Using a test TPC, consisting of the ALICE TPC field cage prototype in
combination with the final ALICE TPC readout and electronics, the energy loss
distribution and resolution were measured for identified protons. The
measurements were compared to theoretical calculations and good quantitative
agreement was found when detector effects were taken into account. The
implications for particle identification are discussed.Comment: 6 pages, 4 figures, Quark Matter 06 poster proceedin

Christiansen, P.

English

arXiv

Using a test TPC, consisting of the ALICE TPC field cage prototype in combination with the final ALICE TPC readout and electronics, the energy loss distribution and resolution were measured for identified protons. The measurements were compared to theoretical calculations and good quantitative agreement was found when detector effects were taken into account. The implications for particle identification are discussed

Christiansen, Peter

Lund University Publications

Particle identification studies with an ALICE test TPC

Using a test TPC, consisting of the ALICE TPC field cage prototype in combination with the final ALICE TPC readout and electronics, the energy loss distribution and resolution were measured for identified protons. The measurements were compared to theoretical calculations and good quantitative agreement was found when detector effects were taken into account. The implications for particle identification are discussed.Using a test TPC, consisting of the ALICE TPC field cage prototype in combination with the final ALICE TPC readout and electronics, the energy loss distribution and resolution were measured for identified protons. The measurements were compared to theoretical calculations and good quantitative agreement was found when detector effects were taken into account. The implications for particle identification are discussed.Using a test TPC, consisting of the ALICE TPC field cage prototype in combination with the final ALICE TPC readout and electronics, the energy loss distribution and resolution were measured for identified protons. The measurements were compared to theoretical calculations and good quantitative agreement was found when detector effects were taken into account. The implications for particle identification are discussed.Using a test TPC, consisting of the ALICE TPC field cage prototype in combination with the final ALICE TPC readout and electronics, the energy loss distribution and resolution were measured for identified protons. The measurements were compared to theoretical calculations and good quantitative agreement was found when detector effects were taken into account. The implications for particle identification are discussed

CERN Document Server

arXiv:physics/0703097v1   8 Mar 2007March 9, 2007 2:2 WSPC/INSTRUCTION FILE qm06International Journal of Modern Physics Ec© World Scientific Publishing CompanyParticle Identification Studies with an ALICE Test TPCPeter ChristiansenDiv. of Experimental High-Energy Physics, Lund University,Box 118, SE-221 00 Lund, Sweden,peter.christiansen@hep.lu.sefor the ALICE TPC Collaboration.Received (received date)Revised (revised date)Using a test TPC, consisting of the ALICE TPC field cage prototype in combinationwith the final ALICE TPC readout and electronics, the energy loss distribution andresolution were measured for identified protons. The measurements were compared totheoretical calculations and good quantitative agreement was found when detector effectswere taken into account. The implications for particle identification are discussed.1. IntroductionThe ALICE Time Projection Chamber (TPC) is the main tracking detector in thecentral region of the ALICE heavy ion experiment at LHC 1. In addition to excellenttracking capabilities, the TPC has particle identification (PID) capabilities throughthe measurement of the ionization. This is especially interesting in the region of therelativistic rise, βγ > 3 (pT > 3 GeV/c), where other ALICE detectors have little orno PID capabilities for pions, kaons, and protons. STAR has successfully used TPCPID on the relativistic rise to measure important features of heavy ion collisions inthe regime of intermediate and high pT2.For PID with TPCs a major limitation is the calibration. So far much of thePID analysis has used phenomenological descriptions of the data, based on earlierexperience rather than theoretical calculations. In this paper we compare resultsfrom a beam test with a theoretical model of the gas energy loss and comment onthe advantage of such a study.2. Model DescriptionIn the model of Hans Bichsel 3, the calculation of the energy loss is based on theFermi Virtual Photon concept, implemented by Allison and Cobb and elaboratedby Bichsel. The straggling function (Landau distribution), f , for the energy loss,∆, can then be determined for all track segments, x, and βγ (f(∆) = f(∆, βγ, x)).In general one has the bookkeeping problem that straggling functions are notanalytical functions and for different βγ and x they are not related by a simple1March 9, 2007 2:2 WSPC/INSTRUCTION FILE qm062 P. Christiansen for the ALICE TPC Collaborationone parameter scaling. However, it was shown in the paper ( 3, Section 8) that twoscaling parameters, a(βγ) and b(βγ), can be found so that:f(∆, βγ, x) ≈ g(a(βγ) ·∆+ b(βγ, x)).In that way all straggling functions (and associated truncated mean distributions)can be approximated by one straggling function (here denoted g for generic) and atable of two parameter scaling variablesa.The ionization measured on the TPC pads is related to the energy lost bythe charged particle as it traverses the gas, but there are in principle six steps –energy loss, energy deposition, ionization, electron transport, amplification, and ADconversion (see 3 p. 159) – which should be considered in going from energy lossof particles to the ADC output measured in the TPC. So one might ask to whatextend the conclusions for the gas energy loss are also true for the experimentaldata:(i) Does the energy loss spectra calculated in the model describe the measuredcharge spectra?(ii) Can all relevant quantities for PID (such as the truncated mean distribution)be derived from the straggling function?(iii) Is two parameter scaling applicable for experimental distributions?3. Experimental ResultsThe results presented here were obtained with a test setup described in 4, wheredetails of the analysis, and more results on electronics performance and spatialresolution, can also be found.The data were collected with an ALICE Inner Read-Out Chamber (IROC) whichhas 63 pad rows with pad length of 7.5 mm each, resulting in a total track length of472.5 mm (for the ALICE TPC there is also an Outer Read-Out Chamber (OROC)).The test TPC was operated with the newly proposed gas mixture Ne-CO2-N2 (85.7-9.5-4.8) 5.The tracks originated from a secondary beam of single particles from the CERNPS where the momentum, p, of the beam was adjustable between 1 and 7 GeV/cwith a resolution σp/p ∼ 0.01. The PID was obtained by a TOF setup that allowedthe complete separation of pions and protons up to p = 3 GeV/c. For each pad rowa cluster was reconstructed and the cluster charge, Q, was determined as the sumof the signals in the cluster.From the tracks the cluster charge straggling function, f(Q), was measured, andthe “truncated mean” distribution, C, was derived track by track from the averageof the 60% lowest cluster charges.aIgnoring the high energy tail of the straggling function one could possibly to a high degree ofprecision (see Figure 9 and 10 in 3) use only one relevant parameter, the number of primarycollisions Np = dNp/dx(βγ) ·∆x.March 9, 2007 2:2 WSPC/INSTRUCTION FILE qm06Particle Identification Studies with an ALICE Test TPC 3Q [ADC ch]100 200 300 400 500 600 700 800 900Probability density0.00.51.01.52.02.53.0-310×p = 1.0 GeV/c = 272P∆w = 279C [ADC ch]50 100 150 200 250 300Probability density0246810121416182022-310×p = 1.0 GeV/c<C> = 248 = 8.3 %CσQ [ADC ch]100 200 300 400 500 600 700 800 900Probability density0.00.51.01.52.02.53.03.54.04.5-310×p = 3.0 GeV/c = 172P∆w = 191C [ADC ch]50 100 150 200 250 300Probability density051015202530-310×p = 3.0 GeV/c<C> = 149 = 9.3 %CσFig. 1. The straggling data (left) and the truncated mean distribution (right) for protons withmomentum 1 and 3 GeV/c. Left: The dashed line indicates the most probable value, ∆p, of thestraggling functions while the double arrows denote the full width at half maximum FWHM, w.Right: The solid line is a normalized Gaussian fit to the distribution.In the following we study protons with p = 1 GeV/c (294 tracks), and p =3 GeV/c (614 tracks). This is a small subset of the beam test data with clean PID.The straggling data and the truncated mean distributions are shown in Figure 1.The probability of a true Gaussian fit to the two truncated mean distributionscorresponding to p = 1 and 3 GeV/c was found to be only 15 %, and 0.04 %,respectively, indicating that the truncated mean is not strictly Gaussian distributed,with a shoulder at higher C clearly visible, especially in the 1 GeV/c data.The energy loss resolution was found to be in agreement with that determinedin the ALICE TPC Technical Design Report 1, when one takes into account thatthe track length in the full ALICE TPC is 3.3 times longer (IROC+OROC) thanin the prototype (IROC).4. Comparison with Model CalculationsIn this section we use the measurements shown in Figure 1 and model calculationssupplied by Hans Bichsel to study the questions outlined in section 2.Figure 2 shows an overlap of the measured straggling data for 1 GeV/c protonsand the scaled measured straggling data for 3 GeV/c protons (left), and similarlythe overlap of the two truncated mean distributions (right). The scaling param-eters were determined from the data. The close agreement demonstrates the ap-March 9, 2007 2:2 WSPC/INSTRUCTION FILE qm064 P. Christiansen for the ALICE TPC CollaborationQ [ADC ch]100 200 300 400 500 600 700 800 900Probability density0.00.51.01.52.02.53.0-310×1 GeV/c protons3 GeV/c protons (scaled)C [ADC ch]50 100 150 200 250 300Probability density024681012141618-310×1 GeV/c protons3 GeV/c protons (scaled)Fig. 2. The 3 GeV/c proton straggling data (left) and truncated mean distribution (right) scaledto match the 1 GeV/c distribution. The scaling parameters are a = 1.46, and b = 33, see text.Note that the same set of scaling variables is used for both comparisons.plicability of two-parameter scaling between Minimum Ionizing Particles (MIPs)(p = 3 GeV/c protons) and particles with an energy loss similar to that on theplateau (p = 1 GeV/c protons has similar energy loss spectra as 1000 GeV/c pro-tons (except for the large ∆ tail)). Since the scaling works for the smallest lengthscale (the pads in the OROCs are longer: 10 mm and 15 mm) and for the relevantenergy loss range for high-pt PID, this scaling is probably applicable to all relevantdistributions.Q [ADC ch]0 100 200 300 400 500 600 700 800 900Probability density0.00.51.01.52.02.53.03.54.04.5-310×p = 3.0 GeV/cDataModelC [ADC ch]0 50 100 150 200 250 300 350Probability density05101520253035-310×p = 3.0 GeV/cDataModelFig. 3. Comparison of the measured cluster charge straggling data and truncated mean distribu-tions (data) to the energy loss functions calculated in the Bichsel model (solid line).Figure 3 shows a comparison between the experimental cluster charge data andenergy loss calculations for 3 GeV/c protons. The conversion factor (ADC ch/eV)is the only free parameter and has been adjusted to align the peaks; the overallnormalization is fixed by requiring the integral to be unity. The agreement betweenthe two straggling functions is reasonable, but the extracted resolution from thetruncated mean is σC = 8.0 % in the model and 9.3% for the data (Figure 1),March 9, 2007 2:2 WSPC/INSTRUCTION FILE qm06Particle Identification Studies with an ALICE Test TPC 5leaving a discrepancy of 15 %.This difference is also found when comparing the experimental straggling datato the experimental truncated mean distribution. If cluster charges are randomlygenerated with probabilities according to the experimental straggling data (shownin Figure 1) and a virtual track is constructed with the same number of clusters asfor the data, the resolution is close to the 8 %, in agreement with the model, butnot with the experimental results. The resolution of the real tracks is deterioratedbecause cluster charges are correlated; when cluster charges in neighboring pad rowsare compared they exhibit a +33% correlation factor, which reduces the genericinformation for the track.Q [ADC ch]0 100 200 300 400 500 600 700 800 900Probability density0.00.51.01.52.02.53.0-310×p = 1.0 GeV/cDataSimulationC [ADC ch]0 50 100 150 200 250 300 350Probability density02468101214161820-310×p = 1.0 GeV/cDataSimulationQ [ADC ch]0 100 200 300 400 500 600 700 800 900Probability density0.00.51.01.52.02.53.03.54.04.5-310×p = 3.0 GeV/cDataSimulationC [ADC ch]0 50 100 150 200 250 300 350Probability density051015202530-310×p = 3.0 GeV/cDataSimulationFig. 4. Comparison of the measured straggling data and truncated mean distributions (experi-mental data) to the model of Bichsel with detector effects included (simulation).The correlations in the cluster data originate from detector effects during the sixsteps from energy loss to ADC output mentioned in section 2. Figure 4 shows thecomparison between the data and the adjusted Bichsel model modified for diffusion(electron transport) and exponential gain amplification variations (amplification),see 4 for details. From the agreement we conclude that the energy loss mechanismand the two detector effects combined are sufficient to describe the data.This method also fixes the adjustment of the gain to G = 9.6 ADC ch/electronor an effective gas amplification gain of 9 600 (the fraction of the signal picked upby the pads).March 9, 2007 2:2 WSPC/INSTRUCTION FILE qm066 P. Christiansen for the ALICE TPC CollaborationIf the simulated distributions are treated as fits to the truncated mean distri-butions, the probability of the model describing the data is 21 %, and 0.11 %, forthe p = 1 and 3 GeV/c data sets respectively. In both cases, the description ofthe data is better than the previous Gaussian fit functions in Figure 1 where, foreach setting, there are two fit parameters to adjust. For the energy loss model thetwo gas gains differ by less than 3 % which could be due to gas density variationsbetween the two runs.5. ConclusionWe have found that it is possible to describe the test data with calculations fromfirst principles when detector effects are taken into account. This good agreementbetween model and data led Hans Bichsel to propose an optimization of the ALICEMonte Carlo simulation 6.The tracks are not fully characterized by their straggling functions since thecluster charges in a track are correlated. So it is not possible to directly use energyloss calculations to optimize PID. Note also that a fit to the cluster charges of asingle track to extract e.g. the most probable energy loss as a PID estimator willface the same problems since the data are correlated, so that the fitting assumptionof independent data is not fulfilled.It is interesting to note that the two parameter scaling relation is still true forthe experimental data. This might only be true in the case, as here, where one hasalmost identical track geometry, so that effects of diffusion are the same.Even though the disagreement between the resolution derived from the experi-mental straggling data and the one measured from the truncated mean could havebeen found in the data, it was only realized after comparing to the model calcu-lations, so we would like to stress that a quantitative precise model of the energyloss in gases is an important tool for calibrating and understanding a TPC (andany other gas detector).6. AcknowledgmentsThe authors would like to thank Hans Bichsel for many valuable discussions onenergy loss in gases, PID, and for supplying us with model calculations.References1. ALICE Collaboration, “Time Projection Chamber”, ALICE TDR 7, CERN/LHCC2000-001.2. B. I. Abelev et al. [STAR Collaboration], Phys. Rev. Lett. 97, 152301 (2006).3. H. Bichsel, Nucl. Instrum. Meth. A 562, 154 (2006).4. D. Antonczyk et al. [ALICE TPC Collaboration], Nucl. Instrum. Meth. A 565, 551(2006).5. C. Garabatos [ALICE Collaboration], Nucl. Instrum. Meth. A 535, 197 (2004).6. H. Bichsel, Nucl. Instrum. Meth. A 566, 1 (2006).

Particle Identification Studies with an ALICE Test TPC

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