The offline segment finding algorithm uses a three-cell
mask as this contains all possible patterns of track
segments with eight hits.
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 superlayer [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-
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.
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 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 (
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 to a radius, and hence, to the direction of high
\
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.