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Eric suggested that looking at the POCA distance (in sigma) from ACD tiles should give different distributions for tiles that are hit than for those that have no hit. Basically, this is saying that you expect tiles without hits to have track POCA that are more than 1 sigma from the tile active boundary. On the other hand hit tiles are expected to have POCA with negative sigma (distances inside the active tile). The cross-over from dominantly positive sigma POCAe to negative sigma should be a sharp feature which is present in the data and MC.

Monte Carlo

Generate 10^5 CrProtonFor a set of CrProtons generated from a modified HEAD1.1365 of GR, I grab the projected and propagated track covariance projected onto the POCA planecovariant distances of POCA from ACD tile elements. After selecting for well reconstructed tracks, I split POCA into two sets: those corresponding to hit tiles, and those corresponding to unhit tiles. I then also plot the fraction of the total number of events at a given covariant distance which correspond to (un)hit tiles.

I examine the resulting distributions for POCA from both the first track and first cluster.

CalTrackAngle < 0.1  #Well Reconstructed Tracks


Tkr1IsHit = mipsPmtA() < 0.001 && mipsPmtB() < 0.001

  #Hit Tile Definition

McTkr1DirErr < 0.1  # Track Quality Selection

Tile 22

It appears that there are not enough statistics for Looking at an individual tile in the center of the ACD, the statistics are too limited to draw any real conclusions. However, these plots hint hit tiles tend to have POCA with negative covariant distances while the unhit POCA tend to have positive covariant distances (as expected).

Middle tiles

TileId = 11,12,13,21,22,23,31,32,33

Top Tiles

Taking all the top ACD tiles substantially increases the statistics. It is now clear that there is a sharp shift from hit to unhit tiles at a covariant distance of 0.

Top Tiles

The plots before were slightly unfair, since they were cutting on McTkr1DirErr to select for well reconstructed tracks. Replacing this cut with a cut on CalTrackAngle we get very similar plots.

CalTrackAngle < 0.1

Data