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Comment: Migrated to Confluence 4.0

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  1. open digi, recon, merit and relation filesunmigrated-wiki-markup
  2. reads digi to create two arrays of layer end trigger bits \ [tower\]\[layer\]\[face\] , one for CalLo and one for CalHi
  3. open recon and loop on clusters, then for each cluter
    1. open the corresponding crystal collection
    2. check if any crystal end has more than 100 MeV (or whatever other threshold)
    3. if any, then look if the corresponding layer end has a trigger bit set
    4. if not so, fill in a map of missing trigger bits and add 1 to a so called "cal ghost number"
  4. for each cluster, the higher the ghost number, the higher the probability that it's a ghost... but it looks like that above 2, they're all ghosts.

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Bottom panel of plot below shows the correlation between number of ghost-tagged xtals and ghost-tagged energy (zero suppressed)

It looks like that almost all good gamma events have at least one ghost-tagged xtal and even requiring a large ghost-tagged energy we select a large fraction of events

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Evt Id 130047

Evt Id 333286

8 tagged xtals
80% ghost-energy

32 tagged xtals
97% ghost-energy

In conclusion, this algorithm cannot be applied to flight data.

Ghost number and cluster classification

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