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Segment Finding Algorithm

The offline segment finding algorithm uses a three-cell mask as this contains all possible patterns of track segments with eight hits.gif The mask is stepped around a complete superlayer two cells at a time forming overlapping regions. This method is not suitable for the trigger as we do not need to find all segments. Furthermore, to get all hits from three cells in a complete superlayer together would require too large a communication overhead given the sector organisation of the readout.

Since 90% of all segments in NC and CC events are contained in one or two cells in a superlayergif [2] a one- or two-cell mask should be sufficient for online segment finding. At present our SLT segment finding algorithm is based on a single-cell mask. This choice of mask is simple and has the advantage of requiring little data to be transferred and no co-ordinate transformations. Since we are concerned with the r- tex2html_wrap_inline810 projection, only the axial superlayers are searched for track segments.

The segment finding algorithm uses a track following method that looks for straight lines in local non-orthogonal (LNO) cell co-ordinates. In this co-ordinate system one axis (u) is defined to lie in the sense wire plane perpendicular to the CTD z-axis and the other axis (v) is parallel to the the drift lines in the planar drift approximation. In the algorithm we choose the co-ordinates along the u-axis as the wire numbers and along the v-axis as the drift distances. This system has the advantage of describing the geometry of a single cell by only the spacing between the sense wires (which is constant in a given superlayer). Little geometry is required and no co-ordinate transformations are needed during segment finding in a one-cell mask.gif

The present road following method works as follows.

At least three hits are required to define a segment and hits are used only once. In practice the minimum number of hits required is one of the parameters of the algorithm and can vary between three and eight. The average number of hits on a MC segment in one cell is five [2]. We have used four hits to define a segment (See section 5.1 for more details on this choice.).

The road width is calculated from the expected nominal drift distance resolution of 130  tex2html_wrap_inline870 m. The one standard deviation error on the predicted drift distance by the algorithm is normally 2.5 times the drift distance resolution. If we have missed a hit and are allowing a gap of one wire, the standard deviation on the prediction increases by 1.5. In each case the road width is taken to be three standard deviations ( tex2html_wrap_inline872  mm).

The algorithm finds both a segment and its mirror (the segment formed from the other sign of the ambiguous drift directions). We have exploited the design of the CTD cell to reduce the number of wrong choices of the segment ambiguity. The sense wire planes of the CTD are not radial but at 45 tex2html_wrap_inline874 to a radius, and hence, to the direction of high tex2html_wrap_inline798 \ tracks. In most cells the correct segment will cross the sense wire plane and point roughly towards the bunch-crossing point, whereas the wrong segment will have a large angle with the radius vector to the segment centre. Only one solution is taken to define the vector hit and that is the one that points most directly at the bunch-crossing point.


next up previous
Next: Track Finding Algorithm Up: Online Pattern Recognition Previous: Online Pattern Recognition

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