next up previous
Next: Track Efficiency Using Up: Segment Finding Efficiencies Previous: Segment Finding Efficiencies

Number and Type of Found Segments

Table 1 show the segment profiles for the different event types with and without applying the fiducial cuts. For the segment profiles the way in which the percentages in each segment class are calculated is different from that explained in section 4.4.1. In this case, the percentages in each segment class were obtained by counting up the number of segments in each class, dividing by the total number of segments in all classes, and multiplying by 100. It can be seen that most of the segments in an event are of the nasty class. This is particularly true in BG events for which only 2% of the segments are within our fiducial cuts. Few segments are split. This can be understood since the definition of a segment can only vary between four and eight hits, while five hits on a single-cell segment is the average. The number of spurious segments are high when no fiducial cuts are applied. These segments could be a problem in over estimating the track multiplicity at the track finding stage.

  table146
Table 1:   Segment profiles.

Table 2 shows the results of comparing the first and last MC ID's on each found segment. A good ID match represents cases in which the MC ID's of the first and last points are identical, while bad means they are not identical, and ambiguous\ means they are the same but of opposite sign.

  table164
Table 2:   ID matching.

Table 10 shows the percentage of found segments within the fiducial cuts. In the following the definition of percentage is the number of segments under consideration times 100 divided by the total number of segments, where the total number of segments is the number of found plus the number of lost segments within the fiducial cuts. The normal algorithm is the proposed SLT segment finding algorithm described in this note, while the minimal algorithm and the truth algorithm were described in section 4.4. A segment finding efficiency greater than 92% is obtained in the fiducial space. The difference between the first and second rows in table 10 show that a large fraction ( tex2html_wrap_inline922 ) of the segments found in each event type are above what could easily be found by the simplest method. This justifies the more complicated approach and extra time spend in the present algorithm. Approximately 10% of the segments were classified as spurious, and the number of split segments was less than 0.1% for each event type and algorithm. This is because of the small number of points (four) required to define a segment. The different percentages of found segments in the different event types for the minimum algorithm reflects the relative density of hits in the different event types. The differences in the percentages of found segments from 100% by the truth algorithm indicates that our approach to choosing the ambiguous direction of the segment fails less than 0.3% of the time in the fiducial space.

  table176
Table:   Efficiency for finding segments: tex2html_wrap_inline790  GeV/c and tex2html_wrap_inline912 .

Table 11 show the percentage of nasty segments in the different event types. These numbers clearly reflect the effect of the fiducial cuts on the different event types. The contamination of segments within the fiducial cuts by the large number of nasty segments ( tex2html_wrap_inline928 % in NC and CC events) could cause lost segments and possible lead to a low track finding efficiencygif.

  table190
Table:   Nasty segments: tex2html_wrap_inline790  GeV/c and tex2html_wrap_inline912 .

Table 5 shows the efficiency for finding segments when no fiducial cuts are applied. Presumably the lost segments outside the fiducial space are largely due to the heavy hit masks in superlayer one. The above studies indicate that the segment finder must find the segments within fiducial space among a huge background of segments, and hits, outside the fiducial space.

  table200
Table 5:   Efficiency for finding segments: no fiducial cuts.

A study of the segment finding efficiency in the different superlayers was performed.gif For BG events the percentage of found segments was approximately constant in each superlayer, while for NC and CC events there was a slight decrease in efficiency in superlayer one ( tex2html_wrap_inline936 ). Figure 5 shows the percentage of nasty segments in each superlayer for the different event types. The flat distribution for BG events is due to low tex2html_wrap_inline798 tracks distributed approximately uniformly over the chamber. The large number of nasty segments in superlayer one for NC and CC events is due to low tex2html_wrap_inline880 events giving low angle tracks from the current jet.

  figure31
Figure 5:   Percentage of segments which are in the nasty class in each superlayer.

The segment finding efficiency in different geometrical regions of the chamber and for different track tex2html_wrap_inline798 \ was studied. Different tex2html_wrap_inline900 points were chosen at the fourth wire at the forward end of the CTD in each axial superlayer. For NC events the segment finding efficiency is relatively uniform over tex2html_wrap_inline900 for different values of minimum tex2html_wrap_inline798 cut; table 6 shows the mean percentage of segments found for different minimum tex2html_wrap_inline798 cuts. For BG events the distribution of the percentage of found segments is uniform in tex2html_wrap_inline900 and raises slightly for the case when tex2html_wrap_inline798 is greater than 1.0 GeV/c. The percentage of nasty segments raises with tex2html_wrap_inline798 cut and tex2html_wrap_inline900 cut as expected, but is relatively flat for tex2html_wrap_inline798 greater than 0.5 GeV/c and particularly so for BG events.

  table215
Table 6:   Mean segment finding efficiencies for different tex2html_wrap_inline798 cuts.

The minimum number of hits required to define a segment was varied (see figure 6). The efficiency for segment finding falls rapidly above a four hit definition for both NC and BG events, while the number of nasty segments is constant.

  figure31
Figure 6:   Segment finding efficiency for different segment minimum hit definitions.

A study of the maximum number of hits required to define a mask shows that we must require more than eight for good NC efficiency. The efficiency for finding segments for BG events and the number of nasty segments is independent of this requirement.


next up previous
Next: Track Efficiency Using Up: Segment Finding Efficiencies Previous: Segment Finding Efficiencies

Douglas M. Gingrich
Thu Mar 28 18:08:05 MST 1996