Status of Monitoring in HLT ALgorithms Ricardo Gonçalo, RHUL – BNL Analysis Jamboree – Aug. 6, 2007.

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Ricardo Gonçalo, RHUL BNL Analysis Jamboree – Aug. 6, 2007
Presentation transcript:

Status of Monitoring in HLT ALgorithms Ricardo Gonçalo, RHUL – BNL Analysis Jamboree – Aug. 6, 2007

Ricardo Goncalo, RHULBNL Jamboree, 6 Aug 072 Monitoring infrastructure HLT algorithms (both FEX and Hypo) instrumented to produce histograms of internal variables Monitored variables exposed by the algorithms and collected after the AlgoX::execute() method Primitive type variables, objects (through accessor) and collections can be monitored Used both for online monitoring/data quality and offline software validation See: Tomasz’s talk in For instructions, see:

Ricardo Goncalo, RHULBNL Jamboree, 6 Aug 073 Monitoring infrastructure (cont.) Easy to add to algorithms Just declare variables to be monitored –Relevant methods in HLT algos base class Declare wanted histograms –No overhead if undeclared Advisable to reset variables every event with unphysical values

Ricardo Goncalo, RHULBNL Jamboree, 6 Aug 074 Triggerp T threshold(*)Obs  E T (jets) ?? E T miss 12, 20, 24, 32, 36, 44 Prescale E T miss 52, 72No presc J/  ee TopologicalB-phys    4 J/    TopologicalB-phys BsDsPhiPiTopologicalB-phys BXBX e + E T miss 18+12Prescale  + E T miss 15+12No presc Jet + E T miss 20+30No presc 2 Jets + E T miss 42+30No presc Jet+ E T miss +e No presc Jet+ E T miss +  No presc 4 Jet + e23+15No presc 4 Jet +  23+15No presc  + E T miss 15+32,25+32, 35+20,35+32  + e 10+10Express  +  10+6Express 2  + e 10+10Express Triggerp T threshold(*)Obs Electron5,10,15,Prescale Electron20,25,100No presc Di-electron5,10Prescale Di-electron15No presc Photon10,15,20Prescale Photon20No presc Di-photon10Prescale Di-photon20No presc Jets5,10,18,23,35,42,70Prescale Jets100No presc 3 Jets10,18B-tag 4 Jets10, 18B-tag 4 Jets23Express  10, 15, 20, 35 Di-  10+15,10+20,10+25 Muon4, 6, 10, 11, 15, 20, 40Muon spectr. Muon4, 6, 10, 11, 15, 20, 40ID+Muon Di-muon4, 6, 10, 15, 20Passtthr. ETET 100, 200, 304prescale ETET 380No presc

Ricardo Goncalo, RHULBNL Jamboree, 6 Aug 075 L2 electron slice: e10 T2Calo_egamma on 1k ttbar events Other hypotheses similar (but less events)

Ricardo Goncalo, RHULBNL Jamboree, 6 Aug 076 L2 electron slice: L2CaloHypo

Ricardo Goncalo, RHULBNL Jamboree, 6 Aug 077

Ricardo Goncalo, RHULBNL Jamboree, 6 Aug 078 Other things: Steering monitoring: RoI eta vs phi Events passed at each chain step etc… Step counters (for Hypo algorithms): For algorithm experts: where do most events get rejected…

Ricardo Goncalo, RHULBNL Jamboree, 6 Aug 079 EF Photon Hypo

Ricardo Goncalo, RHULBNL Jamboree, 6 Aug 0710 L2 tracking for e/gamma (IDScan)

Ricardo Goncalo, RHULBNL Jamboree, 6 Aug 0711 IDScan (cont.) Missing histograms in IDCaloHypo (track-cluster matching)

Ricardo Goncalo, RHULBNL Jamboree, 6 Aug 0712 T2Calo_Jets

Ricardo Goncalo, RHULBNL Jamboree, 6 Aug 0713 Missing ET

Ricardo Goncalo, RHULBNL Jamboree, 6 Aug 0714 Missing ET

Ricardo Goncalo, RHULBNL Jamboree, 6 Aug 0715 Timers Many histograms ofalgorithmexecution timeproduced Numbers from RTT give onlyball-parkestimate, butstill useful Many still need tuning

Ricardo Goncalo, RHULBNL Jamboree, 6 Aug 0716 Viewing results: AID and DCube Analysis Interpretation & Display (A.Undrus, Brookhaven) Shows monitoring plots (algorithms, timers, steering) Shows statistical comparison with reference (DCube, K.Ciba) Linked from the NICOS nightlies pages ATN results for now, RTT to follow See Alex’s and Krzysztof Ciba’s talk in:

Ricardo Goncalo, RHULBNL Jamboree, 6 Aug 0717 Conclusions The algorithm and timing Monitoring histograms are used for software validation –There are currently 735 histograms being filled Produced every night by RTT for 1000 top events Not fine tuned in many cases, but very useful