outline: hlt overview objectives of muon+hadron algorithm flow selection parameters monitoring...

Outline:• HLT overview• Objectives of muon+hadron• Algorithm flow• Selection parameters• Monitoring• Summary required• Determination of

parameters

Antonio Pérez-CaleroHugo Ruiz

31/05/2007Hugo Ruiz – Winter Meeting Santiago

20072

Single

L0 L0 di

Good hadrs

Confirm

Extra s

Di + hadr

Extra s

Recovr’d di

+ hadrSingle Generic B

D* J/Exclusive sels.

~ 1 KHz

o(10 KHz)

Confirm

o(100 KHz)

Alleys

Global HLT reconstruction

• Source of TOS for semileptonics: give ~ all semileptonics to excl/incl selection– Efficiency on BdD*:


20073

(Pessimistic)

• Source of tagging-enriched TIS for any other channel:

4109 unbiased B per year


20074

• Better: count only events with other B in acceptance

• Even better: TIS efficiency on offl-selected evts

• And tageff

• Exemple with the released version( pT>1GeV, hadr pT>0.5GeV, both |IP| in [0.1,3]mm, DOCA<0.2mm, Pointing<0.4, Dz>2mm)

– 4.36 KHz, somewhere between alley and incl. selection

• For muonless samples of Hans’ preselection (Bs2Dspi, Bs2DsDs, Bd2Kpi, Bd2D0Kst, Bd2KstD2KSPiPi, Bs2PhiPhi, Bu2KD->KSPiPi, Bd2PiPiPi, Bu2Kee)

– Events: 10,808– L0: 55%– HltMuHad: 4.8% of those passing L0– TOS: 2.25% of those passing L0– TIS: 1.7 % of those passing L0

• With extra tagging power• Additional: nice muon id calibration sample?


20075

31/05/2007Hugo Ruiz – Antonio Perez-Calero, HLT workshop at

Amsterdam6

L0+T+VELO

pT, IP

VELO 3D

hadron IP

hadron pT

VELO forward

Pointing, Dz

VertexMaker: DOCA

VertexUpgrader

Muon Hadron TrackFilter

VertexFilter

VertexUpgrader

Reconstruction

Legend:

VertexMaker

Seq

uen

ce

r

1

2

3

3 selection steps

• Selection: 7 parameters (2: alley & selection)– : pT, Ipmin (Ipmax)– Hadron: pT, Ipmin (IPmax)– Vertices: DOCA, Pointing, z

Need still to study:– IP vs IPS– Can remove Ipmax? (yes from eff vs rate)

• Reconstruction:All parameters for:– Confirmed muon– VELO3D– VELO-forward


20077

• What we need is what the standard algorithms will provide at the 3 selection steps: EVERYTHING

• This includes:– Input and output rate at each filtering level– # of input and output candidates at each filtering

level• Note that #filtering levels > #algorithms

– Distributions of the variables in which we cut• And of the ‘best’ of the candidates, to be able to extract the

dependence of the rate on the variable

• Need: HLT-PVSS GUI that allows navigation in HLT alleys and algorithms to display rates, variable distributions…

• For shift team: rates + access to variable reference histos (with reference histos)


20078

1. For TISTOSing:– Needed only summary from last

HltVertexFilter• Vertices that triggered• Their two tracks

– For TIS/TOSING based on summary to be a better approximation, we need the reco algos to give a chance to ALL good combinations

– Not the best nth, for instance


20079

2. To study alley performance (on offl-selected events) we need at each step:– Output candidates (LHCb id’s)– Cut values

• Extinguish “pretrigger and trigger”

• The info is made available from the standard algorithm summaries

• # objects in summaries:


200710

Filters cands

Mu|IP| [0.1,3]mm 1,52

HltRecoVelo 114

Had|IP| [0.1,3]mm

DOCA<0.2mm 13,21VELO=>

Forward 16,90

HadPT> 500MeV 3,22

2nd DOCA cut 3,76

Pointing<0.4

Dz>2mm 2,47

• Aim: highest possible:1. Semileptonic efficiency on alley2. B purity on incl. selectionfor the allowed rate(muon alley will optimize muon confirmation

efficiency)

• Rate at any set of parameters: from running HLT emulation on sample of random L0 triggered events

• Assume:– 1st ~ 2fb-1 of data on disk– Then try to think about first months…


200711

1. Alley: efficiency on TOS for semileptonics– Need to optimize it on TIS, running the HLT simulation– How many TIS semileptonics will have?

• B/S>0, but shouldn’t expect HLT to do better than offline!

• How many triggered on the other side?15 M * 0.5 * 0.015 = 110 K events / year

– Can use this events to optimize alley parameters• Plot in slide 3 done with ~ 1 K events


200712

ChannelYield/2 fb-

1 B/S

Bd0 D* - + 9 M 0.4

B+ D0 (*) + 3.5 M 0.6

Bs Ds(*) + 2 M 0.6

2. Incl. sel: efficiency on TIS for generic B– Take as starting point decays with highest

rates:• B+ J/K+: 1.74 M events/year, B/S = 0.37

• Bd J/K*: 0.67 M events/year, B/S = 0.17– >2M with kite good B/S

– Require TOS: ~ 100%– Pass a typical muon+hadron:

2 M * 0.5 * 0.015 ~ 15 K events / year in the interesting region of an optimization plot (TIS efficiency vs rate).


200713

• Assume 21032, 107 sec/year:1.Alley: 1.5 Hz of semileptonics (plot like in

slide 2 in 1000 seconds, ~ 20’)2.Incl. sel:

– J/K+/K*: 0.2 Hz– Need more. Can add more selections, in

particular add semileptonics of point 1 to get a sample of ~ 2Hz• Use them to evaluate trigger on the other

side• Here penalisation of factor ~ 0.5 * 0.015

need 30 hours to get same statistics


200714

• If for these events we:– save the output of the HLT global reconstruction– send them to the monitoring farm

• We can obtain online Antonio’s plots:


200715

• Muon from muon alley summaries

• Hadron from global reconstruction summaries

• Rate from monitoring of cut variables

• At start-up lumi << 21032

– Any combination of cuts will give << allowed rate– Just apply a set of minimum cuts to debug the

alley!

• Under the carpet:– GEC/no GEC– Different luminosity setups


200716

outline: hlt overview objectives of muon+hadron algorithm flow selection parameters monitoring...

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