Ⅱ-9. Instrumentation

RIKEN Accel. Prog. Rep. 53

SAMURAI DAQ speed improvement
J. Gao,∗1,∗3 H. Baba,∗1 M. Sasano,∗1 and L. Stuhl∗21000

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Fig. 1.: Deﬁnition of the time interval between neighboring events ∆t.
∗1
∗2
∗3

RIKEN Nishina Center, Japan
Center for Nuclear Study, University of Tokyo, Japan
School of Physics, Peking University, China

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7501000(b) BDCFig. 2.: Plot of ∆t versus data size for SAMURAI30data. A comparison of the ﬁgures indicates that PDClimits the DAQ performance.

dead time(size = s) = inf{∆ti |sizei = s}.
With pairs (sizei , ∆ti ) of each event, where sizei isthe data size of the device of interest for event i and∆ti is deﬁned in Fig. 1, we can draw a 2D histogram.
For example, Fig. 2 shows two plots generated fromSAMURAI30 experiment1) data. In this example, thehistogram for the proton drift chamber (PDC) has a linear edge, but that for the beam drift chamber (BDC)
appears to have a plateau on the small data size side.
This implies that BDC should wait for other device(s)to ﬁnish data saving when it has a small amount of data.
We conclude that PDC is the bottleneck and should beimproved. We can repeat these steps to optimize DAQ
performance until all devices show a linear edge on thehistograms or the dead time becomes low enough for theexperiment.
This method is also useful for comparing the performance of diﬀerent modules. For example, Fig. 3(a) andFig. 3(b) show the results for two widely used TDCs,

1000750Δt(µs)

In this contribution, we report the improvement of
the SAMURAI DAQ speed. SAMURAI DAQ contains
several VME subsystems. The typical dead time wasabout 200 µs. It is necessary to improve the DAQ system
to acquire suﬃcient data for recent studies.
The accurate measurement of dead time is critical forthe performance improvement. Conventionally, we estimate the typical dead time by checking the width of busy
signals using an oscilloscope. However the width can be
diﬀerent for events with diﬀerent data sizes. Therefore
we developed a new method to evaluate the dead time
as a function of data size using saved data. This method
helps us ﬁnd the device that critically limits the DAQ
speed.
The data size is easy to obtain from the data ﬁle, while
the dead time can be extracted from event time-stamps.
Let ∆t be the time interval between neighboring events(see Fig. 1). If we acquire a large amount of data, wecan expect some events to be accepted immediately after
the dead time is ﬁnished (like event 3 in Fig. 1).

(a) AMT-VME(b) V1190

Fig. 3.: Plot of ∆t versus data size for source test data.
AMT-VME TDC has a larger slope, implying that its
performance is worse.
AMSC AMT-VME TDC and CAEN V1190 TDC, respectively. We performed a source test for each TDC
and plotted the resulting 2D histograms. The edge in theAMT-VME’s histogram exhibits a sharper slope, which
implies that the dead time of this module quickly increases when the data size increases. We emphasize herethat it would be very diﬃcult to recognize this performance diﬀerence through an oscilloscope measurement.
We applied this method to optimize DAQ for theSAMURAI 11 experiment.2) According to the results
shown in Fig. 3, the DAQ speed was limited by the TDC
modules, rather than the detectors. Therefore in the experiment, all AMT-VME TDCs were replaced by V1190
TDCs. Combined with other optimizations, such as thedisabling of unnecessary TDC headers, the typical deadtime in SAMURAI 11 was about 50 µs, and the accepted
trigger rate was about 5 kHz. This is a large improvement over previous experiments in SAMURAI, which
typically had a dead time of about 200 µs and accepted
trigger rate of about 1 kHz.
(This work is funded by the China scholarship Council.)
References

1) L. Stuhl et al., RIKEN Accel. Prog. Rep. 52, 33 (2018).
2) M. Sasano et al., in this report.
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