UltraDAS: the high-performance data acquisition system at the Isaac Newton Group of Telescopes

2000 ◽  
Author(s):  
Guy T. Rixon ◽  
Nicholas A. Walton ◽  
Dennis B. Armstrong ◽  
Guy F. Woodhouse ◽  
Simon M. Tulloch
Keyword(s):  
Data Acquisition ◽  
High Performance ◽  
Data Acquisition System ◽  
Performance Data ◽  
Acquisition System ◽  
Isaac Newton

scholarly journals The high-performance data acquisition system for the GAMMA-400 satellite-borne gamma-ray telescope

2017 ◽  
Author(s):  
Alexey Bakaldin ◽  
Sergey Bobkov ◽  
Oleg Serdin ◽  
Maxim S. Gorbunov ◽  
Andrey I. Arkhangelskiy ◽  
...  
Keyword(s):  
Data Acquisition ◽  
High Performance ◽  
Gamma Ray ◽  
Data Acquisition System ◽  
Performance Data ◽  
Acquisition System

scholarly journals The Data Acquisition System of the Na62 Experiment at Cern

2019 ◽  
Vol 214 ◽  
pp. 01037
Author(s):  
Marco Boretto
Keyword(s):  
Data Acquisition ◽  
High Performance ◽  
Branching Ratio ◽  
Data Acquisition System ◽  
Acquisition System ◽  
Trigger System ◽  
Level Trigger ◽  
Decay Products ◽  
Ethernet Network ◽  
Trigger Decision

The aim of the NA62 experiment is to study the extreme rare kaon decay K+ ? π+vv and to measure its branching ratio with a 10% accuracy. In order to do so, a very high intensity beam from the CERN SPS is used to produce charged kaons whose decay products are detected by many detectors installed along a 60 m decay region. The NA62 Data Acquisition system (DAQ) exploits a multi-level trigger system; following a Level0 (L0) trigger decision, 1 MHz data rate from about 60 sources is read by a PC-farm, the partial event is built and then passed through a series of Level1 (L1) algorithms to further reduce the trigger rate. Events passing this level are completed with the missing, larger, data sources (~400 sources) at the rate of 100 KHz. The DAQ is built around a high performance ethernet network interconnecting the detectors to a farm of 30 servers. After an overall description of the system design and the main implementation choices that allowed to reach the required performance and functionality, this paper describes the overall behaviour of the DAQ in the 2017 data taking period. It then concludes with an outlook of possible improvements and upgrades that may be applied to the system in the future.

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