The Software Development For The Silicon Detector Data Analysis at ANKE-COSY.

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1 TechNotes/TechNote15 October 18, MMX G.M. Jülich 2010 The Software Development For The Silicon Detector Data Analysis at ANKE-COSY. August 2010 G. Macharashvili Contents 1 introduction notations and definitions The local coordinate system definition detector description global coordinate system profile and configuration file configuration data correction factors selection parameters simulation control parameters the setup data concerning the simulation run summary data XmlHandler library simulation primary interaction particle interaction with matter visualization some technical details analysis software overview advantages of the analysis software

2 6 primary (raw data) analysis 17 7 energy calibration 18 8 tracking analysis energy cuts neural network method global coordinate system origin (target center) definition beam polarization measurement in p d pd at 45MeV A y measurement in p d p sp ppπ at 353MeV summary Appendix A. typical detector configuration file Appendix B. classes overview class STTDetector class XmlHandler class STTEnergyCalibratoion class TelescopeTrackingAnalysis class DynamicHit class DynamicHitEvent class VTrack class VTrackList Appendix C the neural network method basics class XStop

3 1 introduction At the ANKE spectrometer [1] a vertex detector consisting of two- or three- layer silicon telescopes has been developed [2]. The telescopes were used in the measurements at intermediate energies to detect low energy particles (mainly protons and deuterons) at large scattering angles in lab. system. The PAX collaboration in the framework of PAX Project [3, 4] recently suggested to study the polarization build-up in an antiproton beam at the AD-ring of CERN at energies in the range of MeV [5, 6]. First step of this study will be done with a proton beam at COSY. The polarization build-up will be achieved by spin-filtering, i.e., by a repetitive passage of theproton(antiproton)beamthroughapolarizedatomichydrogenordeuteriumgastarget 1. The spin-filtering with protons aims to confirm the results of the FILTEX experiment [7] and determine the pp spin dependent cross sections at 50MeV. The second phase will be realized in the Antiproton Decelerator ring (AD) at CERN to polarize antiprotons for the first time using spin-filtering. The experimental setup requires a Polarized Internal gas Target (PIT) [8]. To proceed the measurements we intend to use the silicon telescopes placed inside the accelerator beam-pipe to detect protons, deuterons and antiprotons. Most important task was to estimate antiproton detection peculiarities in the silicon detectors. AttheSpin-FilteringFacilityatCOSYwealsoplantomakeinparellelanextensivestudy of analysing powers and spin correlation parameters in pd breakup reactions at energies between 30 and 50MeV, an energy range where previous measurements are scarce and limited while three nucleon effects are expected to be significant [9]. Spin observables in proton deuteron breakup reactions at low energies offer a reach testing ground for the modern theory of nuclear forces, the chiral effective field theory (EFT) [10]. For the three nucleon continuum the experimental data and the theoretical predictions are today at variance. Most important reaction parameters (kinematicaly independent) are φ n - the reaction plane normal, and ϑ - the scattering angle. Reliable and precise reconstruction of these parameters in each event makes possible to measure any spin observable using the beam and the target of appropriate directions of polarization. In this report we discuss the software developed for the data analysis of experiments at the ANKE spectrometer and the simulation software used to design and optimize the detector for the PAX project and the Spin-Filtering facility at COSY ring. 1 note that σ σ 3

4 1.1 notations and definitions In this section we give some term definitions and corresponding classes common with Root- Sorter. module - is a double-sided silicon sensor with P- and N- strips on the sides. One silicon module is sometimes termed as detector and corresponds to RootSorter class ASiSTDetector. side - each module (detector) object contains two P- and N- sides 2. side corresponding class in RootSorter library is ASiSTSide. segment - is the collection of certain number of strips physically connected to one electronics channel. Each side object consists of a sorted list of segment objects. The segment object has the following attributes boolean: noisy, use, not connected, double: Pitch, Threshold, ThresholdNeighbor, Pedestal, QDCRaw. Each segment object has its own calibration function descriptor (4-th order polinomial) and the corresponding fitted parameters Cal[5]. The calibration parameters are defined at the test-pulse calibration procedure which is aimed to correct and linearize the QDC signal to E conversion. Some segments can have thir own pitch different from the common one for all segments of the side. The segment object accesses also the electronics channel descriptors such as: QDCSubSystem, QDCSlot, QDCChip, and QDCChannel. telescope - is a set of two or three modules arranged in order to detect tracks originating from the target. telescope corresponding class in RootSorter library is ASiSTTelescope. Sometimes we use a term sector (especially for the setup aimed for the Spin-Filtering measurements) to enumerate the direction covered by the particular telescope. The telescope covering the sector with x > 0 and y > 0 is identified as 0-th, with x < 0 and y > 0 as 1-st, etc. The telescope object consists of a list of two or three layer objects. layer - is a set of modules placed at the certain distance with respect to the target. layer corresponding class in RootSorter library is ASiSTLayer. The layer closest to the target is identified as 0-th layer. The term layer sometimes concerns to a neural network method but the case easily recognized by a context. cluster - is a set of fired neighbouring segments of a side. Clusters are constructed by scanning all hitted segments of the side. 2 Positive charge signals are obtained from the p-doped side of a silicon detector (module), whereas negative charge signals are obtained from the n-doped side. 4

5 hit - is a common term to refer the objects of 1-dimensional(ASiSTHit1D) or 2-dimensional (ASiSTHit2D) hits in a side (1-d) or in a detector (2-d), respectively. ASiSTHit1D or ASiSTHit2D object contains the cluster density center coordinates x c or x c,y c and the N-side and P-side energy deposits. 1.2 The local coordinate system definition N-side (long) corresponds to the local x cordinate with the origin at the module center and is directed from a segment with the lowest number to a segment with the highest number. P-side (short) corresponds to the local y coordinate with the origin at the geometrical center of the module and is directed from a segment with the lowest number to a segment with the highest number. The local z coordinate is directed along the module thickness with direction corresponding to the right-handed cartesian coordinate system. The local coordinate system definition differs from that used in the Root- Sorter framework 3. 2 detector description STT1 STT2 Cell Figure 1: The ANKE setup at the COSY ring. The three-layer version of the vertex detector is shown. The silicon telescopes are used in the same way with the cluster and the cell targets. 3 In the RootSorter framework P-side corresponds to local x and N-side corresponds to local y 5

6 ANKE is an experimental facility for the spectroscopy of products from proton and deuteron induced reactions on internal targets. It has been implemented in the accelerator ring of the cooler synchrotron COSY of the Forschungszentrum Jülich(FZ-Jülich), Germany. The device consists of three dipole magnets, various target installations and dedicated detection systems. It enables a variety of hadron-physics experiments like meson production in elementary proton-nucleon processes and studies of medium modifications in proton-nucleus interactions. The ANKE spectrometer operates at COSY proton and deuteron beams of energies MeV. A detailed description of the ANKE setup is given in Ref.[1]. The silicon telescope is placed inside the target box mounted just in front of the spectrometer magnet. The two major configurations of silicon telescopes are considered. At the ANKE spectrometer the silicon telescopes [2] were installed in the beam vacuum pipe symmetricaly with respect to the cluster jet target to the left and to the right of the beam direction (see Fig 3. Each telescope consisted of two or three position-sensitive detectors (layers), oriented parallel to the beam direction. For the beam polarization measurement we used the three-layer telescopes with the layers of thicknesses 0.3, 0.3, and 5.0mm, counting from the beam. The first layers were placed as close to the cluster jet target as 30 mm. Within the mechanical constraints of the detector support, the telescope positions with respect to the interaction region were chosen to optimize the figure of merit for the p d analyzing reaction at around 45MeV beam energy [29]. For the A y measurements in p d p sp ppπ at 353MeV the two-layer version of the telescopes was used. In this case the 1st and the 2nd layer thicknesses were 70 and 300µm respectively. The 1st layers of the both telescopes are placed at a distance of 28mm to the beam axis and the 2nd layers at 48mm. For the spin-filtering experiments at COSY and CERN-AD we proposed the four-sector telescope setup as shown in Fig.2. The same detector is intended to be used for low energy deuteron breakup measurements[9]. The two-layer telescopes surround the polarized atomic Hydrogen/Deuterium target cell [8]. 2.1 global coordinate system The physical three-dimensional coordinate system is connected with the global system with the origin at the target (beam-target crossing area) center 4. z-axis of the global system is directed along the beam, y-axis upwards, and x-axis in a way to form the right-handed cartesian system. The local 2-D coordinate system of a layer is defined as follows. The local system origin is located at the geometrical center of a layer. The coordinates are directed from a segment 0 to a segment with maximum number. The local coordinates are converted to the global system and vice versa by means of certain affine transformation. 4 The target center is defined as the mean value of the reconstructed track coordinates at x = 0. 6

7 Figure 2: Detector for the spin-filtering measurement with four-sector silicon telescopes. The target forward flange is not shown. The cell walls are shown in violet (Teflon), the target mechanical parts in sky blue (Aluminum), and the sensitive modules in yellow (Silicon). The second layer is shifted in the forward direction in order to increase the geometrical acceptance (by 10%). 7

8 Figure 3: Existing silicon telescopes in their 3-layer version mounted at the ANKE spectrometer. The beam-target crossing area is shown by the up arrow. The thicknesses of the 1st and the 2nd layers are exagerated to make them visible. 3 profile and configuration file As shown in Fig.4 all the analysis codes work in the same environment described in the profile and configuration file. All parameters necessary for the data analysis and the simulation codes are kept in common configuration files associated to each data file (termed sometimes run). The files contain different types of data, such as configuration, selection parameters (parameters cut values), simulation specific, correction factors, and run summary data. The configuration files are generated for each run to avoid long-term instability. We used the xml [15] language to describe all the data in order to have a possibility to check these files and make changes/additions of some parameters in a standard way. The profile and configuration files are organized as a hierarchical structure in the same way as the whole setup description(class ASiSTSetup) is organized. The setup configuration consists of run global data 5 and the list of the telescope objects. Each telescope structure contains the telescope specific data and a list of the detector objects. Each detector structure contains the data described in detail in sec.(12). It has to be stressed that the module correspondent element 6 name is Detector and a telescope element name is Telescope. 3.1 configuration data The configuration data is extracted partially from the RootSorter setup files and added partially by the primary analysis code. The following configuration data are kept in this section: 5 such as run number, run start/stop times, number of events, etc. 6 xml term 8

9 naming and identification. For the naming scheme we keep the RootSorter conventional scheme: e.g. STT1 2 is equivalent to the identification telescope=0 and layer=1. box shape module sizes. Measurement unit can be specified (defailt mm ), positioning of the module center in the global coordinate system, rotation around x, y, and z axes. Default unit deg. number of segments in P- and N- sides and the pitch size (defailt unit mm ) ASiSTReferencePoint object of RootSorter is saved. 3.2 correction factors The following correction factors are kept in the configuration files For each telescope the geometry correction factors are defined by the primary analysis code and written to the file. It means that the beam-target crossing areas must have the same center (z, y) of coordinates for all telescopes. calibration correction factors(common for each module element CalibrationCorrection ) are defined and written by the calibration analysis code, although the preliminary version of the calibration correction factors are defined and written by primary analysis code (the element CalibrationCorrectionFixed ). The calibration code defines also the asymmetry correction factors for each module. The factor corrects the N-side signal in order to equalize the P- and N- side energy deposits. The signal asymmetry correction factor is less efficient. 3.3 selection parameters In the configuration files we keep also some cut parameters to be used in the subsequent analysis procedures. These parameters can by modified by a user. The deposited energy cuts can only be increased. Minimum energy cut for each module (element Detector ) used in the primary analysis code. The primary E cut value is selected less than an actual E min cut value used at the tracking analysis. P- and N- side energy deposit asymmetry cut, The vertex selection cuts on yz plane, 9

10 3.4 simulation control parameters geant4 simulation code control parameters which can be accessed through the configuration file: the tracking limitation factors (see Table. 1), various cut parameters ( E, vertex z o, y o, etc), volume definitions, affine transformation matrices, material properties, intrinsic resolutions (uncertainties) such as: energy, coordinate (pitch), time, ϑ, φ, etc; 3.5 the setup data concerning the simulation The simulation code uses the same geometry and positioning parameters that the analysis code taken from the configuration section. For the simulation code the files also contain data on materials, its densities, cut values, etc. element G4CSGSolid to store a solid of any shape 7 and the corresponding sizes. The affine transformation data mentioned in sec.(3.1), The material name and density, the detector intrinsic resolutions (energy, time, coordinate) can be specified in the section. Any other detector parameter can also be added to this section. the visualization control and color and/or transparency of the sensitive detector 3.6 run summary data In this section we store the run number, number of events, and the number of reconstructed tracks. We keep in the profile and configuration file the function corresponding to the simulated most probable energy dependence E 1 = f( E 2 ). 3.7 XmlHandler library Extensible Markup Language (XML) is a set of rules for encoding documents in a machinereadable form. It is defined in the XML 1.0 Specification produced by the W3C[15], and several other related specifications, all gratis open standards. XML s design goals are to emphasize simplicity, generality, and usability over the Internet. It is a textual data format 7 not all geant4 solids can be generated yet 10

11 with strong support via Unicode for the languages of the world. Although the design of XML focuses on documents, it is widely used for representation of arbitrary data structures, for example, in web services. XmlHandler library has beed developed to have a standartized access to the configuration files. The library code provides the following options: (i) any parameter can be added/modified for each detector by analysis code, to change dynamically the file structure without influencing the parameters used in other codes, (ii) the parameters naming scheme and access are file structure independent (i.e. no DTD 8 /Schema 9 [15] files required), (iii) some simulation and analysis parameters are initialized in the same way, (iv) the configuration files can be edited(the xml language is widely used standard). The typical configuration file contents is shown in Appendix A. All these parameters written in the profile and configuration files can be accessed from the analysis codes using the class STTDEtector (see Appendix B 13.1). Xmlhandler library has to be used directly to get an access to the user defined ( nonstandard ) parameters. 4 simulation The dominant objective of the simulation is the detector design and optimization in order to effectively detect pp, pp, and pd interaction events at low (30 100MeV) and medium (up to 3GeV) energies at the COSY ring [19]. At the same time we had to foresee a possibility to use the same detector (probably with minor modifications) at the CERN-AD (antiproton decellerator) ring. The simulation tasks can be subdivided as follows: (i) to optimize the detector geometry and positioning in order to maximize the geometrical acceptance and to minimize the uncertainties of the kinematical variable measurement, (ii) to estimate the expected detector performance, the electronics and the data processing software efficiencies, etc. (iii) to estimate the influence of the misalignment of the detector components on its performance, and to define the geometrical tollerance, (iv) on-line procedure development in order to continuously analyse the beam and/or target polarization, background value, and to monitor parameters that affect the data taking. (v) another role of the simulation software is to provide a generated input data to test and tune the reconstruction and the analysis tools. The simulated events data-base makes also possible to develop the data analysis software using generated event model. 8 document type definition 9 the successor of DTD 11

12 (vi) The simulation code provides a possibility to generate events for a specific detector configuration and produce a file which can be used for the neural net (see Section 14.1) class generation. The output file has a special format and controls the neural network training and test procedures. At the training and test it is possible to estimate the quality of the fit, and if necessary to change/modify the neural network configuration. After the training procedure finishes successfully the XStop97 class is generated. The generated cleass included to the common library. the simulation study has been carried out in order to estimate the p interaction with silicon detector layers of different thickness [26], where experimental data are scarce. If antiprotons pass the detector layer, the energy deposition does not differ from that for protons. In case of antiproton annihilation the deposited energy is almost independent on the kinetic energy of the antiproton. It depends mainly on the thickness of the layer and the annihilation depth. In thin layers most of the ionizing secondaries leave the sensitive volume. The simulated antiproton-proton interaction data makes possible to develop the background rejection cryteria. The main problem in pp elastic scattering detection is the background produced by pp inelatic interaction. The developed event reconstruction algorithms for pp are almost the same as for that in pp interaction. The original Monte-Carlo simulation software is based on the geant4 framework [12]. For the data analysis we also use the root class libraries [13]. The detector geometry for the simulation software is defined by the detector configuration file (see Appendix 12). The configuration file contains common data for analysis and simulation. It also contains the volume descriptors, positioning, rotation, material properties and the intrinsic energy and time resolutions, etc. 4.1 primary interaction We use the term primary interaction to refer to the beam and the target interaction, in contrast with secondary interactions of particles with the detector materials. The interaction point is generated according to the beam transverse distribution and the target density based on the experimental data. The primary interaction generators has been developed to generate the interactions pp, pp and pd depending on the spin orientations. The following primary interaction generation models are used: (i) pp - elastic scattering events are generated using the SAID [17] predictions data base including beam or target polarization in the beam energy range of MeV. Inelastic interactions (pp ppx) are generated using the phase-space model. (ii) pd - elastic scattering is generated using the approximated experimental data for different energies [21]. The low energy deuteron break-up according to the phasespace model. The effective mass of the proton-pair (m pp ) is generated uniformly in the 12

13 kinematicaly allowed interval (0,m max pp ). In the c.m. system a two-body decay pd n + (pp) is generated isotropically. The proton-pair (m pp ) is decayed isotropically in the pair rest system. All kinematical parameters are generated uniformely in the corresponding intervals at MeV proton beam energy. For pd pd elastic scattering with the beam polarization we use the experimental data from Ref. [21] at 49.3MeV beam energy. (iii) pp - annihilation, elastic and charge-exchange interactions of antiproton are generated by the originally developed experimental data driven model [19] according to the available experimental data and by the theory driven model [20]. All necessary information for the data-driven model has been taken from the review [22] and the references therein. pp interaction is branched to three types: elastic, inelastic (annihilation), and charge-exchange. In the primary pp interaction events all branches are presented: for pp pp the model [20] is used, for pp nn charge exchange scattering the experimental data driven model is used [22], and for pp X secondaries are generated using the CHIPS model [23]. The partial intensities of these three types of reactions are taken from Ref.[22]. 4.2 particle interaction with matter Electromagnetic and hadronic interaction of the secondary particles with the detector materials, are generated using the geant4 low energy hadronic physics models [14, 24]. The so called physics-list, necessary for the simulation code, is configured for low energy model (geant4 model below 25 GeV). The ionization losses and the multiple scattering processes are activated for all charged particles. The physics processes simulation package contains the chiral invariant phase-space decay model (CHIPS) which makes possible to simulate antiproton interaction and/or annihilation in different materials [23]. The main part of most hadronic physics list consists of high energy and low energy parameterized models. They cover all the long-lived particles at all incident energies. These models are fast but usually not very detailed. Better models exist, but they can not be applied to all particles at all energies. The Bertini cascade model is clearly better in the energy range 0 10 GeV [40]. For generation of low-energy hadron-nuclei interaction the Bertini cascade model is used. The Binary cascade introduces a new approach to cascade calculations, based on a detailed 3-dimensional model of the nucleus, and on binary scattering of reaction participants aon nucleons. In some sense this makes it a hybrid of a classical cascade code, and a quantum molecular dynamics model [39]. It is valid for protons, neutrons, pions, kaons, and hyperons, but is not valid for anti-baryons. 13

14 4.3 visualization The simulation code provides several visualization capabilities (drivers). Most useful is the OpenInventor visualization driver. OpenInventor drivers and the Hepvis class library are based on the well-established OpenInventor technology for scientific visualization [41]. They have high extendibility and support high interactivity. OpenInventor technology provides a graphical interface to move/rotate 3D image of the objects (the setup), magnifying any part of the scene. That is most important to dynamicaly check the detector volume shapes, possible incorrect placements, and volumes overlapping. OpenGL is an interface to the de facto standard 3D graphics library. It is well suited for real-time fast visualization and demonstration. The DAWN driver (the code working on a specialized detector description file) is convenient to produce high quality printable detector images, make cuts on the volumes. 4.4 some technical details We optimized the simulation code in order to speed-up the event processing and to simplify the particle tracing strategy. The G4UserLimits [42] class is initialized with the following parameters: parameter unit value Allowed maximum step mm Allowed maximum track mm Allowed maximum time ns 10.0 Minimum kinetic energy cut M ev Minimum range cut µm 1.0 Table 1: G4UserLimits class initialization parameters For low energy simulation (E < 25GeV) it is necessary to change some parameters which control the electro-magnetic interaction simulators using predefined tables (e.g. de/dx table precision). G4EmProcessOption class is used to tune the model limitations and some options. The code shown below explains the details. G4EmProcessOptions opt ; opt.setverbose(1) ; // Multiple Coulomb scattering // opt.setmscsteplimitation(fusedistancetoboundary) ; opt.setmscrangefactor(0.02) ; 14

15 // Physics tables // opt.setminenergy(1000*ev) ; // default 100*eV opt.setmaxenergy(25*gev) ; // default 100*TeV opt.setdedxbinning(400) ; // default 12*7 opt.setlambdabinning(250) ; // default 12*7 opt.setsplineflag(true) ; // default true // Ionization // opt.setsubcutoff(true) ; // default false //... Hadronic elastic and inelastic interactions of particles with matter are most complicated and model dependent. The hadronic interactions applied to all stable and long-lived 10 baryons, and all long-lived mesons. These are the particles that Geant4 can track and therefore requires processes to be assigned. Short-lived particles are not tracked, but they appear in some hadronic models, so a large list of resonances, quarks and diquarks is also defined. For the hadronic processes an extra level of detail must be addressed. Cross sections and physics models must be assigned to various processes before the processes are assigned to the particles. For hadron elastic scattering, the same process, G4HadronElasticProcess, is assigned to all the long-lived hadrons. The hadronic model which implements this process is G4LElastic, which has its origins in the GHEISHA model of Geant3. It is used for all incident particle energies. For hadron inelastic scattering, each long-lived hadron has its own process. Each of these processes is typically implemented by combination of two or more models. 5 analysis software overview The analysis software is organized as a set of functionally independent codes: primary data analysis, energy calibration correction, and tracking analysis. The execution of the primary analysis code, as has been mentioned above, is controlled by RootSorter framework software developed at IKP [18]. The calibration correction and the tracking (reconstruction) analysis are based on the original class libraries, developed as independent modules. Analysis (reconstruction) of the simulated data and the data collected at a beam-time are the same. Auxiliary software tools have also been developed for the detector components alignment, signal calibration, track and vertex reconstruction, kinematical fitting, physics analysis, etc. 10 geant4 specific term 15

16 The detector geometry for simulation and for reconstruction (the tracking analysis) are derived from the same detector description data base. The reconstruction of an event depends on the setup configuration and on the process being analysed. At low energies in some cases we reconstruct elastic and/or deuteron breakup events completely, whereas at higher energies we have to detect and identify particles, measure their 4-momenta, and in some cases we can use the kinematical constraints. raw data c2elite (RS) tracks (root) primary sorter hits (root) tracking code calib. code config (xml) Figure 4: The block-diagram of the analysis software. The data files are shown with shaded background. The analysis and the simulation software operation does not depend on the silicon detector configuration. The detector and the code configurations are initialized dynamically using the data bases (configuration files). The analysis software hierarchy and the working chain are shown in Fig.4. The code c2elite (RS), shown in Fig.4, is provided by RootSorter system to convert the raw data format event to the format which primary sorter accepts as an input (termed ems format). 5.1 advantages of the analysis software RootSorter based primary analysis code is run only once. it is possible to select which primary data should kept in the root file. the current version implies to save 2-dimensional (coupled 1-d) hit lists. the alternative is to save uncoupled 1-d hit lists for each side, or even the fired segment lists. The output files of the primary analysis code are of about 1GB size (10 7 raw events) and can be easily transfered between computers. 16

17 6 primary (raw data) analysis The raw data, collected at a beam-time, first are analysed by the primary sorter which is developed in the framework of the common code library RootSorter [18]. The primary sorter accepts all input events, selects useful segments data, and generates two-dimensional hits (class DynamicHit object). Each segment contains the energy deposit expressed in M ev. All calibration and correction procedures are done by the RootSorter code running just before the data access. Each layer with x and y segments (N- and P- sides) is analysed independently. At that point the calibration is not perfect, nevertheless the energy deposits are close to its true values. We made several modifications in the RootSorter code in order to optimize raw data analysis. Most important is the two-dimentional hit generation algorithm. The RootSorter class ASiSTHitGenerator has been replaced with a new version using completely different algorithm of N-side and P-side hits coupling. For each event N-side and P-side active segment lists are scanned separately in order to define clusters. The number of segments in a cluster is restricted to 4. The restriction to 4 active segments in a cluster was found optimal. Each cluster list is modified, reordering clusters to the newly sorted order with energy deposit decreasing. The two-dimensional (ASiSTHit2D) hit list is generated coupling 1st element of N-side ordered list with 1st element of P-side ordered list, 2nd element of N-side with 2nd element of P-side etc. So the two dimensional hits list is also ordered by energy deposit decreasing. The energy deposit asymmetry in N- and P-sides is not considered. Two-dimensional hits, belonging to layers, ordered by energy, are written to a root-file as an event (class DynamicHitEvent object). The event object contains also complete event identification data (i.e. time, flattop index, beam polarization index, target polarization index, trigger code, etc.). The root-file contains also some histograms showing the run profile. The primary sorter creates also the configuration files for each run. Configuration file contents are discussed in sec.(3). Some parameters necessary for consequent analysis are taken from the RootSorter configuration file, some are defined by the primary sorter itself, and some are added later by other analysis codes. The raw data analysis code also provides a possibility to define optimal selection cryteria for geometrical and kinematical parameters. The primary sorter carries out the most time consuming job to produce two-dimensional hit files and configuration files. In case of correctly tuned working parameters it has to run once. The raw data file conversion to the root-file squizes the data file more than 10 times, with minimum loss of the raw information. Neither RootSorter classes nor data-bases are necessary for subsequent analysis. The processes done by RootSorter code at the primary analysis execution: correct each QDC signal using the common mode correction method [18]. 17

18 convert the QDC signal to the energy deposit in a segment using the calibration parameters taken from ASiSTSetup configuration file. scan each side in order to define clusters and to generate 1-dim hits. couple 1-dim hits from each side of a module to 2-dimensional hits and store them. event header/identification data extraction. The following data is kept to be send to the output file: time, flattop index, beam polarization index, target polarization index, trigger data, BCT 11 value. 7 energy calibration The hit energy deposits written in the root-files are calibrated by means of RootSorter predefined calibration factors. These factors are defined with the so called test-pulse technique and/or with an α-source. The detector response calibration and scale linearization procedures are described in general in Ref.[16]. For absolute scale calibration the α radiation source is used. The absolute energy calibration procedure is applied to each detector segment (set of strips) before the detectors are installed at the experimental setup. After absolute energy calibration the dynamic range of the electronics chain response linearization has been performed. The procedures, applied to individual signals before analysis (such as pedestal subtraction, common mode correction, etc), may distort the signal to the deposited energy correspondence. So, for analysis the experimental data the calibration factors need fine correction. This is done by the calibration code (Fig. 4) which calculates fine correction factors for each layer and stores them to the configuration files. The primary sorter code, designed for tracking analysis, provides a possibility to define on-line the scale factors to convert the raw QDC counts to energy deposits [16]. The scale factors are defined individually for each side of a sensitive layer. The energy calibration code is an indepedent program and uses as input the configuration and hits root-files. As an output the code produces root files containing some plots showing the run profile and inserts the calibration correction factors to the configuration file. The code does not use any RootSorter library. First, the tracks are reconstructed using the hit root-files. The calibration code gets the geometry parameters provided by the primary analysis code from the configuration file of a particular run. Track is defined as a vector t = { E 1, E 2,ϑ,ϕ}, where E 1 and E 2 are the energy deposits in the two layers, ϑ and ϕ are the polar and the azimuthal angles in the global system, respectively. In case of the three-layer configuration of the telescope, 11 some parameter proportional to the beam intensity 18

19 the energy deposit in the third layer is added. After applying some selection criteria the track is checked whether it stops in the second layer. It is done using the special code (class XStop97) realizing the Artificial Neural Network conception/mechanism, generated at the simulation stage. The same class helps also to distinguish/identify the detected particle type (p/d). For all stopped proton tracks we plot the ( E 1, E 2 ) profile histogram. These profile histograms are used for the calibration fine correction factor definition. The calibration quality is checked at the off-line analysis stage comparing the experimental ( E 1, E 2 ) spactra to the simulated one. Actually we save the profile histograms to define the average values of E 1 for each E 2 bin. Comparing with the same profile histogram accumulated for the experimental data, we define c 1 and c 2 correction factors for the 1st and the 2nd layers, respectively (see Fig.6). These correction factors are kept in the configuration file belonging to the particular run. The calibration correction factors do not exceed (1 c i ) = 18%. In future we intend to modify the calibration correction procedure applying smarter correction methods. [MeV] E E 2 [MeV] Figure 5: Typical ( E 1, E 2 ) plot for pd interaction at 353MeV beam used for pd (pp) s nπ reaction analysis. 8 tracking analysis The root-files produced by the primary sorter are the data input for the tracking analysis. The code accepts also the configuration file belonging to the particular run, or in case of its absence it accepts the default configuration file. The configuration file besides the configuration data contains some control parameters for the tracking analysis, such as cut values, calibration correction factors, etc. First, a straight-lines are built in each telescope using three -dimensional hits in the 1st and the 2nd layers, combining the hits in the 2nd layer with all hits in the 1st layer. The hit analysis is performed for each sector (or telescope) independently. In case the track line 19

20 E 1 MeV E 2 MeV Figure 6: Profile histogram of energy deposits in the 1st and the 2nd layers. The red line shows the simulated energy deposit dependence. These two dependences are used to define the calibration correction factors for each layer. distance to z -axis is less than the cut value it is stored as a track candidate. The hits used to construct a track are removed from the hits list so that the subsequent algorithms are only run on the remaining hits.. In case the telescope consists of three layers, the third layer hits are scanned for the stored tracks in a narrow solid angle along the straight line reconstructed using the 1st and the 2nd layers. After that the reconstructed track list is analysed in a way depending on the event kinematics. For low energy interactions (pd -elastic or pd ppn -breakup) the following algorithm of event reconstruction is used. In case two tracks have been detected in the telescopes the crossing point of these two tracks is considered as a vertex. The following are the vertex reconstruction steps. A plane has been built on all three-dimensional hit points belonging to the reconstructed tracks using the linear regression method. In case of elastic scattering the plane is built with the constraint that it has to be parallel to z -axis. The method provides an estimate of goodnessoffit parametershowninfig.7. Theparameterisasquaresumofdistancesofthe pointstothereconstructedplane(wedenotetheparameterasχ 2 ). Allthesehitcoordinates are transformed to coordinates in the system connected with the plane. Distribution of distances of the hit coordinates to the plane is shown in Fig.8. The parameteris used to make a decision whether the reaction plane is reconstructed correctly. New straight-line tracks are reconstructed on the transformed two -dimensional coordinates in the reaction plane system. The crossing point of these two tracks is considered as a vertex. The track reconstruction algorithm has been implemented and tested on simulated events where it showed a good performance. 20

21 num. of events num. of events χ 2, relative units hit-point distance to plane, mm Figure 7: χ 2 for reconstructed plane. Figure 8: distribution of the hit-point distances to the plane. 8.1 energy cuts At the very begining ot the event analysis the minimum energy cuts are applied to the energy deposits in all layers. The energy cut values are taken from the configuration file. After the track is reconstructed, it is necessary to define the track kinetic energy and to identify the particle. For this we use the ( E 1, E 2 ) plot and apply cuts on the energies. To simplify the deposited energy cut on the ( E 1, E 2 ) plot we calculate a parameter [28]: 1 α = ( E 1 + E 2 ) 1.62 E , (1) in the two-layer version of the telescope. Typical 1/α dependence on E 1 + E 2 is shown in Fig.9. All particles with 1/α > 4.8 are considered as deuterons. Using the graphical cuts we can select stopped protons or deuterons. In some other cases a graphical cuts have been used for the particle identification. 8.2 neural network method The another possibility of particle selection was realized using the artifical neural network method. The mathematical concept of the neural networks are parallel processing units, that are trained rather than fitted. The neural networks generalize any functional dependence and perform very well with noisy data. Some basics on the neural network method are given in Appendix In order to identify deuterons in the overlapping region, to detect the stopped protons, 21

22 ] 1.62 [MeV 1/α E 1 + E 2 [MeV] Figure 9: Typical 1/α -parameter dependence on E 1 + E 2. Almost all events above 1/α > 4.8 correspond to the deuterons stopped in the second layer. The upper horizontal band corresponds to the stopped deuterons and the lower one to the stopped protons. etc, we introduce a function to determine the deuteron momentum. p NN = f(ϑ lab,φ,e 1,E 2 ). (2) Thefunctionisbasedonthefeed-forwardneuralnetworkconcept[31]. Orinotherwordsitis a nonparametric nonlinear approximation of a function defined by the smeared data table. To generate the table we performed pd pd simulation with the appropriate telescopes geometry and performance. The deutron momentum is also defined kinematically using the measured ϑ lab : p kin = f(ϑ lab ) (3) These two momenta p NN and p kin are stongly correlated for deuterons. Whereas for protons momentum is not defined by the angle so they may coincide only accidentally. These background protons(concentrated in the overlaping area) can not be eliminated. The class XStop97 12 provides a possibility of deuteron/proton identification at low energies increasing with good reliablity. 8.3 global coordinate system origin (target center) definition The tracking analysis is used to determine the telescopes positioning with respect to the beam-target crossing volume. The global coordinate system origin is defined as a wighted center of the beam-taget crossing volume. The telescopes positioning is corrected in order to have the common global coordinate system origin. The coordinate correction factors are written in the configuration files. 12 the class naming comes from the network configuration: 9 nodes in the first and 7-nodes in the second hidden layer. 22

23 The tracking analysis code by means of TelescopeTrackingAnalysis produces the track list (an object of VTrackList class) and writes it to a root-file which contains the track lists for each event. These files have to be used for event physical analysis in association with the data from other detector systems of the ANKE setup. 9 beam polarization measurement in p d pd at 45MeV Recently the silicon telescopes were used to measure the polarization of a proton beam circulating inside the COSY ring at the injection energy of 45MeV. The polarization was determined from the left-right asymmetry of the proton-deuteron elastic scattering on the deuterium cluster jet target. Scattered particles were detected by two telescopes installed inside the vacuum near the cluster jet to the left and to the right of the beam direction. Each of telescopes consisted of three double-sided strip detectors. Counting from the beam, the first and the second detectors had a thickness of 300µm, while the third one was of 5mm thickness. The first detectors were placed as close to the target as 30mm. In this configuration each of telescopes covered the angular ranges of 45 o < ϑ < 108 o and φ 50 o for scattering polar and azimuthal angles, respectively. The angle ϑ corresponds to deuteron scattering angle in lab. system. For up and down polarized beams we calculated the asymmetry using the geometric means of the counts of the telescopes in the ϑ ± ϑ interval [30]. The asymmetry is calculated using L1 L 2 R 1 R 2 ε(ϑ) = L1 L 2 + = P B cosφ A y (ϑ), (4) R 1 R 2 where L 1(2) corresponds to the counts of the telescope-1(2) located to the left with respect to the beam polarization vector, and R 1(2) is the same for the telescope-1(2) located to the right with respect to the beam polarization vector. P B denotes the absolute value of the beam polarization being measured. We assume that polarization modules are the same for beam up and down polarizations. A y (ϑ) is the analyzing power in elastic pd scattering at 45MeV taken from Ref. [21]. The calculation of the expression (4), used for asymmetry, eliminates the difference of the two telescopes accepted solid angles, efficiencies, and the difference of the luminosities for up and down polarized beams at first order. This guarantees that the fake asymmetry enters only in second order [30]. For the analyzing power approximation, as well as for the obtained asymmetry data, we used a parabola in the following form, ε(ϑ) = a(ϑ ϑ o )[1+b(ϑ ϑ o )] (5) where a, b, and ϑ o are the fit parameters in the ϑ interval 48 o < ϑ < 72 o. At first we 23

24 fitted Eq(5) to the experimental data taken from [21]. Then the parameters b and ϑ o were fixed and the same function has been used to fit the measured asymmetry points. So, we defined one single parameter a for our data. The fit curves are shown in Fig.10, in red for asymmetry and in blue for the experimental analyzing power data. Substituting these two fitted functions in Eq(4) with different a and a values we obtain P B = a a cosφ = 0.431± (6) It has to be stressed that the statistical uncertainty is dominated by the fit procedure, and not directly by the event sample. asymmetry MeV ptoton beam P B = ± measured asymmetry A y (pd pd), King et al., PL 69B (1977) deuteron θ lab, degrees Figure 10: Measured asymmetry of pd -elastic scattering (in red) and the experimental data on A y (in blue), used for the beam polarization determination, are taken from [21]. The experimental data points are smoothed by spline. The Silicon telescopes demonstrated quite good performance to identify protons and deuterons and to measure reliably the spin asymmetry in case of a polarized beam (or target). The described procedure for the beam polarization measurement was used also to estimate the beam depolarization caused by the proton-electron interactions at very low relative energies [29]. 10 A y measurement in p d p sp ppπ at 353MeV The second experiment where we used the Silicon telescopes was the analysing power measurement in pn ppπ by measuring the spectator proton energy in p d p sp (pp)π interaction at 353MeV beam at ANKE. 24

25 A y p d p sp (pp) π MeV cos θ π Figure 11: Analysing power A y in the reaction p p (pp)π at 353MeV proton beam energy at ANKE. The reaction has been identified by detecting of the spectator protons in the reaction p d p sp (pp)π and measure their energy (preliminary results). We used two-layer telescope (of 70 µm and 300 µm thickness, respectively) placed to the left and to the right with pespect to the beam direction. The telescopes were used to detect a stopped spectator protons and measure their energies. The analysis was carried out in order to identify protons stopped in the 2nd layer. To recognize stopped protons we used the neural network model. That provided a possibility to reconstruct the event kinematics and estimate the asymmetry. The obtained result is shown in Fig summary In this report we reviewed the software developed for analysis of the data detected with the silicon telescopes at COSY. (i) The off-line software system consists of three major parts: primary sorter, auxilliary correction codes (cut tuning, calibration correction, geometry, etc.), tracking analysis code. The software system contains also the simulation code in geant4 framework. It can be easily modified depending on a particular task. The software is organized in a way that the primary sorter has to run only once. This demands to save maximum possible information from the raw data files. The hits saved in the output root-files provide a possibility to reconstruct tracks using different selection cryteria. In the primary analysis code all the corrections foreseen in RootSorter library are done (such 25

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