Investigation of J/Psi Event Acceptance in the FLT

Chapter 9

9.1 Introduction

Events containing a J/ψ can be used at HERA to probe the low-x gluon distribution of the proton[87]. In order to do this, it is necessary to know the efficiency of the FLT for these events. In this chapter, trigger efficiencies are measured for the CTDFLT, the FTDFLT and the standard parametrization of the GFLT which was described in section 8.3.

Further, a comparison was made of measured parameters for the J/ψ sample and a beam-gas sample. This enabled a first approximation to a dedicated sub-trigger to be suggested.

J/ψ event tagging methods previously suggested[88] have utilized the luminosity monitor. Here, the response of the entire detector is simulated in an effort to identify differences between signal and background.

9.2 Event Generation

The ASCII interface for the HERWIG generator described in section 8.1 was used again here in conjunction with program versions 5.2/5.3. 26,000 J/ψ events were generated.

HERWIG allows a choice of five structure functions. These were all investigated and found to produce no discernible differences in the properties of events seen in the detector. For the sake of consistency, option five was used throughout[89].

9.3 Results

Investigation then centered on the task of separating the J/ψ events from the beam-gas background. The beam-gas sample produced to allow background studies was generated using the UA5 generator. Forty thousand events were produced with a homogeneous distribution along the beam-line from z = -19 m to z = +1 m.

9.3.1 Trigger Efficiencies

Table 9.1 shows the proportions of events accepted by the full simulations of the CTDFLT, the FTDFLT and by the parametrization of the GFLT. The results for the RBOX are also shown. [At the time of this work the design of the RBOX was complete. It was felt that using the most modern version of the simulation was important. This was no longer compatible with the RBOX code so only a small event sample was passed through the RBOX code. This is why the statistical errors are larger in this case.]

Table 9.1: Event classifications from ZGANA.

The event classes have the meaning used previously in section 5.2.3 so the CTD class two and three must be summed to provide a total acceptance. This means that the CTDFLT accepts 93.1% with beam-gas leakage of 7.6%. For a leakage rate of 1 kHz m-1 this gives a background of 1528 Hz from the 20 meter source length.

The beam-gas leakage in the FTDFLT corresponds to a rate of 2100 Hz. At the time of the simulation from which results are described here, no FTD class zero was defined in ZGANA: events without diamonds were rejected. In the final system, these events will be described as unclassified. The beam-gas leakage in the parametrizations of the GFLT corresponds to a leakage rate of 1138 Hz.

9.3.2 Comparison of Signal and Background

The statistics on the plots relate to the beam-gas sample. Where relevant, the mean of the J/ψ distribution is given on the plot. The figures that are shown relating to calorimeter data (figure 9.1 to figure 9.3) show sizable differences between signal and background and therefore are useful in a dedicated sub-trigger.

Figure 9.1: Sum of visible transverse energy in the electromagnetic calorimeter.

Figure 9.2: Sum of total transverse momentum (x-direction only).

Figure 9.3: Sum of total transverse visible energy.

In particular, figure 9.3 explains why the parametrization of the GFLTB rejects some events: there are many signal events with low transverse energy deposition. These will fail the CALFLT cuts.

Figure 9.4 shows that approximately 25% of the beam-gas sample has hits in the veto-wall. Very few signal events register in the veto-wall: in a sample of 500 CC events, no hits were observed.

Figure 9.4: Veto-wall hits.

The C5 collimator is located three meters upstream (for the protons) of the interaction region and is designed to reduce the halo of off-beam particles in the beam. It is possible for good events to produce C5 hits by virtue of having tracks in the backward direction but in general, hits in the collimator are strongly indicative of a background event. It would clearly be advisable for the trigger to take advantage of this to veto events with C5 hits. Figure 9.5 shows that only a negligible fraction of signal events have C5 collimator hits whereas figure 9.6 shows that substantial discrimination against background is a prospect.

Figure 9.5: Number of hits in C5 collimator for J/ψ events.

Figure 9.6: Number of hits in C5 collimator for beam-gas events.

9.4 Discussion

Table 9.1 shows that excellent acceptance is obtained by the tracking trigger. In addition, as previously described in section 5.1, the RBOX will combine data from the FTD and the CTD and so these figures may be expected to improve. However, the table also shows that further optimization is advisable in the GFLT: some ways to produce a dedicated sub-trigger were seen to be plausible from considering the figures, many of which show substantial discrimination between signal and background. To investigate the utility of this as a first approximation to a dedicated sub-trigger was devised. It is important to emphasize that no optimization has been done on the trigger parameters: the cut values could be tuned and other sub-detectors included.

Table 9.1: Event classifications from ZGANA.

The sub-trigger was developed from a simple philosophy. Calorimeter triggers were set so that they were ‘free’: i.e. plots of measured values were studied to find cut values that would produce no beam-gas leakage but still provide some benefit in terms of J/ψ acceptance. Then if there were clear grounds to reject the event this course was taken. Finally the tracking detector triggers were applied to those events still unclassified.

The full details of this trigger are shown in figure 9.7 and the results obtained in table 9.2. It can be seen that the efficiency is comparable to that of the CTDFLT but with improved beam-gas leakage figures. The leakage rate implied here is 954 Hz. It should be noted that it is not trivial to improve on the CTDFLT because its performance is already good.

Figure 9.7: Sub-trigger decision flowchart.

Table 9.2: Event classifications for the dedicated sub-trigger.

Previous work[90] on J/ψ events in the FLT achieved an efficiency of 66% with beam-gas rates below the acceptable limit. That particular trigger is a complex entity utilizing many sub-detector components; moreover, it has been optimized. Operating here would permit cross checking of efficiencies and result in complementary data-sets for J/ψ physics.

A characteristic of J/ψ events is the presence of leptons with high transverse momentum in the opposite direction to the quark jet, as described in section It should in general be possible to find these tracks in the CTD or the RTD and a more complex trigger, perhaps at higher levels, could search for these by correlating with the CAL or muon detectors.

9.5 Conclusions

The tracking detector FLT will provide excellent efficiency for J/ψ events since good performance has been obtained with the CTD and RBOX. Reasonable performance may be expected from the GFLT. An optimized sub-trigger along the lines suggested here would provide very good efficiency for J/ψ events.

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By Tim Short

I am a former investment banking and securitisation specialist, having spent nearly a decade on the trading floor of several international investment banks. Throughout my career, I worked closely with syndicate/traders in order to establish the types of paper which would trade well and gained significant and broad experience in financial markets.
Many people have trading experience similar to the above. What marks me out is what I did next. I decided to pursue my interest in philosophy at Doctoral level, specialising in the psychology of how we predict and explain the behaviour of others, and in particular, the errors or biases we are prone to in that process. I have used my experience to write The Psychology of Successful Trading. In this book, I combine the above experience and knowledge to show how biases can lead to inaccurate predictions of the behaviour of other market participants, and how remedying those biases can lead to better predictions and major profits. Learn more on the About Me page.

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