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18-tistos-diy.md

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First steps in LHCb
TisTos DIY
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Learning Objectives {.objectives}

  • Learn what TisTos is and why it's useful
  • Use an interactive python session to look at TisTos on a local DST

The LHCbIDs of the final state particles of the candidate that was created offline (in the stripping or by your script) can be compared to those saved by the HLT to find out if the offline candidate was accepted by the trigger. The classification that results from this comparison is called TisTos (Trigger independent of Signal/Trigger on Signal).

An offline candidate is considered to be Tos with respect to a trigger selection if it was accepted by that trigger selection. In more formal terms, if the LHCbIDs of each of the final state particles of the candidate accepted by the trigger selection overlap for more than 70% with the LHCbIDs of final state particles of the offline candidate.

For example, the Hlt1TrackAllL0 line accepts an event if there is at least one track with a lot of PT and a large IPCHI2. If any of the tracks accepted by the Hlt1TrackAllL0 line overlap for more than 70% with one of the tracks of the offline candidate, it is Tos with respect to Hlt1TrackAllL0. If Hlt1DiMuonHighMass is considered instead, then the LHCbIDs of both tracks that make-up the Hlt1DiMuonHighMass candidates must overlap with the LHCbIDs of two tracks that are part of the offline candidate.

To have a look at how this works, we'll use candidates from the D2hhPromptDst2D2RS selection, which can be retrieved thusly:

candidates = evt['AllStreams/Phys/D2hhPromptDst2D2RSLine/Particles']
candidates.size()

It could be that there are more than one candidates, which are unlikely to all be real. MC matching could be used to find the real one when running on simulation and on data a single candidate can be selected, either randomly or using some criterium. Dealing with multiple candidates correctly is beyond the scope of this tutorial, so just always take the first one in the container.

Let's use the TriggerTisTos tool now. In preparation for Run-II, the Hlt1 and Hlt2 DecReports and SelReports are now stored in different locations. That means two TisTos tools will be needed, each configured to pick up information from either HLT1 or HLT2. Since the tools we create are public tools, they have to be configured in the following way (before the AppMgr is instantiated):

from Configurables import ToolSvc, TriggerTisTos
ToolSvc().addTool(TriggerTisTos, "Hlt1TriggerTisTos")
ToolSvc().Hlt1TriggerTisTos.HltDecReportsLocation = 'Hlt1/DecReports'
ToolSvc().Hlt1TriggerTisTos.HltSelReportsLocation = 'Hlt1/SelReports'

Create the tools in the same way you created others during the other lesson, but use instance-specific names that correspond to the configuration we just added: Hlt1TriggerTisTos and Hlt2TriggerTisTos. The tools use ITriggerTisTos as an interface.

Use the advance function to find an event that has some candidates for the chosen selection and set the TisTos tools to use our candidate and trigger selection:

hlt1TisTosTool.setOfflineInput()
candidate = candidates[0]
hlt1TisTosTool.addToOfflineInput(candidate)
hlt1TisTosTool.setTriggerInput()
hlt1TisTosTool.addToTriggerInput("Hlt1TrackAllL0Decision")
result = hlt1TisTosTool.tisTosTobTrigger()
result.tos()

The set calls reset the internal storage of candidate or trigger information, and the addTo calls then add the things we are interested in.

An offline candidate is considered to be Tis with respect to a trigger selection if removing it from the event would still cause the trigger selection to accept the event, i.e. if there is another particle in the event that was also accepted by the trigger selection. In more formal terms, if the LHCbIDs of the all of the final state particles of any of the candidates accepted by the trigger selection overlap less than 1% with all of the LHCbIDs of the final state particles of the offline candidate.

result.tis()

Note that a candidate can be both Tis and Tos with respect to a trigger selection, or Tos with respect to one selection, and Tis with respect to another. To tell the tool to consider more trigger selections, use the following (regexes are also supported), and try to find some events that are both Tos and Tis:

hlt1TisTosTool.setTriggerInput()
hlt1TisTosTool.addToTriggerInput("Hlt1TrackAllL0Decision")
hlt1TisTosTool.addToTriggerInput("Hlt1DiMuonHighMassDecision")
result = hlt1TisTosTool.tisTosTobTrigger()

The (Tos) trigger efficiency of a trigger selection can be calculated as:

$\epsilon_{\mathrm{Tos}}=N_{\mathrm{Tis}&\mathrm{Tos}} / {N_{\mathrm{Tis}}}$

Loop over the events in the DST and calculate the efficiency of Hlt1TrackAllL0. You can add some more Hlt1 selecitons when checking for Tis, which ones would make sense?

There is a third classification, which is called Tob. This is the case if the overlap — as defined for Tis and Tos — is between 1% and 70%.

To determine if a candidate is a combination of Tis, Tos and Tob or none of these, an LHCb software tool has been created that calculates the overlaps and classifies candidates with respect to trigger selection. This tool is called TriggerTisTos and it implements the ITriggerTisTos interface.