The Focussing-Lightguide Disc DIRC will provide crucial Particle Identification (PID) information for the P&#8254;ANDA experiment at FAIR, GSI. This detector presents a challenging environment for reconstruction due to the complexity of the expected hit patterns and the operating conditions of the P&#8254;ANDA experiment. A discussion of possible methods to reconstruct PID from this detector is given here. Reconstruction software is currently under development

A.T. Maas

B Seitz

B. Seitz .

D Glazier

E Cowie

G Hill

G Rosner

K Föhl

M Hoek

M Murray

M. Hoek

M. Kotulla .

PANDA computing group

R Kaiser

T Keri

T. Keri

The PANDA collaboration

English

Crossref

Reconstruction methods —
                    P̄ANDA

Cowie, E.

Föhl, K.

Glazier, D.

Hill, G.

Hoek, M.

Kaiser, R.

Keri, T.

Murray, M.

Rosner, G.

Seitz, B.

Enlighten

       Cowie, E., Föhl, K., Glazier, D., Hill, G., Hoek, M., Kaiser, R., Keri, T., Murray, M., Rosner, G., and Seitz, B. (2009) Reconstruction methods — P‾ANDA focussing-light guide disc DIRC. Journal of Instrumentation, 4 (09). P09005. ISSN 1748-0221   Copyright © 2009 IOP Publishing Ltd and SISSA.   A copy can be downloaded for personal non-commercial research or study, without prior permission or charge   The content must not be changed in any way or reproduced in any format or medium without the formal permission of the copyright holder(s)   When referring to this work, full bibliographic details must be given      http://eprints.gla.ac.uk/87852/       Deposited on:  20 November 2013          Enlighten – Research publications by members of the University of Glasgow http://eprints.gla.ac.uk 2009 JINST 4 P09005PUBLISHED BY IOP PUBLISHING FOR SISSARECEIVED: August 14, 2009ACCEPTED: August 24, 2009PUBLISHED: September 9, 2009WORKSHOP ON FAST CHERENKOV DETECTORS - PHOTON DETECTION,DIRC DESIGN AND DAQMAY 11-13, 2009, GIESSEN, GERMANYReconstruction methods —PANDA Focussing-Lightguide Disc DIRCE. Cowie,a K. Fo¨hl,b,1 D. Glazier,b G. Hill,a,2 M. Hoek,a R. Kaiser,a T. Keri,aM. Murray,a G. Rosnera and B. SeitzaaDepartment of Physics & Astronomy, Kelvin Building, University of Glasgow,Glasgow G12 8QQ, Scotland, U.K.bSchool of Physics, University of Edinburgh,Edinburgh EH9 3JZ, Scotland, U.K.E-mail: g.hill@physics.gla.ac.ukABSTRACT: The Focussing-Lightguide Disc DIRC will provide crucial Particle Identification(PID) information for the PANDA experiment at FAIR, GSI. This detector presents a challengingenvironment for reconstruction due to the complexity of the expected hit patterns and the operatingconditions of the PANDA experiment. A discussion of possible methods to reconstruct PID fromthis detector is given here. Reconstruction software is currently under development.KEYWORDS: Particle identification methods; Cherenkov detectors; Pattern recognition, clusterfinding, calibration and fitting methods; Analysis and statistical methods1Now University of Giessen, II Physikalisches Institut, Heinrich-Buff-Ring 16, D-35392 Giessen, Germany.2Corresponding author.c© 2009 IOP Publishing Ltd and SISSA doi:10.1088/1748-0221/4/09/P090052009 JINST 4 P09005Contents1 Introduction 12 PID in the focussing-lightguide disc DIRC 13 Possible reconstruction methods 23.1 Track independent methods 23.2 Track dependent methods 34 Preferred reconstruction method —Maximum likelihood 35 Conclusion and outlook 41 IntroductionThe PANDA experiment [1] at the proposed FAIR facility, Darmstadt, Germany, will employ de-tectors based on the DIRC (Detection of Internally Reflected Cˇerenkov light) principle for ParticleIdentification (PID) [2]. The end-cap Disc DIRC detector covers the forward angle range θ < 22 ◦,with the exception of an aperture of 5 ◦ vertically and 10 ◦ horizontally.Two competing designs are currently under investigation for the end-cap DIRC detec-tor [3, 4]: the Time-of-Propagation (TOP) Disc DIRC [5] and the Focussing-Lightguide Disc DIRC(FLDD) [6]. Both utilise a disc radiator geometry and differ in the methods used to reconstruct theCˇerenkov angle. These proceedings will focus on investigations into reconstruction methods forthe FLDD design. Details on this design can be found in [7]. The FLDD will reconstruct theCˇerenkov angle from two detector parameters using a pattern recognition technique, which in turnwill be used in PID.2 PID in the focussing-lightguide disc DIRCThe FLDD will reconstruct the Cˇerenkov angle from measurements of two parameters: the positionof the hit around the rim of the disc, φrim, and the position of the hit on the photon detection plane,p. Figure 1(a) shows a typical simulated event, illustrating the hit patterns obtained for variouscombinations of incident particle polar and azimuthal angles, θ and φ . Readout time informationwill be available for each hit in the FLDD design, allowing separation of overlapping hit patterns.This will reduce multiplicities and background and is illustrated in figure 1(b).PID in Cˇerenkov detectors is carried out by calculating the probability of the particle belongingto a particular species from the reconstructed Cˇerenkov angle. By calculating the particle veloc-ity, directly related to the Cˇerenkov angle, and including momentum measurements provided byreconstruction from external detector systems the particle species can be identified.– 1 –2009 JINST 4 P09005(a) Overlapping patterns (b) Time separated patternsFigure 1. (a): Simulated hit patterns for the Focussing-Lightguide Disc DIRC for various combinationsof incident particle polar and azimuthal angles, θ and φ . (b): Separation of overlapping hit patterns usingreadout time information. This will reduce multiplicities and background in the reconstruction.3 Possible reconstruction methodsReconstruction methods for the Cˇerenkov angle can be categorised as either “track independent”or “track dependent”. In this section the benefits and drawbacks of these methods are discussed, inparticular with regards to their suitability for implementation for the FLDD.3.1 Track independent methodsReconstruction techniques which do not rely on track reconstruction information from external de-tector systems are known as track independent. Track parameters such as momenta, angles andvertex positions are not used in the reconstruction of the Cˇerenkov angle, instead relying on com-plex reconstruction algorithms to reconstruct from raw data from the detector alone. Examples oftrack independent methods include Spatial Hough Transforms, Elastic Net algorithms and Statisti-cal Searches, described further in e.g. [8].These methods have the advantage that the reconstruction can be carried out online, as noexternal information is needed and raw data can be processed immediately. In turn, this can be usedto reduce data volume by rejecting unwanted events or tracks online and mapping onto parameterspace. This is considered to be advantageous for an experiment such as PANDA where the primaryinteraction rates are expected to be as high as 20 MHz [9] and no event level trigger is availabledue to the similarity between the obtained hitpatterns from proton-antiproton collisions and noisein the spectrometer.However, track independent methods have many disadvantages. A very complex method mustbe employed on a short timescale, with an excellent knowledge needed of the expected patternshape and the detector performance. For the FLDD the expected pattern shape may not be easilyparameterised as a simple parabola or low order polynomial function, thus making track indepen-dent methods difficult to implement.For online processing there are further complications in the case of the FLDD. A complexreadout would be required with very fast timing and comparatively high onboard processing capac-ity. There is limited space available for onboard electronics in the design specifications of the Target– 2 –2009 JINST 4 P09005Spectrometer [10], where the FLDD will be installed, thus making this approach unfavourable. Fi-nally, separation of overlapping patterns would be difficult in an online track independent method,leading to a high likelihood of false PID results.3.2 Track dependent methodsIn contrast to track independent methods discussed in the previous section, track dependent meth-ods use external tracking information in the reconstruction. This has many advantages over thetrack independent approach. Track parameters can be used to limit the pattern search area and thushelp reject noise hits. The parameterisation of the pattern is less important for this method and thealgorithms employed are generally faster. Examples of track dependent methods include (In)DirectRay Tracing, Yield Determination, Pattern Comparison Angular Hough Transforms, described fur-ther in e.g. [8].Generally for this method a comparison is made between the measured Cˇerenkov angle andpossible Cˇerenkov angles for each possible particle type calculated from the externally recon-structed track parameters. These possible angles are generated from either Monte Carlo simulationsof the experiment or from test beam reconstruction. The most probable result identifies the particletype of the track.An obvious disadvantage of this approach is that it must be carried out offline to first al-low the reconstruction of the tracking detector information. As the final decision of online oroffline reconstruction has still to be made, offline reconstruction is the preferred approach for theFLDD reconstruction.4 Preferred reconstruction method —Maximum likelihoodThe method currently in development for PID in the FLDD is based on the principle of maximumlikelihood fitting. This method was employed by the BABAR collaboration in the reconstructionof the BABAR DIRC detector [11]. The Cˇerenkov angle is reconstructed for each detected photon,with a maximum likelihood fit based on known detector performance parameters employed tocalculate the most probable PID result.Two algorithms were developed for the BABAR DIRC. A local track-based algorithm, witheach track fitted individually, was used for alignment of the detector. A global, event-based algo-rithm fitted each track within an event simultaneously, giving the highest performance and allowingnoise and alignment information to be taken into account. A similar approach is proposed for theFLDD, allowing for alignment information to be used for the best performance in PID.Furthermore, the maximum likelihood approach is comparatively simple to implement in thePANDA software framework, known as PANDARoot [12]. PANDARoot includes the CERN soft-ware packages ROOT [13] and MINUIT [14], the latter a minimisation package important in themaximum likelihood method. Investigations are ongoing into the applicability of this approach tothe FLDD design.– 3 –2009 JINST 4 P090055 Conclusion and outlookThe FLDD will form an integral part of the PANDA PID system. Reconstruction software is cur-rently in development based on a track dependent maximum likelihood algorithm, having beenproposed as the most suitable based on the specifications of the detector and past employment indetectors based on the DIRC principle. A simulation of the detector has been included in the PAN-DARoot software framework and reconstruction algorithms are currently under development in thisframework, using the latest geometric description of the detector. Preliminary studies suggest thatthe detector reconstruction will operate within the desired performance criteria.AcknowledgmentsThe authors would like to acknowledge financial support by the UK Science and Technology Facil-ities Council (STFC) and the EU FP6 grant, contract number 515873, DIRAC-secondary-Beams.References[1] M. Kotulla et al., Technical progress report for: PANDA strong interaction studies with antiprotons,PANDA Internal Report, February (2005).[2] G. Schepers et. al., RICH for PANDA, Nucl. Instrum. Meth. A 598 (2009) 143.[3] K. Fo¨hl et al., The DIRC detectors of the PANDA experiment at FAIR, Nucl. Instrum. Meth. A 595(2008) 88.[4] B. Seitz et al., The DIRC detectors of the PANDA experiment at FAIR, in the proceedings of the 1stinternational conference on Technology and Instrumentation in Particle Physics, March 11–17,Tsukuba, Japan (2009), accepted for publication in Nucl. Instrum. Meth. A.[5] P. Scho¨nmeier et al., Disc DIRC endcap detector for PANDA@FAIR, Nucl. Instrum. Meth. A 595(2008) 108.[6] B. Seitz et al., A focussing disc DIRC for the PANDA experiment, IEEE Nucl. Sci. Symp. Conf. Rec. 3(2007) 2198.[7] M. Hoek, The PANDA Focussing-Lightguide Disc DIRC, in the proceedings of the Workshop on fastCherenkov detectors — Photon detection, DIRC design and DAQ, May 11–13, Giessen, Germany(2009), to appear in JINST.[8] A.T. Maas, Particle identification with RICH detectors: algorithms and their optimization. A casestudy on the experiments CERES and HERMES, DESY-THESIS-2000-051.[9] THE PANDA collaboration, . M. F.M. Lutz, B. Pire, O. Scholten and R. Timmermans, Physicsperformance report for PANDA: strong interaction studies with antiprotons, arXiv:0903.3905.[10] T. Keri, The front-end readout electronics for PANDA Focussing-Lightguide Disc DIRC, in theproceedings of the Workshop on fast Cherenkov detectors — Photon detection, DIRC design andDAQ, May 11–13, Giessen, Germany (2009), to appear in JINST.[11] BABAR-DIRC collaboration, I. Adam et. al., The DIRC particle identification system for the BaBarexperiment, Nucl. Instrum. Meth. A 538 (2005) 281.– 4 –2009 JINST 4 P09005[12] PANDA computing group, A data analysis and simulation framework for the PANDA collaboration,GSI Scientific Report (2006).[13] R. Brun and F. Rademakers, ROOT: an object oriented data analysis framework, Nucl. Instrum. Meth.A 389 (1997) 81.[14] F. James and M. Roos, Minuit: a system for function minimization and analysis of the parametererrors and correlations, Comput. Phys. Commun. 10 (1975) 343.– 5 –

Reconstruction methods — P&#8254;ANDA focussing-light guide disc DIRC

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Reconstruction methods — P‾ANDA focussing-light guide disc DIRC