Pass8 tag calorimeter ghost with trigger diagnostic information

This page presents an attempt to use the trigger TEM diagnostic information to recognize calorimeter ghost signal.
Pass 8 Recon & Analysis Upgrades Weekly Meeting Agenda

Summary

• Code and method
• Ghost number and cluster classification
• Event display

Information available

• The TEM trigger diagnostic information provides trigger information on CalLo and CalHi per each layer end.
• From Sasha:

  The code example for accessing CAL diagnostic info I can recommend you is the algorithm from calibGenCAL package used for trigger thresholds measurement:
  /afs/slac/g/glast/users/chehtman/calibGenCAL_analysis/calibGenCAL/calibGenCAL-05-06-00/src/lib/Algs/LPATrigAlg.cxx
  The method
  LPATrigAlg::fillTrigBitArray()
  just extracts CAL trigger bits from a digi file and puts them into some local structure.

• As a reminder, CalLo is at 100 MeV and CalHi at 1 GeV.

Analysis

Code and method

• Starting from Sasha's example, I wrote a piece of code that does the following:

1. open digi, recon, merit and relation files

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.

• Basic version of the code: tagghosts.C
  • Another version of the code, algorithm is the Johan's one, but output is a tree with number of tagged xtals and sum-of-energy of tagged xtals for the first 3 cluster tagghosts_v1.C .

Overlay energy for tagged cluster

I run the tagghosts_v1.C on the 100 AG-GR-v19r4p1gr14-OVL, and looked at the Overlay energy in the first and second cluster vs. the ghost-tagged energy.
The selection is just trigger and filter and CalNumClusters>0. I also required that there is at least 1 tagged xtals, if there are no such xtlas there is nothing to say.
Few details on the algorithm:

• There are two tagging options: conservative (if both xtal ends are ghost-like) and permissive (if at least one xtal end is ghost-like).
  • I used 'permissive' since purity is high and efficiency is low (see below).
• The energy threshold for tagging a xtal end is set to 120 MeV (no good reason for this number, need to be optimized).

Here the plots for first (left) and second (right) cluster.

My conclusions are:

• Johan algorithm works fine!
• Clusters with at least one ghost-tagged xtal tend to have large overlay energy.
• It never happen that overlay energy is 0 and there is some ghost-tagged xtal (thanks also to the energy threshold set to 120 MeV). The 'purity' of such selection is ~1.
  • Efficiency is low. We can't tag a good fraction of ghost clusters in this way.
• There are few events with low overlay energy and low ghost-tagged energy. After looking at event displays I think we can consider good cluster is the ghost-tagged energy is <~3%
  • My suggestion for ghost tagging ghost clusters is "TagGhostNumXtals>0 && (TagGhostRawEnergySum/CalawEnergySum)> 0.03"
• We can include this algorithm just after clustering, and then tag ghost clusters. Not sure how we should use this info:
  • Select best cluster only if non ghost-tagged - what happen if the best cluster is also a ghost (i.e. there are no other options?)?
  • Use this info in tracking ( e.g. knowing that the direction is likely to be wrong). Need Tracy here...
  • Use in event-level analysis to select events with useless cal information (and treat them as tracker only if possible)

Ghost number and cluster classification

• tbd

Event display