Ⅱ-9. Instrumentation

RIKEN Accel. Prog. Rep. 49 (2016)

SpiRITROOT: an analysis framework for the SπRIT experiment
G. Jhang,∗1,∗2 J. W. Lee,∗1,∗2 G. Cerizza,∗2 T. Isobe,∗3 and M. B. Tsang∗2 for the SπRIT collaboration
The main detector in the SπRIT(SAMURAI pion
Reconstruction and Ion Tracker) experiment is theSπRIT-TPC (time projection chamber)1) . The GETelectronics2) as front-end readout electronics reads thecharge and time with 12,096 pad electrodes of the TPC
and stores structured binary-encoded data. To decodeand analyze this data in a consistent and systematicmanner, we provide a software framework to the collaborators that accounts for both simulation and dataanalysis.
SpiRITROOT is the analysis framework for theSπRIT experiment developed on the structure of FairRoot4) . FairRoot provides a highly modulized programming environment that allows developers to maintain the code in an organized manner and to simplify
the analysis process for users. SpiRITROOT has twoparts: the simulation and the experimental data flow.
A Schematic of these parts is shown in Fig. 1. Each
rectangle indicates a stage inside the software framework. Each stage has entries (blue text) corresponding
to tasks.
In the simulation data flow, collision events generated by external programs such as PHITS5) andpBUU6) are fed to SpiRITROOT to simulate the detector response through the GEANT4 package. After
simulating the events, the digitization stage takes overwith its three tasks: drift task, pad response task, andelectronics task. The drift task calculates the numberof ionized electrons, their drift time, and their dispersion from the ionized point. The pad response task
calculates the amplification and dispersion by the anode wires. Finally, the electronics task simulates theresponse pulse from the electronics using a reference
pulse measured from the cosmic data.
In the experimental data flow, the data are read by
the unpacker stage. The GETDecoder task unpacks
the binary data, while the STConverter task maps thesignals of each channel to each pad. In addition, theANAROOT task has been added to unpack and decode
data from RIBFDAQ, which contains information fromthe KATANA and multiplicity trigger arrays7) .
The reconstruction stage is common to both dataflows. The pulse shape analysis task analyzes theshaped pulse from the electronics to find the hit position and energy loss. The hit clustering task collects groups of hits satisfying certain criteria, such as
proximity between hits, into clusters to determine theclosest position to the ionized point. The linear tracking task, developed mainly to identify tracks and an∗1
∗2
∗3

Department of Physics, Korea University
NSCL, Michigan State University
RIKEN Nishina Center- 161 -alyze the commissioning experiment data, is used tofit the straight tracks and determine the reaction vertices. The Riemann tracking task identifies and separates the tracks in the event and provides approximate
track parameters, such as the curvature and dip angle,
using a simple helix fit. Finally, the GENFIT9) task
analyzes the tracks using a Kalman filtering algorithmand provides information on physical observables suchas momentum, mass, and charge to perform particle
identification taking into account the energy loss.
SpiRITROOT is working successfully in analyzing
the experimental data. One of the useful tools provided to the users for online analysis is the event display, which allows to visualize the reconstructed eventsin 3D. The brief report on commisssioning experimentand one example of 3D-reconstructed collision eventare presented in Ref.8)
Simulation
GEANT4

Experimental
Data

Digitization

Unpacker

Drift
Pad Response
Electronics

GETDecoder
STConverter
ANAROOT

Reconstruction
Pulse Shape Analysis
Hit ClusteringLinear Tracking
Riemann Tracking
GENFIT
Simlation
data flow

PhysicalObservables

Experimental
data flow

Fig. 1. Simple description of the data flow in SpiRITROOT. Both the simulation and experimental data are
analyzed in the single framework.

References
1) R. Shane et al., Nucl. Instr. Meth. A 784, 513 (2015).
2) E. Pollacco et al., Phys. Procedia 37, 1799 (2013).
3) T. Isobe et al., in this report.
4) M. Al-Turany et al., J. Phys. Conf. Ser. 396, 022001(2012).
5) T. Sato et al., J. Nucl. Sci. Technol. 50:9, 913-923(2013)
6) J. Hong and P. Danielewicz, Phys. Rev. C 90, 024605(2014).
7) M. Kaneko et al., in this report.
8) M. Kurata-Nishimura et al., in this report.
9) C. Höppner et al., Nucl. Instr. Meth. A 620, 518 (2010).