1. Ghost Identification M. Needham EPFL

2. Outline Embedding data Matching study  2 /dof study

3. Embedding Data or MC Can study ghosts and detector efficiency by embedding data in data [or MC] This procedure can be useful for other things [e.g. measuring spillover effects] For this to be work: Mechanism to merge two file streams [re-use Boole spillover mechanism ?] Code to merge the streams [exists for ST and Velo, miss OT] Independent way to identify good tracks in one stream: By eye ? Standard reconstruction on clean events with wide windows ? Understanding how efficiency/ghost rates scale with occupancy from MC Tracks gained in merging - ghosts, Losses: inefficiency Even if we cannot extract absolute numbers can make relative MC-data comparisions All this exists, just a case of plugging it together

4. Merging Scheme I Re-use the TAE merging code Simple to do, code exists + well tested But: Don’t use all information [e.g. neighbour sum] For overlapping clusters algorithm selects only the best

5. Merging Scheme II Algorithms exist in ST/Velo flavours Breaking in digits is easy, merging uses all info [including neighbour sum] Re-clustering: as in Boole All info is in conditions database and is consistant with the data

6. Matching Study in MC LHCb-2007-020 Yield versus matching cut Yield versus # candidate long tracks per seed

7.  2 /dof Study J/  Ks