- Email: [email protected]
dx sampling
ARTICLE IN PRESS
Nuclear Instruments and Methods in Physics Research A 568 (2006) 359–363 www.elsevier.com/locate/nima
Hadronic particle identification with silicon detectors by means of dE/dx sampling M.G. van Beuzekom NIKHEF, P.O. Box 41882, 1009 DB Amsterdam, The Netherlands Available online 10 July 2006
Abstract In order to study Generalized Parton Distributions (GPD) at future fixed-target Deep Inelastic Scattering (DIS) experiments, a detector is needed to observe particles at angles larger than can be covered by the usual forward-angle spectrometer used in this type of experiments. This additional (recoil) detector will determine the exclusivity of the measurements, which cannot be guaranteed by the main spectrometer due to its insufficient energy resolution. Within the limited space available in the target area of such an experiment, the recoil detector must accomplish particle identification as a stand-alone detector, up to particle momenta of 1.3 GeV c1. Using Monte Carlo simulations it has been investigated to what extend an energy loss measurement with a small barrel shaped detector consisting of a few layers of silicon sensors can be used to realize this goal. It turns out that a rough estimate of the momentum is necessary in this case, which can be obtained by measuring the curvature of the tracks in the strong magnetic field employed by the polarized targets used in this type of experiments. Because these studies rely heavily on calculated energy loss distributions as produced by the Monte Carlo code, a beam test with a mixed proton-pion beam has been carried out at CERN to verify the results of the simulation. The beam test results presented here confirm the predicted particle identification capabilities of a detector based on multiple sampling of ionization tracks in silicon up to 1.3 GeV c1. r 2006 Elsevier B.V. All rights reserved. PACS: 34.50.Bw; 29.40.Wk; 29.40.Gx; 02.50.Ng Keywords: Silicon strip-detectors; Particle identification; Energy loss straggling
1. Introduction Over the years many experiments have been conducted to measure the partonic structure of the nucleon. In this way, the momentum and spin distribution of the nucleon have been mapped out in terms of the unpolarized and polarized structure functions. Recently, Generalized Parton Distributions (GPD) were introduced which, in addition to the information contained in the usual structure functions mentioned above, also describe the dynamic correlations between partons in the nucleon. These GPDs have attracted considerable attention as they can also be used to extract the total angular momentum of the quarks and gluons in the nucleon. The simplest way to access GPDs is through the Deep Virtual Compton Tel.: +31 20 592 2154; fax: +31 20 592 5155.
E-mail address: [email protected]. 0168-9002/$ - see front matter r 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.nima.2006.06.028
Scattering process (DVCS). Some initial results on DVCS have become available recently [1], but new high luminosity lepton scattering facilities are needed in order to determine GPDs with the required precision. A common feature of the newly proposed experiments aimed at studying GPDs, like TESLA-N, [2] is the use of a forward angle spectrometer. The energy resolution of such a high momentum multi-stage spectrometer is, however, not enough to ascertain exclusivity of the measurements. Adding a recoil detector in the target region will ensure the exclusivity of the measurements by observing the recoiling proton. This recoil detector must fit in the limited space available around the polarized target that has to be used in this kind of experiment and hence has to fit in between the poles of the target magnet. The high (5 T) field of the target magnet can be used for a coarse determination of the particle momentum by measuring the radius of curvature of the
ARTICLE IN PRESS 360
M.G. van Beuzekom / Nuclear Instruments and Methods in Physics Research A 568 (2006) 359–363
track in the field. Most probably, the detector has to be operated in the beam vacuum in order to able to also observe recoils of very low momentum. As there will be no room to equip such a recoil detector with a large, separate particle identification detector (such as a RICH) for the large angle particles, the system has to provide hadronic particle identification as a stand-alone detector. In a previous publication [3], the possibility of using a small detector consisting of a few layers of silicon has been explored. Particle identification is accomplished by means of a dE/dx measurement of the tracks passing through the silicon. This concept was shown to be feasible—at least for proton identification—with a two layer silicon prototype detector installed at the HERMES experiment at DESY [4]. However, when extending these results to other particles and higher momenta, one has to rely heavily on the energy loss distributions generated by the Monte Carlo code. In order to verify the Monte Carlo results, it was decided to cross check the results by a beam test. This article briefly reviews the results from the Monte Carlo simulations followed by a description of the beam test and the results obtained with these tests. The MC and beam results are compared and the detector performance is quantified in terms of efficiency and purity. 2. Monte Carlo Particle identification with silicon detectors is based on measuring the amount of ionization due to the energy deposition of the passing particles in the sensors. The energy deposition depends on the particle type and momentum. Moreover, for a fixed momentum, the energy deposition fluctuates. This energy loss straggling is described by either a Vavilov or Landau type distribution depending on the energy regime of interest. These distributions are strongly skewed and have a long tail on the high-energy side as a result of collisions with atomic electrons in which a large amount of energy is transferred in a single collision. By using several detector layers, the energy loss distribution is sampled multiple times, which gives the possibility to use statistical tests to determine the particle type. The simplest statistical method is the so-called truncated mean method where the largest sample of a set is disregarded in the calculation of the mean energy loss. In this method, the contribution of the high-energy tail is cancelled which is illustrated in Fig. 1. The top part of the figure shows the ionization distributions1 for pions, kaons and protons with a momentum of 0.8 GeV c1 in a 150 mm silicon sensor. The truncated mean distribution, where the mean is determined by three out of four samples, is shown in the bottom plot. Although the truncated mean method nicely shows the advantage of a statistical test for particle identification by 1 The ionization is directly related to the energy loss; one electron–hole pair is created for 3.6 eV of energy deposited.
Fig. 1. Example of the truncated mean method. The top plot shows the ionization distributions of 0.8 GeV c1 particles in a single 150 mm silicon sensor. The bottom plot shows the truncated mean of three out of four samples of 150 mm silicon.
means of dE/dx measurements, it is not the best method available. Tests like the maximum likelihood method and the Kolmogorov–Smirnov (KS) test [5] give a somewhat better result for the Monte Carlo data. Another advantage of the KS test is that it also gives the absolute probability that a set of dE/dx samples belongs to a certain energy deposition distribution. This is especially an advantage in case of real beam data where particles other than the expected pions and protons are present. Therefore, the KS test is applied to the beam and Monte Carlo data. The Monte Carlo simulations were used to find the optimal detector configuration for a given, fixed, amount of material in the detector. The total material budget was equivalent to four silicon sensors of 300 mm (1.28% X0). The amount of noise is kept constant, or in other words, the signal-to-noise ratio of a sensor is scaled proportional to its thickness. More details on the MC simulations can be found in Ref. [3]. To quantify the performance of different detector configurations, the PID capabilities are expressed in terms of efficiency and purity. The simplest observable is N rt , the number of particles of true type t that are identified as reconstructed type r. The efficiency (e) is defined here as the fraction of correctly identified particles in a dataset P which consists of true particle type t only, i.e. t ¼ N tt = i N it . For MC data the true particle type is known while for the data from the test beam the particle type is determined by a system other than the system under test. The purity (p) is the fraction of correctly identified particles in the dataset consisting of all particles that were identified as being of this type by the system under test P (both in the MC and in the experiment), i.e. pt ¼ N tt = N ti . Hence, the purity i species in the tests and depends on the number of particle on their relative abundance, while the efficiency does not.
ARTICLE IN PRESS M.G. van Beuzekom / Nuclear Instruments and Methods in Physics Research A 568 (2006) 359–363
0
number of 300 µm layers 2 4 6
361
8
layer dependence noise dependence
Pmax [GeV c-1]
1.6
1.4 Fig. 3. Particle spectrum of the 1.3 GeV c1 beam. From left to right the peaks correspond to pions, protons, 3He, deuterons and tritons.
500
1000
1500 noise [e-]
2000
2500
Fig. 2. Maximum momentum at which pions and protons can be separated with an efficiency of 0.8 as a function of the number of silicon layers (open squares) and as a function of noise (solid points).
The maximum momentum at which particles can be identified with a given (reasonably high) efficiency and purity (80% in this study) depends on parameters like the number of silicon layers and the noise in the readout electronics. Fig. 2 shows the maximum momentum at which particles can be identified as a function of the number of sensors (samples). Increasing the number of layers is beneficial for the identification limit, but tends to saturate for a large number of layers. Also shown in the figure is the fairly weak dependence on the noise level of the readout electronics. 3. Beam test In order to verify the Monte Carlo results, an experiment has been carried out with a set of eight silicon sensors. The beam test took place at one of the secondary beam lines of the CERN PS complex. The beam was created by bombarding an aluminum target with the 20 GeV c1 primary electron beam. The secondary beam consisted of a mixture of pions, protons and positrons, with relative fractions of 50%, 30% and 20%, respectively. By changing the current of one of the bending magnets, the beam momentum can be varied from 0.5 to 3.5 GeV c1. In this experiment, the maximum momentum used was 1.3 GeV c1. Independent particle identification was obtained by measuring the time-of-flight (TOF) of the beam particles over a distance of 4.2 m. The system consisted of four scintillators around the silicon detectors. The TOF system
could separate protons from pions and positrons with an efficiency of more than 99.5%. A trigger was created from a four-fold coincidence of the TOF scintillators. Positrons were identified with an air filled Cherenkov detector operated at atmospheric pressure. This Cherenkov detector was placed upstream of the TOF. The setup that was used for the dE/dx based particle identification system consisted of eight silicon sensors with double-sided readout. Four of these sensors are part of the Lambda Wheels [6,7] detector of the HERMES experiment [8]. These 300 mm thick sensors have a strip pitch of 160 mm and are readout by HELIX chips running at a frequency of 10 MHz. The other four sensors, also 300 mm thick, are part of a precision beam telescope developed for testing purposes at NIKHEF [9]. These sensors have readout a pitch of 120 mm, and were connected to APC chips. Both types of silicon detectors have a signal-to-noise ratio of about 8.2 Upon receipt of a trigger, the 8000 strip signals of the silicon sensors were digitized and written to disk. Data has been acquired for five different momenta: 0.8, 1.0, 1.1, 1.2 and 1.3 GeV c1. Fig. 3 shows the particle spectrum as determined by the TOF system for a beam momentum of 1.3 GeV c1. The top part of the plot shows the energy loss to TOF correlation, and the bottom part is the projection on the time axis, yielding the statistics. From left to right, the peaks correspond to pions, protons, 3He, deuterons and tritons. It is also clearly visible that 3He loses, on average, more than four times the average energy of protons. This is due to the double charge of the 3He and the quadratic dependence of the energy loss on charge. The 3He particles have twice the momentum of the other particles because the magnetic momentum selection scales linearly with charge. The TOF information 2
The S/N is defined here as the ratio of the signal in the strip cluster divided by the noise of that cluster of strips. This is different from the more common definition Scluster/Nstrip.
ARTICLE IN PRESS M.G. van Beuzekom / Nuclear Instruments and Methods in Physics Research A 568 (2006) 359–363
362
is used to assign a particle type to all tracks recorded during the beam test. This information is the reference for determining the efficiency and purity of the dE/dx based particle identification.
4. Results The data of the eight double-sided silicon sensors have been analyzed in two groups of four sensors and also as one group of eight sensors. Calibration factors have been determined for all readout chips and sensors. After common mode noise subtraction, the signal in the strips neighboring the strip with the largest signal was added to get the total charge deposition in the sensor. 3000 π Beam Monte Carlo P
Counts
2000
1000
0
40
80
120 ∆E [a.u.]
160
200
240
Fig. 4. Energy deposition distribution from beam data and Monte Carlo simulations. The pion peaks (left) match very well, the width of the measured proton peak, however, is significantly wider than the simulated peak.
Signals from the front and backside of the sensor are combined which increases the signal-to-noise ratio. This addition, however, only has a small effect on the outcome of the analysis. The energy deposition distributions have been created by collecting all events of a single particle type as determined by the TOF system. Fig. 4 shows the energy deposition distributions from the beam test together with that of the Monte Carlo simulations. A common calibration factor is used for both the pion and proton distributions. The signal-to-noise ratio of the MC data was adapted to that of the measurements. The agreement between the two pion distributions is good, but the mean value of the measured proton distribution is 2–4% lower than the mean value from the simulations. The discrepancy between MC results and measurements exists for all sensors and all energies. The most prominent difference between the two proton distributions is the width. It has been verified that the difference is not caused by any instrumental effect or by a systematic error in the data analysis. Due to this shifted proton peak, the distance between the pion peak and the proton peak is smaller for beam data than for the MC data, which translates into a reduced PID efficiency and purity. This effect, however, is only significant for the high momentum beam data. The PID efficiency and purity for different particles and beam momenta for a setup with four silicon layers are listed in Table 1. For comparison the Monte Carlo results are also listed. The MC data has the same signal-to-noise ratio and in both cases all data are analyzed using the KS test. The PID efficiency is close to 1 for momenta below 1 GeV c1 and drops gradually to the 0.8 level for momenta in excess of 1.3 GeV c1. The PID purity is in the same range, but it must be noted that these numbers depend on the relative particle fluxes.
Table 1 Particle identification efficiency (purity) of a four-layer detector for the various momenta used in this experiment Beam momentum (GeV)
0.80
Pion Pion Proton Proton
0.98 0.97 1.00 0.99
Monte Carlo Beam Monte Carlo Beam
1.00 (1.00) (0.99) (0.98) (0.97)
0.95 0.93 0.99 0.97
1.10 (0.99) (0.97) (0.95) (0.94)
0.93 0.90 0.97 0.93
1.20 (0.96) (0.93) (0.93) (0.90)
0.89 0.86 0.93 0.90
1.30 (0.93) (0.89) (0.90) (0.86)
0.85 0.82 0.88 0.84
(0.88) (0.84) (0.86) (0.82)
Table 2 Particle identification efficiency (purity) of an eight-layer detector for the various momenta used in this experiment Beam momentum (GeV)
0.80
Pion Pion Proton Proton
1.00 1.00 1.00 1.00
Monte Carlo Beam Monte Carlo Beam
1.00 (1.00) (1.00) (1.00) (1.00)
0.99 0.98 1.00 1.00
1.10 (1.00) (1.00) (0.99) (0.98)
0.97 0.96 1.00 0.98
1.20 (1.00) (0.98) (0.97) (0.96)
0.95 0.93 0.98 0.96
1.30 (0.98) (0.96) (0.96) (0.93)
0.92 0.89 0.96 0.92
(0.95) (0.92) (0.92) (0.89)
ARTICLE IN PRESS M.G. van Beuzekom / Nuclear Instruments and Methods in Physics Research A 568 (2006) 359–363
The agreement between the results from beam data and MC data is good, albeit that the efficiency and purity are consistently 2–4% lower for the experimental results. Nevertheless, it is clear that the MC simulation can be used to give a reliable prediction of the PID capabilities of a multilayered silicon detector. Table 2 shows a similar comparison for a system consisting of eight sensor layers. The efficiency and purity of an eight-layer detector are about 7–8% higher. The difference between Monte Carlo and measurement again amounts to 2–4%.
363
Acknowledgments I would like to thank M. Hauschild and the SPS/PS group for their support in using the T11 experimental area. I would also like to thank N. van Bakel, M. Demey, E. Jans and J. Steijger for their help in setting up and running the experiment described here. This work was supported in part by the Stichting voor Fundamenteel Onderzoek der Materie (FOM), which is financially supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO).
5. Conclusions The results presented here show that, using dE/dx information, a set of silicon detectors can provide the PID capabilities needed for future Deep Inelastic Scattering (DIS) facilities aimed at studying GPD. It has been shown that the agreement between the energy loss distribution from the Monte Carlo simulation and from experiment is good enough to estimate the performance of a full-scale recoil detector based on the MC results. Separating pions from protons, with an efficiency of more than 0.8 for each particle species, is achieved for momenta up to 1.3 GeV c1 using four layers of silicon. For momenta below 1 GeV c1 the efficiency is close to 1.
References [1] [2] [3] [4] [5]
[6] [7] [8] [9]
A. Airapetian, et al., Phys. Rev. Lett. 87 (2001) 182001. M. Anselmino, et al., 2000, hep-ph/0011299. M.G. van Beuzekom, et al., Nucl. Instr. and Meth. A 513 (2003) 79. J. Visser, et al., Nucl. Instr. and Meth. A 521 (2004) 430. W.A. Press, et al., Numerical Recipes in C, The Art of Scientific Computing, second ed., Cambridge University Press, Cambridge, 1992. D. Heesbeen, Ph.D. Thesis, RU Groningen, 2003. M.G. van Beuzekom, et al., Nucl. Instr. and Meth. A 461 (2001) 247. K. Ackerstaff, et al., Nucl. Instr. and Meth. A 417 (1998) 230. R. Scholte, Master Thesis, University of Twente, 1998.
Nuclear Instruments and Methods in Physics Research A 568 (2006) 359–363 www.elsevier.com/locate/nima
Hadronic particle identification with silicon detectors by means of dE/dx sampling M.G. van Beuzekom NIKHEF, P.O. Box 41882, 1009 DB Amsterdam, The Netherlands Available online 10 July 2006
Abstract In order to study Generalized Parton Distributions (GPD) at future fixed-target Deep Inelastic Scattering (DIS) experiments, a detector is needed to observe particles at angles larger than can be covered by the usual forward-angle spectrometer used in this type of experiments. This additional (recoil) detector will determine the exclusivity of the measurements, which cannot be guaranteed by the main spectrometer due to its insufficient energy resolution. Within the limited space available in the target area of such an experiment, the recoil detector must accomplish particle identification as a stand-alone detector, up to particle momenta of 1.3 GeV c1. Using Monte Carlo simulations it has been investigated to what extend an energy loss measurement with a small barrel shaped detector consisting of a few layers of silicon sensors can be used to realize this goal. It turns out that a rough estimate of the momentum is necessary in this case, which can be obtained by measuring the curvature of the tracks in the strong magnetic field employed by the polarized targets used in this type of experiments. Because these studies rely heavily on calculated energy loss distributions as produced by the Monte Carlo code, a beam test with a mixed proton-pion beam has been carried out at CERN to verify the results of the simulation. The beam test results presented here confirm the predicted particle identification capabilities of a detector based on multiple sampling of ionization tracks in silicon up to 1.3 GeV c1. r 2006 Elsevier B.V. All rights reserved. PACS: 34.50.Bw; 29.40.Wk; 29.40.Gx; 02.50.Ng Keywords: Silicon strip-detectors; Particle identification; Energy loss straggling
1. Introduction Over the years many experiments have been conducted to measure the partonic structure of the nucleon. In this way, the momentum and spin distribution of the nucleon have been mapped out in terms of the unpolarized and polarized structure functions. Recently, Generalized Parton Distributions (GPD) were introduced which, in addition to the information contained in the usual structure functions mentioned above, also describe the dynamic correlations between partons in the nucleon. These GPDs have attracted considerable attention as they can also be used to extract the total angular momentum of the quarks and gluons in the nucleon. The simplest way to access GPDs is through the Deep Virtual Compton Tel.: +31 20 592 2154; fax: +31 20 592 5155.
E-mail address: [email protected]. 0168-9002/$ - see front matter r 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.nima.2006.06.028
Scattering process (DVCS). Some initial results on DVCS have become available recently [1], but new high luminosity lepton scattering facilities are needed in order to determine GPDs with the required precision. A common feature of the newly proposed experiments aimed at studying GPDs, like TESLA-N, [2] is the use of a forward angle spectrometer. The energy resolution of such a high momentum multi-stage spectrometer is, however, not enough to ascertain exclusivity of the measurements. Adding a recoil detector in the target region will ensure the exclusivity of the measurements by observing the recoiling proton. This recoil detector must fit in the limited space available around the polarized target that has to be used in this kind of experiment and hence has to fit in between the poles of the target magnet. The high (5 T) field of the target magnet can be used for a coarse determination of the particle momentum by measuring the radius of curvature of the
ARTICLE IN PRESS 360
M.G. van Beuzekom / Nuclear Instruments and Methods in Physics Research A 568 (2006) 359–363
track in the field. Most probably, the detector has to be operated in the beam vacuum in order to able to also observe recoils of very low momentum. As there will be no room to equip such a recoil detector with a large, separate particle identification detector (such as a RICH) for the large angle particles, the system has to provide hadronic particle identification as a stand-alone detector. In a previous publication [3], the possibility of using a small detector consisting of a few layers of silicon has been explored. Particle identification is accomplished by means of a dE/dx measurement of the tracks passing through the silicon. This concept was shown to be feasible—at least for proton identification—with a two layer silicon prototype detector installed at the HERMES experiment at DESY [4]. However, when extending these results to other particles and higher momenta, one has to rely heavily on the energy loss distributions generated by the Monte Carlo code. In order to verify the Monte Carlo results, it was decided to cross check the results by a beam test. This article briefly reviews the results from the Monte Carlo simulations followed by a description of the beam test and the results obtained with these tests. The MC and beam results are compared and the detector performance is quantified in terms of efficiency and purity. 2. Monte Carlo Particle identification with silicon detectors is based on measuring the amount of ionization due to the energy deposition of the passing particles in the sensors. The energy deposition depends on the particle type and momentum. Moreover, for a fixed momentum, the energy deposition fluctuates. This energy loss straggling is described by either a Vavilov or Landau type distribution depending on the energy regime of interest. These distributions are strongly skewed and have a long tail on the high-energy side as a result of collisions with atomic electrons in which a large amount of energy is transferred in a single collision. By using several detector layers, the energy loss distribution is sampled multiple times, which gives the possibility to use statistical tests to determine the particle type. The simplest statistical method is the so-called truncated mean method where the largest sample of a set is disregarded in the calculation of the mean energy loss. In this method, the contribution of the high-energy tail is cancelled which is illustrated in Fig. 1. The top part of the figure shows the ionization distributions1 for pions, kaons and protons with a momentum of 0.8 GeV c1 in a 150 mm silicon sensor. The truncated mean distribution, where the mean is determined by three out of four samples, is shown in the bottom plot. Although the truncated mean method nicely shows the advantage of a statistical test for particle identification by 1 The ionization is directly related to the energy loss; one electron–hole pair is created for 3.6 eV of energy deposited.
Fig. 1. Example of the truncated mean method. The top plot shows the ionization distributions of 0.8 GeV c1 particles in a single 150 mm silicon sensor. The bottom plot shows the truncated mean of three out of four samples of 150 mm silicon.
means of dE/dx measurements, it is not the best method available. Tests like the maximum likelihood method and the Kolmogorov–Smirnov (KS) test [5] give a somewhat better result for the Monte Carlo data. Another advantage of the KS test is that it also gives the absolute probability that a set of dE/dx samples belongs to a certain energy deposition distribution. This is especially an advantage in case of real beam data where particles other than the expected pions and protons are present. Therefore, the KS test is applied to the beam and Monte Carlo data. The Monte Carlo simulations were used to find the optimal detector configuration for a given, fixed, amount of material in the detector. The total material budget was equivalent to four silicon sensors of 300 mm (1.28% X0). The amount of noise is kept constant, or in other words, the signal-to-noise ratio of a sensor is scaled proportional to its thickness. More details on the MC simulations can be found in Ref. [3]. To quantify the performance of different detector configurations, the PID capabilities are expressed in terms of efficiency and purity. The simplest observable is N rt , the number of particles of true type t that are identified as reconstructed type r. The efficiency (e) is defined here as the fraction of correctly identified particles in a dataset P which consists of true particle type t only, i.e. t ¼ N tt = i N it . For MC data the true particle type is known while for the data from the test beam the particle type is determined by a system other than the system under test. The purity (p) is the fraction of correctly identified particles in the dataset consisting of all particles that were identified as being of this type by the system under test P (both in the MC and in the experiment), i.e. pt ¼ N tt = N ti . Hence, the purity i species in the tests and depends on the number of particle on their relative abundance, while the efficiency does not.
ARTICLE IN PRESS M.G. van Beuzekom / Nuclear Instruments and Methods in Physics Research A 568 (2006) 359–363
0
number of 300 µm layers 2 4 6
361
8
layer dependence noise dependence
Pmax [GeV c-1]
1.6
1.4 Fig. 3. Particle spectrum of the 1.3 GeV c1 beam. From left to right the peaks correspond to pions, protons, 3He, deuterons and tritons.
500
1000
1500 noise [e-]
2000
2500
Fig. 2. Maximum momentum at which pions and protons can be separated with an efficiency of 0.8 as a function of the number of silicon layers (open squares) and as a function of noise (solid points).
The maximum momentum at which particles can be identified with a given (reasonably high) efficiency and purity (80% in this study) depends on parameters like the number of silicon layers and the noise in the readout electronics. Fig. 2 shows the maximum momentum at which particles can be identified as a function of the number of sensors (samples). Increasing the number of layers is beneficial for the identification limit, but tends to saturate for a large number of layers. Also shown in the figure is the fairly weak dependence on the noise level of the readout electronics. 3. Beam test In order to verify the Monte Carlo results, an experiment has been carried out with a set of eight silicon sensors. The beam test took place at one of the secondary beam lines of the CERN PS complex. The beam was created by bombarding an aluminum target with the 20 GeV c1 primary electron beam. The secondary beam consisted of a mixture of pions, protons and positrons, with relative fractions of 50%, 30% and 20%, respectively. By changing the current of one of the bending magnets, the beam momentum can be varied from 0.5 to 3.5 GeV c1. In this experiment, the maximum momentum used was 1.3 GeV c1. Independent particle identification was obtained by measuring the time-of-flight (TOF) of the beam particles over a distance of 4.2 m. The system consisted of four scintillators around the silicon detectors. The TOF system
could separate protons from pions and positrons with an efficiency of more than 99.5%. A trigger was created from a four-fold coincidence of the TOF scintillators. Positrons were identified with an air filled Cherenkov detector operated at atmospheric pressure. This Cherenkov detector was placed upstream of the TOF. The setup that was used for the dE/dx based particle identification system consisted of eight silicon sensors with double-sided readout. Four of these sensors are part of the Lambda Wheels [6,7] detector of the HERMES experiment [8]. These 300 mm thick sensors have a strip pitch of 160 mm and are readout by HELIX chips running at a frequency of 10 MHz. The other four sensors, also 300 mm thick, are part of a precision beam telescope developed for testing purposes at NIKHEF [9]. These sensors have readout a pitch of 120 mm, and were connected to APC chips. Both types of silicon detectors have a signal-to-noise ratio of about 8.2 Upon receipt of a trigger, the 8000 strip signals of the silicon sensors were digitized and written to disk. Data has been acquired for five different momenta: 0.8, 1.0, 1.1, 1.2 and 1.3 GeV c1. Fig. 3 shows the particle spectrum as determined by the TOF system for a beam momentum of 1.3 GeV c1. The top part of the plot shows the energy loss to TOF correlation, and the bottom part is the projection on the time axis, yielding the statistics. From left to right, the peaks correspond to pions, protons, 3He, deuterons and tritons. It is also clearly visible that 3He loses, on average, more than four times the average energy of protons. This is due to the double charge of the 3He and the quadratic dependence of the energy loss on charge. The 3He particles have twice the momentum of the other particles because the magnetic momentum selection scales linearly with charge. The TOF information 2
The S/N is defined here as the ratio of the signal in the strip cluster divided by the noise of that cluster of strips. This is different from the more common definition Scluster/Nstrip.
ARTICLE IN PRESS M.G. van Beuzekom / Nuclear Instruments and Methods in Physics Research A 568 (2006) 359–363
362
is used to assign a particle type to all tracks recorded during the beam test. This information is the reference for determining the efficiency and purity of the dE/dx based particle identification.
4. Results The data of the eight double-sided silicon sensors have been analyzed in two groups of four sensors and also as one group of eight sensors. Calibration factors have been determined for all readout chips and sensors. After common mode noise subtraction, the signal in the strips neighboring the strip with the largest signal was added to get the total charge deposition in the sensor. 3000 π Beam Monte Carlo P
Counts
2000
1000
0
40
80
120 ∆E [a.u.]
160
200
240
Fig. 4. Energy deposition distribution from beam data and Monte Carlo simulations. The pion peaks (left) match very well, the width of the measured proton peak, however, is significantly wider than the simulated peak.
Signals from the front and backside of the sensor are combined which increases the signal-to-noise ratio. This addition, however, only has a small effect on the outcome of the analysis. The energy deposition distributions have been created by collecting all events of a single particle type as determined by the TOF system. Fig. 4 shows the energy deposition distributions from the beam test together with that of the Monte Carlo simulations. A common calibration factor is used for both the pion and proton distributions. The signal-to-noise ratio of the MC data was adapted to that of the measurements. The agreement between the two pion distributions is good, but the mean value of the measured proton distribution is 2–4% lower than the mean value from the simulations. The discrepancy between MC results and measurements exists for all sensors and all energies. The most prominent difference between the two proton distributions is the width. It has been verified that the difference is not caused by any instrumental effect or by a systematic error in the data analysis. Due to this shifted proton peak, the distance between the pion peak and the proton peak is smaller for beam data than for the MC data, which translates into a reduced PID efficiency and purity. This effect, however, is only significant for the high momentum beam data. The PID efficiency and purity for different particles and beam momenta for a setup with four silicon layers are listed in Table 1. For comparison the Monte Carlo results are also listed. The MC data has the same signal-to-noise ratio and in both cases all data are analyzed using the KS test. The PID efficiency is close to 1 for momenta below 1 GeV c1 and drops gradually to the 0.8 level for momenta in excess of 1.3 GeV c1. The PID purity is in the same range, but it must be noted that these numbers depend on the relative particle fluxes.
Table 1 Particle identification efficiency (purity) of a four-layer detector for the various momenta used in this experiment Beam momentum (GeV)
0.80
Pion Pion Proton Proton
0.98 0.97 1.00 0.99
Monte Carlo Beam Monte Carlo Beam
1.00 (1.00) (0.99) (0.98) (0.97)
0.95 0.93 0.99 0.97
1.10 (0.99) (0.97) (0.95) (0.94)
0.93 0.90 0.97 0.93
1.20 (0.96) (0.93) (0.93) (0.90)
0.89 0.86 0.93 0.90
1.30 (0.93) (0.89) (0.90) (0.86)
0.85 0.82 0.88 0.84
(0.88) (0.84) (0.86) (0.82)
Table 2 Particle identification efficiency (purity) of an eight-layer detector for the various momenta used in this experiment Beam momentum (GeV)
0.80
Pion Pion Proton Proton
1.00 1.00 1.00 1.00
Monte Carlo Beam Monte Carlo Beam
1.00 (1.00) (1.00) (1.00) (1.00)
0.99 0.98 1.00 1.00
1.10 (1.00) (1.00) (0.99) (0.98)
0.97 0.96 1.00 0.98
1.20 (1.00) (0.98) (0.97) (0.96)
0.95 0.93 0.98 0.96
1.30 (0.98) (0.96) (0.96) (0.93)
0.92 0.89 0.96 0.92
(0.95) (0.92) (0.92) (0.89)
ARTICLE IN PRESS M.G. van Beuzekom / Nuclear Instruments and Methods in Physics Research A 568 (2006) 359–363
The agreement between the results from beam data and MC data is good, albeit that the efficiency and purity are consistently 2–4% lower for the experimental results. Nevertheless, it is clear that the MC simulation can be used to give a reliable prediction of the PID capabilities of a multilayered silicon detector. Table 2 shows a similar comparison for a system consisting of eight sensor layers. The efficiency and purity of an eight-layer detector are about 7–8% higher. The difference between Monte Carlo and measurement again amounts to 2–4%.
363
Acknowledgments I would like to thank M. Hauschild and the SPS/PS group for their support in using the T11 experimental area. I would also like to thank N. van Bakel, M. Demey, E. Jans and J. Steijger for their help in setting up and running the experiment described here. This work was supported in part by the Stichting voor Fundamenteel Onderzoek der Materie (FOM), which is financially supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO).
5. Conclusions The results presented here show that, using dE/dx information, a set of silicon detectors can provide the PID capabilities needed for future Deep Inelastic Scattering (DIS) facilities aimed at studying GPD. It has been shown that the agreement between the energy loss distribution from the Monte Carlo simulation and from experiment is good enough to estimate the performance of a full-scale recoil detector based on the MC results. Separating pions from protons, with an efficiency of more than 0.8 for each particle species, is achieved for momenta up to 1.3 GeV c1 using four layers of silicon. For momenta below 1 GeV c1 the efficiency is close to 1.
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