12 Triggering and High-Level Data Selection
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example, electron showers are small and extremely well defined in the transverse
and longitudinal planes. Information from a few Electromagnetic and Hadronic
calorimeter towers at the L1T, the corresponding elements of the preshower detector,
and a small region of the tracking volume (at higher trigger levels) are sufficient for
electron identification. The only global entities are neutrinos (from a global sum of
missing E T ).
For the trigger to be measurably efficient the tools to measure lepton and jet
efficiencies must be built into the trigger architecture from the start. One such
tool is overlapping programmable triggers so that multiple triggers with different
thresholds and cuts that can run in parallel. A second tool is pre-scaled (e.g. random
selection of a fraction) triggers of lower threshold or weaker criteria that run in
parallel with the stricter triggers. A third tool is pre-scaling of a particular trigger
with one of its cuts removed.
The requirement on the use of DAQ bandwidth implies two conditions. First,
each level of the trigger attempts to identify leptons and jets as efficiently as
possible, while keeping the output bandwidth within requirements. The selected
event sample should include all events that would be found by the full offline
reconstruction. Hence, the selection criteria in the trigger must be consistent with
those of the offline. Second, since the bandwidth to permanent storage media is
limited, events must be selected with care at the final trigger level.
The measurement of trigger efficiency requires the flexibility to have overlapping
triggers so that efficiencies can be measured from the data. The overlaps include
different thresholds, relaxed individual criteria, prescaled samples with one criterion
missing, and overlapping physics signatures. For example, measurement of the
inclusive jet spectrum uses several triggers of successively higher thresholds, with
the lower thresholds prescaled by factors that allow a reasonable rate to storage.
These triggers overlap in jet energy all the way down to minimum bias events so
that the full spectrum can be reconstructed accurately. The efficiency and bias of
each higher threshold can be measured from the data sets of lower threshold. A
requirement for understanding the trigger efficiency is that the data used as input
to the L1T system is also transmitted via the DAQ for storage along with the event
readout data. In addition, all trigger objects found, whether they were responsible
for the L1 trigger or not should also be sent.
The L1T accept rate is limited by the speed of the detector electronics readout and
the rate at which the data can be harvested by the data acquisition system. Since it
is pipelined and deadtimeless, the L1T renders a decision on every bunch crossing.
The maximum L1T accept rate is set by the average time to read information for
processing by the Higher Level Triggers (HLT) and the average time for completion
of processing steps in the HLT logic.
The high operational speed and pipelined architecture also requires that specific
data is brought to specific points in the trigger system for processing and that there
cannot be fetching of data based on analysis of other data in an event. The data must
flow synchronously across the trigger logic in a deterministic manner in the same
way for each crossing. At any moment there are many crossings being processed
in sequence in the various stages of the trigger logic. The consequence is that most
537
example, electron showers are small and extremely well defined in the transverse
and longitudinal planes. Information from a few Electromagnetic and Hadronic
calorimeter towers at the L1T, the corresponding elements of the preshower detector,
and a small region of the tracking volume (at higher trigger levels) are sufficient for
electron identification. The only global entities are neutrinos (from a global sum of
missing E T ).
For the trigger to be measurably efficient the tools to measure lepton and jet
efficiencies must be built into the trigger architecture from the start. One such
tool is overlapping programmable triggers so that multiple triggers with different
thresholds and cuts that can run in parallel. A second tool is pre-scaled (e.g. random
selection of a fraction) triggers of lower threshold or weaker criteria that run in
parallel with the stricter triggers. A third tool is pre-scaling of a particular trigger
with one of its cuts removed.
The requirement on the use of DAQ bandwidth implies two conditions. First,
each level of the trigger attempts to identify leptons and jets as efficiently as
possible, while keeping the output bandwidth within requirements. The selected
event sample should include all events that would be found by the full offline
reconstruction. Hence, the selection criteria in the trigger must be consistent with
those of the offline. Second, since the bandwidth to permanent storage media is
limited, events must be selected with care at the final trigger level.
The measurement of trigger efficiency requires the flexibility to have overlapping
triggers so that efficiencies can be measured from the data. The overlaps include
different thresholds, relaxed individual criteria, prescaled samples with one criterion
missing, and overlapping physics signatures. For example, measurement of the
inclusive jet spectrum uses several triggers of successively higher thresholds, with
the lower thresholds prescaled by factors that allow a reasonable rate to storage.
These triggers overlap in jet energy all the way down to minimum bias events so
that the full spectrum can be reconstructed accurately. The efficiency and bias of
each higher threshold can be measured from the data sets of lower threshold. A
requirement for understanding the trigger efficiency is that the data used as input
to the L1T system is also transmitted via the DAQ for storage along with the event
readout data. In addition, all trigger objects found, whether they were responsible
for the L1 trigger or not should also be sent.
The L1T accept rate is limited by the speed of the detector electronics readout and
the rate at which the data can be harvested by the data acquisition system. Since it
is pipelined and deadtimeless, the L1T renders a decision on every bunch crossing.
The maximum L1T accept rate is set by the average time to read information for
processing by the Higher Level Triggers (HLT) and the average time for completion
of processing steps in the HLT logic.
The high operational speed and pipelined architecture also requires that specific
data is brought to specific points in the trigger system for processing and that there
cannot be fetching of data based on analysis of other data in an event. The data must
flow synchronously across the trigger logic in a deterministic manner in the same
way for each crossing. At any moment there are many crossings being processed
in sequence in the various stages of the trigger logic. The consequence is that most