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Introduction

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 CrProton, I grab the projected and propagated track covariance projected onto the POCA plane.

Hit Tile Definition
Tkr1IsHit = mipsPmtA() < 0.001 && mipsPmtB() < 0.001
Track Quality Selection
McTkr1DirErr < 0.1
Tile 22

It appears that there are not enough statistics for an individual tile.

Raw Counts

Ratio of Total Counts

Middle tiles

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

Raw Counts

Ratio of Total Counts

Top Tiles

Raw Counts

Ratio of Total Counts

Data

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