II-9. Instrumentation

RIKEN Accel. Prog. Rep. 54 (2021)

RIKEN Accel. Prog. Rep. 54 (2021)

BYACO ecosystem for innovative online operation of BigRIPS
experiments with seamless connection to comprehensive analysis
T. Sumikama,∗1 Y. Shimizu,∗1 and H. Baba∗1Device and detector settings can be optimized online by using information based on histograms createdin an online analysis. The particle identiﬁcation (PID)
of a radioactive isotope (RI) beam is often necessary
to check the detector response. PID analysis is one of
the most important issues, especially for RI-beam tuning, to produce the required RI beams at the BigRIPS
fragment separator.1) RI-beam separation and PID are
diﬃcult for heavy or low-energy RI beams, since thecharge state of the RI beam could be diﬀerent fromthat of the fully-stripped ion and/or the accuracy ofthe energy-loss prediction may be insuﬃcient. For thePID analysis of RI beams having diﬀerent charge statesfrom that of the main RI beam or occasionally having
any charge states, comprehensive analyses including
ﬁne calibrations and consistency checks among diﬀerent RI-beam settings were performed after the experiments.2–4) In order to operate the BigRIPS separator
for these types of RI beams, it is desirable to performthe comprehensive analysis online.
The BeYond Analysis, Control, or Operation alone(BYACO) ecosystem is being developed for the innovative online operation of BigRIPS experiments byconnecting the comprehensive analyses seamlessly withother components such as device and detector controls
and data acquisition systems. BYACO connects eachcomponent using REST and WebSocket application
programming interfaces (APIs) by applying rapidly
evolving web technologies, as shown in Fig. 1. The
BYACO main server is built using Node.js5) and distributes a single-page application in the web browser,
which is written using the React JavaScript library.6)
The main server handles requests as the proxy server.
Other servers return a response for the forwarded request and can generate an event-driven request to others. To share information in real time, push notiﬁcations are sent using WebSocket technology. A client or
each server sends a list of interest to the main server.
When each server detects updates, they are sent torequesting clients through the main server.
The data analysis is divided into real-time and comprehensive analyses, as shown in Fig. 1. The rawdata calibration and PID reconstruction are processedin the real-time analysis. The obtained variables including the raw data are stored as a TTree object
in root.7) When waveform data are taken in the future, high-throughput processing will be necessary before the real-time PID reconstruction. For the comprehensive analysis, a root-based graphical user interface (GUI) analyzer for the BigRIPS experiment∗1Browser
interface
Sequencer
programming

REST API
WebSocket

Device(EPICS)
Detector

Database

BYACO
Main server

Comprehensive
analysisAnalyzed Data
TTree object (ROOT)DAQ
Raw data

Real-time
analysis

Highthroughput
processing

Fig. 1. Conceptual diagram of the BYACO ecosystem. The
REST and WebSocket APIs are used for the communication. The sequencer programming is for sequential
operations such as automatic RI-beam tuning.

(BigROOT) was developed. Macro programs used in
the oﬄine analysis are implemented as selectable tools
working on PROOF-Lite in root,7) which provides
functions for the event loop as well as histogram drawing and ﬁnal analyses. The GUIs to perform simple
curve ﬁtting and to make projection and proﬁle histograms have been implemented.
The sequential operation consisting of the data acquisition, analysis, device control, etc. needs to await a
response or status change. At present, the sequence of
these asynchronous tasks, which are performed by referring to many types of information, are managed using the Redux and Redux-Saga libraries8,9) on Node.js.
As the ﬁrst application of the sequential operation, theautomatic focusing and centering of RI beams weretested online in 2020 with great success.10)
References

1) N. Fukuda et al., Nucl. Instrum. Methods Phys. Res.
B 317, 323 (2013).
2) N. Fukuda et al., J. Phys. Soc. Jpn. 87, 014202 (2018).
3) T. Sumikama et al., Nucl. Instrum. Methods Phys.
Res. B 463, 237 (2020).
4) T. Sumikama et al., Nucl. Instrum. Methods Phys.
Res. A 986, 164687 (2021).
5) Node.js, https://nodejs.org/ .
6) React, https://reactjs.org/ .
7) R. Brun, F. Rademakers, Nucl. Instrum. Methods
Phys. Res. A 389, 81 (1997), https://root.cern.
ch/ .
8) Redux, https://redux.js.org/ .
9) Redux-Saga, https://redux-saga.js.org/ .
10) Y. Shimizu et al., in this report.

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