Y. Wu: Investigation, Software, Writing - original draft. Z.H. Li: Investigation, Resources, Methodology, Funding acquisition. H. Tan: Writing - review & editing. H. Hua: Writing - review & editing, Funding acquisition. J. Li: Software. W. Hennig: Writing - review & editing. W.K. Warburton: Writing - review & editing. D.W. Luo: Software. X. Wang: Validation. X.Q. Li: Validation. S.Q. Zhang: Validation. C. Xu: Validation. Z.Q. Chen: Validation. C.G. Wu: Validation. Y. Jin: Validation. J. Lin:

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work is supported by the National Key R&D Program of China (Contract No. 2018YFA0404403), the National Natural Science Foundation of China under Grant No. 11775003, No. 11675003, No. 11575006, No. 11875075. We appreciate Z. Liu, C.J. Lin, J. Lee, X.X. Xu, X.G. Wu, Y. Zheng, C.B. Li, P. Jones, R. A. Bark, Z.H. Wang for their great helps during developmental testing of this GDDAQ. We would also like to thank all the experimental groups using this GDDAQ for their feedback.

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