PANDA Software Trigger K. Gtzen Dec. 2012 Challenge Events/Data - - PowerPoint PPT Presentation
PANDA Software Trigger K. Gtzen Dec. 2012 Challenge Events/Data acquired by DAQ (temporarily buffered) Software Trigger Algorithms Trickle of events stored on disc Required reduction factor: ~1/1000 (all triggers in total) A
Software Trigger Algorithms
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– Physics Book Channels – Charged particles only – Combinatorics (inclusive) – Invariant masses – PID information
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– selector efficiencies and – misID levels; 𝑛𝑗𝑡𝐽𝐸 =
# 𝑏𝑑𝑑𝑓𝑞𝑢𝑓𝑒 𝑥𝑠𝑝𝑜 𝑢𝑧𝑞𝑓 𝑞𝑏𝑠𝑢𝑗𝑑𝑚𝑓𝑡 #𝑏𝑚𝑚 𝑥𝑠𝑝𝑜 𝑢𝑧𝑞𝑓 𝑞𝑏𝑠𝑢𝑗𝑑𝑚𝑓𝑡
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e mu pi K p e eff misID misID misID misID mu misID eff misID misID misID pi misID misID eff misID misID K misID misID misID eff misID p misID misID misID misID eff
Particle Type Selector
fraction of pions acc. by electron selector fraction of electrons acc. by pion selector
– selector efficiencies and – misID levels; 𝑛𝑗𝑡𝐽𝐸 =
# 𝑏𝑑𝑑𝑓𝑞𝑢𝑓𝑒 𝑥𝑠𝑝𝑜 𝑢𝑧𝑞𝑓 𝑞𝑏𝑠𝑢𝑗𝑑𝑚𝑓𝑡 #𝑏𝑚𝑚 𝑥𝑠𝑝𝑜 𝑢𝑧𝑞𝑓 𝑞𝑏𝑠𝑢𝑗𝑑𝑚𝑓𝑡
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e mu pi K p e 0,95 0,05 0,05 0,05 0,05 mu 0,05 0,95 0,05 0,05 0,05 pi 0,05 0,05 0,95 0,05 0,05 K 0,05 0,05 0,05 0,95 0,05 p 0,05 0,05 0,05 0,05 0,95
Particle Type Selector
fraction of pions acc. by electron selector fraction of electrons acc. by pion selector
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J/ψ→ ℓ+ℓ - D0→ K-π+ D+→ K-π+π+ Ds
+→ K+K-π+
φ→ K+K- Λc→ pK-π+
... of 0.3% of the DD events due to BR2...
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J/ψ→ ℓ+ℓ - D0→ K-π+ D+→ K-π+π+ Ds
+→ K+K-π+
φ→ K+K- Λc→ pK-π+
... of all(!) DD events
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J/ψ→ ℓ+ℓ - D0→ K-π+ D+→ K-π+π+ Ds
+→ K+K-π+
φ→ K+K- Λc→ pK-π+
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J/ψ→ ℓ+ℓ - D0→ K-π+ D+→ K-π+π+ Ds
+→ K+K-π+
φ→ K+K- Λc→ pK-π+
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J/ψ→ ℓ+ℓ - D0→ K-π+ D+→ K-π+π+ Ds
+→ K+K-π+
φ→ K+K- Λc→ pK-π+
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0,0 2,0 4,0 6,0 8,0 10,0 12,0 14,0 3,500 4,000 4,500 5,000 5,500 6,000
Selected Background [%] sqrt(s) [GeV]
Background levels (PID misID = 5%)
J/psi -> ll D0 -> K pi D+ -> K pi pi Ds -> K K pi phi -> K K Lc -> p K pi All
– Invariant mass cuts seem insufficient to reduce background – Displaced vertices online for cτ ≈ 50-200μm w/o precise IP?
– What if DPM is not realistic?
– Toy MC, Full MC, Online Reco Algorithms?
performance?
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0,00 10,00 20,00 30,00 40,00 50,00 60,00 70,00 80,00 90,00 100,00 0,0 10,0 20,0 30,0 40,0 50,0
Required time difference for event separation
Overlap probability (sequent events)
50 MHz 20 MHz 10 MHz 5 MHz 2 MHz 1 MHz
Probability [%]
merging
– Toy MC (DPM; generator level) background events – Merge fraction of sequent events corresponding to Pmix – Apply algorithm and determine amount of background feedthrough – Vary PID mis-ID levels (flat) – Vary center-of-mass energy
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0,0 10,0 20,0 30,0 40,0 50,0 60,0 70,0 80,0 90,0 100,0 0,00 0,10 0,20 0,30 0,40 0,50
Background feedthrough [%] Overlap Probability
Background feedthrough @ 3.77 GeV
no PID misID = 20% misID = 5% misID = 1%
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0,0 10,0 20,0 30,0 40,0 50,0 60,0 70,0 80,0 90,0 100,0 0,00 0,10 0,20 0,30 0,40 0,50
Background feedthrough [%] Overlap Probability
Background feedthrough @ 5.5 GeV
no PID misID = 20% misID = 5% misID = 1%
– Toy MC studies; performance of simultaneous algo‘s – Toy/Full MC studies (→ Donghee) – Influence of PID on background suppression (→ Donghee) – Event source simulation (→ Mohammad) – Online Track reco (→ Yutie, Marius, Sean)
– Compile full list of signatures & develop algorithms – Study neutral particles/channels – Open charm/baryon selection with displace vertices – Alternative background generation
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– Multiplicity tracks/neutrals – kinematic distributions – event shape, ...
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Distinguish between:
– Selection algorithms based on information available online → Task of Software Trigger
– Time ordering / Tracking / Clustering / Track-Cluster-PID- Matching / Event building → Should mainly be addressed by Detector/FEE/DAQ people → Will be addressed both in this session
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(cut on momenta, cut on masses, PID, fitting, etc...) → Klaus; DONE
(ideally in terms of signal efficiency/background suppression). → not assigned; addressed partially in algorithm development
(efficiency, momentum resolution, energy resolution, partial PID informtation → Donghee's; work in progress
→ Donghee + Klaus; work in progress
(incl. ↔ excl. triggers), implementation when necessary. Depends on level of event mixing. → Klaus; work in progress
(in particular pattern recognition and reconstruction based on a time sequential digi stream) → done for some detectors → is up to the detector subgroups.
→ TBD
Use time based simulated detector digis to feed into hardware to test function and performance. → Mohammad
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J/ψ→ ℓ+ℓ - D0→ K-π+ D+→ K-π+π+ Ds
+→ K+K-π+
φ→ K+K- Λc→ pK-π+
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J/ψ→ ℓ+ℓ - D0→ K-π+ D+→ K-π+π+ Ds
+→ K+K-π+
φ→ K+K- Λc→ pK-π+
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J/ψ→ ℓ+ℓ - D0→ K-π+ D+→ K-π+π+ Ds
+→ K+K-π+
φ→ K+K- Λc→ pK-π+
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J/ψ→ ℓ+ℓ - D0→ K-π+ D+→ K-π+π+ Ds
+→ K+K-π+
φ→ K+K- Λc→ pK-π+
→ need a realistic test scenario
Do time ordered simulation + simulate hardware digi stream
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