SS kaon tagger + OS taggers

SS KAON TAGGER + OS TAGGERS

Míriam Calvo, Marco Musy

12th March 2009

NNET APPROACH 1st step: calibrate p0, p1

2nd step: Use the probability per event to split into 5 categories

Or, use a probability per event including same side

2

i = p0 + p1 i i = SS k , , e, OS k, Qvtx

OS from B+J/ K+ (directly)

SS from BsDs (oscillation)

OS of each category from B0J/ K* (oscillation)

eff increases to 4.5% including SS k(6.4% splitting into 5 categories)

eff increases from 2.2% to 3.4% using 5 categories

Tagging BsJ/

COMBINING SS WITH OS TAGGERS How to proceed?

Combine SS kaon to get an event-per-event probability of mistag using the already sorted tagging categories for the OS taggers.

Some conditions are needed: SS kaon vs Nnet output is the same among Bs

decays. SS and OS taggers mistag are not correlated.

Trigger effect also need to be studied.3

Compatible – same SSK (Nnet output) dependence for all Bs decaysBsDs can be used to calibrate Nnet output from dataBsDs higher annual yield, but poor proper time resolution (use double tagging method)

SS KAON VS NNET OUTPUT

4

BsJ/ (98k sel)BsDs (133k sel)BsDs (27k sel)

Fit p0+p1*xp0=(7±9)·10-3

p1=0.97±0.03p0= (-1.0±0.7)·10-2

p1=1.00±0.02p0= (0.8±1.5)·10-2

p1=0.95±0.04

SS K

Correlation matrix (Tagger decision):------------------------------------------------ e OS k SS k Qvtx: +1.00e: -0.01 +1.00OS k: +0.03 +0.03 +1.00SS k: +0.02 +0.01 +0.03 +1.00Qvtx: +0.10 +0.07 +0.19 +0.02 +1.00------------------------------------------------

Higher correlations between Q_vrtx and OS muon & kaon taggersbut not with Same Side

TAGGER CORRELATIONS

5

BsJ/

SS kaonOS taggers

Offset due to OS correlationsMistag is under-estimated(product of individual taggers probabilities)

+

OS+SS

Correlation Qvtx and other OS taggers is ~ 10-20%

6

true true

true

1- p 1- p

1- p

OS taggers () calibrated with B+J/K+. SS kaon () calibrated with BsDs.

Get rid of OS correlations by measuring OS in 5 categories (Nnet approach) in a control sample as B0J/K*.

REMINDER

7

(combined)OS = 36.25 ± 0.24%tag = 44.97 ± 0.16%eff = 3.40 ± 0.11%1 2 3 4 5

OS taggers

Prob

Bd2JpsiK* (cat1) = 44.2±0.3 (cat2) = 36.3±0.5 (cat3) = 29.0±0.6 (cat4) = 23.4±0.7(cat5) = 18.2±0.5

SS kaonOS taggers

OS taggersTaking measured In 5 categories(to get rid of correlations)

+

OS+SSCorrelation OS taggers – SS kaon is 2.2%

8

1- p 1- p

1- p

truetrue

true

Bd2JpsiK* (cat1) = 44.2±0.3 (cat2) = 36.3±0.5 (cat3) = 29.0±0.6 (cat4) = 23.4±0.7(cat5) = 18.2±0.5

OS + Same Side probability:

)1()1()1( , SSOSiTOT ppp

Probability per event for Bs2JpsiPhi

p i, OS

)1()1())1(),1(max(

TOTTOT

TOTTOT

ppppp

GETTING A PROBABILITY PER EVENT FROM OS+SS

9

(average) = 35.77 ± 0.21%tag = 55.54 ± 0.16%eff = 4.50 ± 0.13%1 2 3 4 5

p

(combined) = 33.19 ± 0.21%tag = 55.54 ± 0.16%eff = 6.28 ± 0.15%

Splitting into 5 categories

eff (OS) = 3.40%

OS taggers alone (without Qvtx)

PROBABILITY PER EVENT WITHOUT QVTX

10

No bias due to correlations

OS + SS (average) = 32.10 ± 0.25%tag = 38.18 ± 0.16%eff = 4.89 ± 0.14% (> 4.50%)

OS (combined) = 31.1 ± 0.3%tag = 20.48 ± 0.13%eff = 2.93 ± 0.10% (< 3.40%)

Approximately same effective efficiency without Qvtx (work on-going to improve this tagger)

OS + SS (combined) = 30.10 ± 0.25%tag = 38.18 ± 0.16%eff = 6.05 ± 0.14% (< 6.41%)

true

1- p

INTO CATEGORIES…

11

tag OS OS eff OS tag OS OS eff OSCat 1 27.18±0.15 44.9±0.3 0.28±0.04 6.14±0.08 40.8±0.7 0.21±0.03Cat 2 6.97±0.08 35.5±0.6 0.59±0.05 6.53±0.08 34.3±0.6 0.64±0.05Cat 3 5.02±0.07 30.8±0.7 0.74±0.05 4.81±0.07 28.0±0.7 0.93±0.06Cat 4 3.68±0.06 26.0±0.8 0.84±0.06 1.94±0.05 21.4±1.0 0.63±0.05Cat 5 2.13±0.05 16.7±0.8 0.95±0.05 1.07±0.03 15.0±1.1 0.52±0.04

average 45.0±0.2 39.0±0.2 2.19±0.10 20.48±0.13 32.6±0.3 2.49±0.10combine

d45.0±0.2 36.2±0.2 3.40±0.11 20.48±0.13 31.1±0.3 2.93±0.10

with Qvtx without Qvtx

BsJ/

tag OS + SS

OS + SS

eff OS + SS

tag OS + SS

OS + SS eff OS + SS

Cat 1 27.91±0.15 43.4±0.3 0.49±0.05 13.95±0.11 40.6±0.4 0.49±0.05Cat 2 10.13±0.10 35.4±0.5 0.86±0.06 9.90±0.10 34.3±0.5 0.98±0.06Cat 3 7.41±0.09 29.5±0.6 1.24±0.07 7.07±0.08 27.2±0.6 1.47±0.07Cat 4 5.33±0.07 24.3±0.6 1.41±0.07 3.78±0.06 20.9±0.7 1.29±0.06Cat 5 4.73±0.07 14.4±0.5 2.40±0.08 3.48±0.06 13.7±0.6 1.83±0.07

average 55.5±0.2 35.8±0.2 4.50±0.13 38.2±0.2 32.1±0.2 4.89±0.14combine

d55.5±0.2 33.0±0.2 6.41±0.15 38.2±0.2 30.1±0.2 6.05±0.14

L0 TISTOSTOBBINGPhase space corrections…12

13

SS K

Nnet output

Fit p0+p1*xp0=(10±9)·10-3

p1=0.96±0.03p0= (-8±8)·10-4

p1=0.95±0.01p0= (4.3±1.6)·10-3

p1=0.92±0.05

Same dependence before/after L0.

SS K

Nnet output

Fit p0+p1*xp0=(7±9)·10-3

p1=0.97±0.03p0= (-1.0±0.7)·10-2

p1=1.00±0.02p0= (0.8±1.5)·10-2

p1=0.95±0.04

Before L0

After L0

BsJ/ (98k sel)BsDs (133k sel)BsDs (27k sel)

BsJ/ (92k sel)BsDs (63k sel)BsDs (24k sel)

vs Nnet

Fit p0+p1*xp0=0.408±0.006p1=(-7.4±0.7)·10-3

p0=0.417±0.008p1=(-9.1±0.7)·10-3

p0=0.416±0.013p1=(-9.2±1.1)·10-3

SS K

rec’ted Bs pt

14

Differences in phase space can introduce differences in of a given category among decays.

Corrections can be performed once known the (Bs pt) dependence (obtained from control channels).

After L0

OS K

rec’ted Bs pt

BsJ/ BsDs BsDs

Fit p0+p1*xp0=0.337±0.007p1=(1.8±0.9)·10-3

p0=0.30±0.01p1=(3.4±1.0)·10-3

p0=0.340±0.016p1=(1.9±1.6)·10-3

vs Bs pt

Bs pt

Cat 1 Cat 2

Cat 3 Cat 4

Cat 5

vs Bs pt

Cat 1 Cat 2

Cat 3 Cat 4

Cat 5

after L0OS + SS taggers

Corrections might be needed when splitting into 5 categories (Nnet approach)

15

BsJ/ BsDs BsDs

OS taggers SS tagger

cat1 cat1cat2 cat2

cat3 cat3cat4 cat4

cat5 cat5

all taggers1/6 of events include SS kaon

(BsJ/ )

16

Nearly flat!

(OS+SS) of each category compatible forBsJ/ and BsDs

vs Bs pt

PID APPROACH OS , e, k, Qvtx from B+J/ K+ (directly)

B0J/ K* (from oscillation)

SS k from BsDs (double tagging)

BsDs (from oscillation)

41 possible combinations, sort into 5 categories according to its :eff ~ 4.9%

17

Corrections needed also for:

Cat

4Cat

5 Cat

3 Cat

1Cat

2

PID combination

Nnet:

tag eff

9.2±0.1 29.0±0.6 1.6±0.1e 2.9±0.1 30.6±1.1 0.43±0.05

kopp 15.0±0.1 31.6±0.5 2.0±0.1kss 26.0±0.2 30.9±0.4 3.8±0.2

Qvrtx 39.5±0.2 40.0±0.3 1.6±0.1Combined 59.8±0.6 31.0±0.2 8.7±0.2

After L0

BsDs

tag eff

5.40±0.07 29.8±0.6 0.88±0.06e 2.73±0.05 30.5±0.9 0.42±0.04

kopp 13.83±0.11 33.5±0.4 1.51±0.08kss 23.27±0.14 32.0±0.3 2.70±0.10

Qvrtx 36.98±0.16 41.0±0.3 1.20±0.07Combined 55.4±0.7 33.0±0.3 6.4±0.2

BsJ/

Nnet: 18

(BsDs numbers in the backup)

Before L0 After L0

L0 TIS L0 TOS

(TIS: remaining differences due to offline selection)

Bs pt (GeV)

Bs pt (GeV)

TISTOSTOBBING L0

19

L0 TIS (%) L0 TOS (%)BsJ/ 23.5 74.3BsDs 36.8 61.0BsDs 55.1 44.2

(TIS includes TIS+TOS)

Nnet:

BsDs

tag eff tag eff

16.55±0.25

28.1±0.8

3.16±0.22

1.95±0.05 33.8±1.2 0.02±0.03

e 3.92±0.13 31.1±1.6

0.56±0.10

2.38±0.06 30.2±1.1 0.37±0.04

kopp 17.44±0.26

32.1±0.8

2.25±0.19

12.70±0.13

33.9±0.5 1.31±0.08

kss 26.4±0.3 33.6±0.6

2.93±0.22

22.37±0.06

32.9±0.4 2.63±0.12

Qvrtx 44.5±0.3 38.7±0.5

2.26±0.20

34.64±0.18

41.8±0.3 0.92±0.07

Combined 65.8±0.3 30.6±0.4

9.9±0.4 52.4±0.2 34.0±0.3 5.4±0.2

BsJ/

Nnet: 20

TIS (23.5%) TOS (74.3%)

tag eff tag eff

15.6±0.20

28.8±0.6 2.80±0.17

1.69±0.08

31.3±2.1 0.23±0.06

e 3.68±0.10

31.4±1.3 0.51±0.07

2.13±0.09

33.6±2.0 0.23±0.06

kopp 16.97±0.20

31.3±0.6 2.37±0.16

12.2±0.2 33.3±0.8 1.31±0.20

kss 27.01±0.24

32.0±0.5 3.52±0.19

25.2±0.3 29.4±0.5 4.42±0.23

Qvrtx 44.27±0.27

39.2±0.4 2.07±0.15

32.6±0.3 42.3±0.5 0.78±0.11

Combined

65.8±0.9 30.6±0.3 9.94±0.24

52.3±0.9 31.6±0.4 7.07±0.28

TIS (55.1%) TOS (44.2%)

CONCLUSIONSSame Side Kaon can be used in combination with Opposite Side taggers to get an event-per-event probability of mistag.

• From Nnet output calibrated with control channels.• eff~6.3% (3.4% without SS)

Qvtx main source of OS tagger correlations.• Get rid of correlations by measuring global OS

wrong tag fraction from control channel (5 categories).

• Actually without it eff~6%. • Needs to be studied further.

Trigger effects matters to get a ‘global’ , but not as event-per-event weight.

21

BACKUP22

23

tag OS OS eff OS tag OS+SS OS+SS eff OS+SSCat 1 27.88±0.18 44.2±0.4 0.38±0.05 27.81±0.18 43.1±0.4 0.53±0.06Cat 2 7.93±0.11 34.4±0.7 0.77±0.07 10.77±0.12 33.3±0.6 1.21±0.08Cat 3 5.95±0.09 28.9±0.7 1.06±0.07 8.22±0.11 27.3±0.6 1.70±0.10Cat 4 4.33±0.08 25.0±0.8 1.09±0.07 6.18±0.10 23.7±0.7 1.70±0.09Cat 5 3.11±0.07 18.4±0.9 1.24±0.07 6.83±0.10 14.0±0.5 3.54±0.12

average 49.2±0.2 37.4±0.3 3.10±0.14 59.8±0.2 33.8±0.2 6.25±0.19combine

d49.2±0.2 34.8±0.3 4.53±0.15 59.8±0.2 31.0±0.2 8.67±0.20

BsDs

tag OS OS eff OS tag OS+SS OS+SS eff OS+SSCat 1 27.18±0.15 44.9±0.3 0.28±0.04 27.91±0.15 43.4±0.3 0.49±0.05Cat 2 6.97±0.08 35.5±0.6 0.59±0.05 10.13±0.10 35.4±0.5 0.86±0.06Cat 3 5.02±0.07 30.8±0.7 0.74±0.05 7.41±0.09 29.5±0.6 1.24±0.07Cat 4 3.68±0.06 26.0±0.8 0.84±0.06 5.33±0.07 24.3±0.6 1.41±0.07Cat 5 2.13±0.05 16.7±0.8 0.95±0.05 4.73±0.07 14.4±0.5 2.40±0.08

average 45.0±0.2 39.0±0.2 2.19±0.10 55.5±0.2 35.8±0.2 4.50±0.13combine

d45.0±0.2 36.2±0.2 3.40±0.11 55.5±0.2 33.0±0.2 6.41±0.15

BsJ/

OS kaon

TOS

SS kaon

B pt

Bs2JpsiPhiBs2DsPiBs2DsMuNu

TOS

TISTIS

p0=(34.3±0.9)·10-2

p1=(1.6±1.1)·10-3

p0= (31.9±2.0)·10-2

p1=(2.6±1.8)·10-3

p0= (33.7±2.3)·10-2

p1=(2.6±2.2)·10-3

p0=(31.0±1.4)·10-2

p1=(2.9±1.4)·10-3

p0= (29.3±1.2)·10-2

p1=(3.1±1.1)·10-3

p0= (33.3±2.4)·10-2

p1=(1.8±2.2)·10-3

p0=(40.6±0.8)·10-2

p1=(-7.7±0.8)·10-3

p0= (40.8±1.4)·10-2

p1=(-9.7±1.2)·10-3

p0= (39.0±1.7)·10-2

p1=(-7.4±1.5)·10-3

p0=(42.7±1.2)·10-2

p1=(-8.2±1.2)·10-3

p0= (42.9±1.0)·10-2

p1=(-9.4±0.9)·10-3

p0= (43.9±1.9)·10-2

p1=(-11.0±1.7)·10-3

B pt

B pt B pt

Not expected to be different between TIS and TOS (just a check)

24

TOS

TIS

SS kaon tagger

Fit p0+p1*xp0=(0.9±1.1)·10-2

p1=0.96±0.03p0= (-1.8±1.3)·10-2

p1=0.98±0.04p0= (0.8±2.0)·10-2

p1=0.90±0.06

BsJ/ BsDs BsDs

Fit p0+p1*xp0=(-0.1±1.7)·10-2

p1=0.98±0.06p0= (-0.6±1.2)·10-2

p1=0.97±0.03p0= (0.3±2.4)·10-2

p1=0.91±0.07

Not expected to be different between TIS and TOS (just a check)

NNet

NNet25

26

tag eff

6.85±0.17 32.0±1.2 0.89±0.12e 2.95±0.12 32.2±1.9 0.38±0.08

kopp 15.5±0.2 33.6±0.8 1.67±0.17kss 25.6±0.3 31.2±0.6 3.6±0.2

Qvrtx 37.9±0.3 40.2±0.5 1.44±0.16Combined 58.1±1.1 31.8±0.4 7.7±0.3

BsDs

Nnet:

Double tagging method:OS

OSSS

F

21

1

F = probability that taggers agree = Nagree/NDT

(from S. Poss)

25 ))(·(10·3783.0)(·1296.03388.0)()(

sss

trues

rec

DMDMBpDp

BSDS MOMENTUM CORRECTION

27

SAME SIDE KAON NEURAL NET OUTPUT CALIBRATIONFrom BsDs control channel28

Jeremie Borel / Vladimir Gligorov (DaVinci v19r12 / v22r0p2)Yield ~ 146/183k (after L0) DC06 selected 113/134k (~39/37k with SSK tagger); 50/63k after L0

Backgrounds:

B/S~0.34/0.41

Jeremie B/S (m=±50 MeV &

L0)

Vava B/S (m=± 50 MeV &

L0)BsDsX 0.02 0.02BdD 0.20 0.11

bDsp 0.04 0.13bc 0.02 0.01

bb 0.07 0.14

_

29

BsDs BsDs XBdD bDs pbc

m (GeV/c2) m (GeV/c2)

SignalBkg

BSDS OFFLINE SELECTION

29

SIGNAL:

BACKGROUND:

PDFS USED IN ROOFIT

RooHistPdfnnetPdf

ttGtmnnetppttExptAnnettPdf

MmGfMmGfmPdf

sig

sigts

ss

sig

mBmmBmsig

)(

);()]'·cos())·10·(21()'2

)[cosh(()·()|(

),;()1(),;()( 21

RooHistPdfnnetPdf

ttGtExptAtPdf

mExpmPdf

bkg

bkgbkgt

bkgbkg

bkg

)(

))21(1();();()·()(

);()(

RooBDecaymixState = unmixed if charge() = tagger_decision mixed if opposite

3

3

·1·)(tatatA

acceptance:

s, s, ms fixed

(bkg=0.5)

sig

RooDecay

30

FIT RESULTS

31

Nnet

SS k

RooFit p0 = (-2±5)·10-3

p1 =0.96 ±0.01

Fit: p0+p1*xp0 = (-6±7)·10-3

p1 = 0.99±0.02

Floating Parameter FinalValue +/- Error -------------------- -------------------------- B/S 3.2075e-01 +/- 2.93e-03 a 7.1132e+00 +/- 1.90e-01 bkg_S1 1.8975e-02 +/- 6.73e-02 bkg_m1 -1.9603e-01 +/- 2.44e-01 bkg_mexp -3.7567e-01 +/- 1.37e-01 bkg_tau 6.7124e-01 +/- 5.08e-03 fr1 3.4824e-01 +/- 6.33e-03 mean 3.8342e-03 +/- 7.58e-04 omegaB 5.1069e-01 +/- 3.23e-03 par0 -2.1409e-03 +/- 5.11e-03 par1 9.6138e-01 +/- 1.31e-02 sig_mass_mB 5.3683e+00 +/- 5.82e-05 sig_mass_sB 1.1764e-02 +/- 1.00e-04 sig_mass_sB2 1.9794e-02 +/- 1.58e-04 sigma 4.3791e-02 +/- 8.62e-04

OFFLINE SELECTION COMPARISONBsDs32

Jeremie Vava

All pions and kaons

IPS > 3 MIPCHI2DV(PRIMARY) > 9

P > 2 GeV P > 2 GeVDLL(pi-p) > -5

DLL(pi-mu) > -5DLL(k-p) > -10

DLL(k-mu) > -10

Ds daughtersPt > 300 MeV Pt > 300 MeV

DLL(K-pi) > 2Kaons : DLL K/p > -10

tight

Bachelor pionPt > 600 MeV Pt > 500 MeV

DLL(K-pi) > 4 loose

Ds

Pt > 2 GeV Pt > 2 GeV

IPS > 4 MIPCHI2DV(PRIMARY)>9

vtx chi2 < 16 2 < 15

BPVVDCHI2 > 100

M< 21 MeV mass window ± 21/50 MeV

Bs

z_Ds – z_Bs > 0 mm

IPS < 4 BPVIPCHI2() < 16

vtx chi2 < 9 2 < 10

M < 500 MeV (70MeV) Mass window ± 50/500 MeV

FS > 12 BPVVDCHI2 > 6.25

cos(p) > 0.99997 cos() > 0.9999

3

33

N_tape (kevts)

after stripping N_sel N_sel

(L0) B/S

Bs2DsPi 2085 445550112831 50206 Y = 146k

133540 62853 Y = 183k

bbar(not phys.)

22000 8028501 (16) 1 (9) 0.07

7(62) 3(32) 0.16

Bs2DsX 2017 148554124 61 0.02

155 78 0.02

Bd2DPi 787 1544061291 661 0.2

831 441 0.11

LbDsP 41 8025104 39 0.04

484 183 0.13

Lb2LcPi 37 422614 7 0.02

12 5 0.01

m = ± 50 / 50 MeV

B/S ~ 0.34 / 0.43

+25%

+25%

Jeremie / Vava

(Increase of bkg because loose PID cuts for the bachelor pion)

34

(HLT eff missing)

B mass B p

B pt B

BsDs offline selected events

VavaJeremie

35

Bkg Bs mass

VavaJeremie

Bs2DsX Bd2DPi

LbDsP Lb2LcPi

bb-bar

36