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B0 → η′(→ ηπ+π−)K0S Time Dependent ��CP analysis
B2TIP practice talk(plus other minor stuff not for B2TIP)
Stefano [email protected]
INFN Padova
WG3 meeting,virtual, 18 May 2016
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 1 / 23
Introduction and motivations
A sensitivity study for Time-Dependent CP violation analysis in theB0 → η′K0channel, a charmless b → sqq decay
CP asymmetry from time-dependent decay rateinto CP eigenstates;
Sη′K
0 = sin 2φeff1 tightly related to sin 2φ1
I identical if only penguin diagram were present;I QCD factorization:
∆Sη′K 0 ∈ [−0.03, 0.03][Williamson and Zupan(2006)]
I SU(3)F approach:∆Sη′K 0 ∈ [−0.05, 0.09][Gronau et al.(2006)]
I new physics can enter in the loop, shifting ∆Sη′K 0
more than SM expectation
B0
η′
K0
b
d
s
s
s
g
W
u, c, t
η′ K0 S
CP
HF
AG
Mo
rio
nd
20
14
0.5 0.6 0.7 0.8
BaBar
PRD 79 (2009) 052003
0.57 ± 0.08 ± 0.02
Belle
JHEP 1410 (2014) 165
0.68 ± 0.07 ± 0.03
Average
HFAG correlated average
0.63 ± 0.06
H F A GH F A GMoriond 2014
PRELIMINARY
actual uncertainties σstat = 0.07, σsyst = 0.03[Belle(2014)]
projected for 50 ab−1 σstat = 0.008, σsyst = 0.008[Urquijo(2015)]
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 2 / 23
Current results
Channel have been analyzed in B-factory[BABAR(2009), Belle(2007), Belle(2014)];I large BR: BR ∼ 6.6 · 10−5 [CLEO(1998)]
constructive interference between penguin diagrams
analysis based on quasi-two body approach;uncertainties are mostly statistical (∼ 3500 events for all final states);I sin 2φeff
1 = +0.68± 0.07± 0.03I syst: ±0.025 from ∆t resolution, ±0.014 from vertexing, ±0.013 from η′K0
S
fraction;
many decay channels available B0 → η′K0S
decay channel
η′ → ρ0(→ π+π−)γ BR=29% not yet
η′ → ηπ+π− 43% today
↘ ηγγ η → γγ 40%
↘ η3π η → π+π−π0 23%
B0 → η′(→ηγγ /η3ππ+π−
)K0BR=27%
K 0S → π+π− today
K 0S → π0π0
just started
K0L not yet
Complex final state, neutrals, large combinatorics;
no competition from LHCb (neutrals);
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 3 / 23
Selection
candidate selection: main cuts
Reconstruct decay chain with mass constrains for π0, η, η′, K0S,
I vertex only (w/o mass) for B0 (more later)
� π0, ηγγ :
I 0.06 < Eγ < 6 GeV, E9/E25 > 0.75
I M(π0) ∈ [100, 150] MeV
I M(ηγγ) ∈ [0.52, 0.57] GeV;
� η′ → ηγγπ+π−:
I d0(π±) < 0.08mm;z0(π±) < 0.1mm;
I N hitsPXD (π±) > 1, PID
I M(η′) ∈ [0.93, 0.98] GeV;
� η′ → η3ππ+π−:
I M(η′) ∈ [0.93, 0.98] GeV;
� K0 → π+π−:
I M(K0S → π+π−) ∈ [0.48, 0.52] GeV;
� B0 → η′(→ ηγγπ+ π−)K0
S+−
I Mbc > 5.25 GeV;
I |∆E | < 0.1 GeV;
� B0 → η′(→ η3ππ+π−)K0
S+−
I |∆E | < 0.15 GeV;
if Ncands > 1, select that with best reduced χ2 for η, η′,K0S inv. masses
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 4 / 23
Signal distribution B0 → η′(→ ηγγπ+ π−)K0
S
+−
Best candidates, after selections
bcM5.25 5.255 5.26 5.265 5.27 5.275 5.28 5.285 5.290
5000
10000
15000
20000
25000
30000
35000
Mbc
Best cands
" MC match
" SXF
Mbc
E∆0.2− 0.15− 0.1− 0.05− 0 0.05 0.1 0.15 0.20
5000
10000
15000
20000
25000
30000
35000
40000
ηM0.4 0.45 0.5 0.55 0.6 0.65 0.70
10000
20000
30000
40000
50000
'ηM0.85 0.9 0.95 1 1.05 1.1 1.150
20
40
60
80
100
120
140
160
180
200
310×
S0KM
0.45 0.46 0.47 0.48 0.49 0.5 0.51 0.52 0.53 0.54 0.551
10
210
310
410
510
/KπLL∆20− 10− 0 10 20 30 40 501
10
210
310
410
)-π+π(S
0) K-π+π γγη'( η→0B
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 5 / 23
Signal distribution B0 → η′(→ η3ππ+π−)K0
S
+−
Best candidates, after selections
bcM5.25 5.255 5.26 5.265 5.27 5.275 5.28 5.285 5.291
10
210
310
410
Mbc
Best cands
" MC match
" SXF
Mbc
E∆0.2− 0.15− 0.1− 0.05− 0 0.05 0.1 0.15 0.21
10
210
310
410
ηM0.4 0.45 0.5 0.55 0.6 0.65 0.71
10
210
310
410
510
'ηM0.85 0.9 0.95 1 1.05 1.1 1.151
10
210
310
410
510
0πM0.08 0.1 0.12 0.14 0.16 0.18 0.21
10
210
310
410
/KπLL∆20− 10− 0 10 20 30 40 501
10
210
310
)-π+π(S
0) K-π+π π3
η'( η→0B
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 6 / 23
Efficiency and combinatorics
Final state with K0S → π+π−
B0 → η′(→ ηγγπ+ π−)K0
S+−
Efficiency %MC true 29.4SXF 1.1
1.06 good cands per event
B0 → η′(→ η3ππ+π−)K0
S+−
Efficiency %MC true 12.1SXF 3.1
1.45 good cands per eventEff drop due to π0 reco, likely to improve
Final state with K0S → π0π0
B0 → η′(→ ηγγπ+ π−)K0
S00
Efficiency %MC true 13.5SXF 2.2
5 good cands per event
B0 → η′(→ η3ππ+π−)K0
S00
Efficiency %MC true 6.0SXF 3.8
30 good cands per eventnot used in BaBar/Belle
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 7 / 23
Vtx reco and ∆t resolution: ηγγchannel
1 Fit the B0 vertex from charged tracks; (π± from η′ → ηπ±, η → π±π0)
2 add also constraint from reconstructed K 0S direction; (K0
S → π+π−)
3 add also constraint from beamspot, only on transferse plane.
(ps)truet∆t-∆10− 8− 6− 4− 2− 0 2 4 6 8 10
0
5000
10000
15000
20000
25000
/ ndf 2χ 867 / 191
Prob 0
norm 8.0e+01± 6.3e+04
CBias 0.0025±0.0307 −
Cσ 0.005± 0.629
T
Bias 0.00485±0.00735 − Tσ 0.01± 1.63
OBias 0.015± 0.132
Oσ 0.03± 4.46
Cf 0.005± 0.344
Tf 0.003± 0.443
Fit
Core
Tail
Outlier
t: 1.89 ps∆Bias: 0.01 ps
)-π+π(S
0) K-π+π γγ
η'( η→0B
Standard
(ps)truet∆t-∆10− 8− 6− 4− 2− 0 2 4 6 8 10
0
100
200
300
400
500
600
700
/ ndf 2χ 253 / 191
Prob 0.00177
norm 1.24e+01± 1.54e+03
CBias 0.013±0.047 −
Cσ 0.033± 0.587
T
Bias 0.0313±0.0524 −
T
σ 0.12± 1.47
OBias 0.080± 0.107
Oσ 0.15± 3.81
Cf 0.041± 0.393
Tf 0.028± 0.393
Fit
Core
Tail
Outlier
t: 1.62 ps∆Bias: -0.02 ps
)-π+π(S
0) K-π+π γγ
η'( η→0B
WithK0S
(ps)truet∆t-∆10− 8− 6− 4− 2− 0 2 4 6 8 10
0
5000
10000
15000
20000
25000
30000
35000
40000
/ ndf 2χ 1.02e+03 / 191
Prob 0
norm 7.8e+01± 6.1e+04
CBias 0.0013±0.0399 −
Cσ 0.002± 0.488
T
Bias 0.0036±0.0704 − Tσ 0.01± 1.14
OBias 0.018± 0.429
Oσ 0.02± 2.97
Cf 0.005± 0.565
Tf 0.004± 0.362
Fit
Core
Tail
Outlier
t: 0.91 ps∆Bias: -0.02 ps
)-π+π(S
0) K-π+π γγ
η'( η→0B
WithBS &
K0S
With beamspot (x , y) & K0S:
No efficiency losssubstaintial improvement in ∆tresolution1.89→ 1.62→ 0.91 ps
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 8 / 23
Vtx reconstruction for B0 → η′(→ η3ππ+π−)K0
S+−
(ps)truet∆t-∆10− 8− 6− 4− 2− 0 2 4 6 8 10
0
2000
4000
6000
8000
10000
12000
14000
16000
/ ndf 2χ 499 / 191
Prob 29− 2.06e
norm 5.65e+01± 3.19e+04
CBias 0.0027±0.0223 −
Cσ 0.005± 0.535
T
Bias 0.0052±0.0277 −
T
σ 0.01± 1.29
OBias 0.019± 0.266
Oσ 0.03± 3.15
Cf 0.007± 0.401
Tf 0.01± 0.46
Fit
Core
Tail
Outlier
t: 1.25 ps∆Bias: 0.02 ps
)-π+π(S
0) K-π+π π3
η'( η→0B
Standard
(ps)truet∆t-∆10− 8− 6− 4− 2− 0 2 4 6 8 10
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
/ ndf 2χ 629 / 191
Prob 0
norm 5.49e+01± 3.02e+04
CBias 0.002±0.036 −
Cσ 0.003± 0.445
T
Bias 0.0050±0.0562 −
T
σ 0.01± 1.07
OBias 0.021± 0.317
Oσ 0.02± 2.88
Cf 0.007± 0.565
Tf 0.006± 0.342
Fit
Core
Tail
Outlier
t: 0.88 ps∆Bias: -0.01 ps
)-π+π(S
0) K-π+π π3
η'( η→0B
WithBS &
K0S
With beamspot (x , y) & K0S:
No efficiency loss1.25→ 0.88 ps
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 9 / 23
Comparison with Belle
Comparison with Belle
Not been able to find ∆t resolutions numbers for Belle, only RMS
Belle has 4 different events categories, depending on vtx qualityreconstruction
ηγγBelle RMS: 1.57 (/2.67/5.16/4.52)
BelleII (this study) RMS: 1.12
η3π
Belle RMS: 1.2 (1.22/3.18/5.9)BelleII (this study): RMS: 1.10
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 10 / 23
Backgrounds
Combinatorial: from continuum background e+e− → uu, dd , ss, ccI evaluated from Mbc side bands on real dataI now from MC productionI use Continuum Suppression variable
F multivariate variables sensitive to event topologyF central (signal) vs jet-like (continuum)
Peaking: any other B decays possibly with real η′ and/or K0S
I evaluated from MC of generic B0B0, B+B−
F actual B0 → η′K0 removed.
Current results based on BGx0 production, namely w/o machinebackgroundI impact of machine background under study
(almost) solved skimming problem for mixed and chargedI harder cut on skim selectionsI now L ≈ 800 fb−1 for all sources (was ≈ 200 fb−1 for mixed/charged)
Next table numbers before Continuum Suppression cut
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 11 / 23
Background reduction (before CS cut)
Sample uu dd ss cc contiuum B0B0 B+B−
Input ev (M) 1284 321 306 1063 2974 429 420
B0 → η′(→ ηγγπ+ π−)K0
S
+−
εsel · 10−6 2.69 3.06 2.40 3.62 3.0 0.13 0.043
ev for 300 fb−1 1247 369 275 1445 3335 22 7
B0 → η′(→ η3ππ+π−)K0
S
+−
εsel · 10−6 0.34 0.54 0.17 1.50 0.76 0.14 0.02
ev for 300 fb−1 166 65 20 597 847 24 3
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 12 / 23
Background distributionsBest candidates, after selections
5.25 5.255 5.26 5.265 5.27 5.275 5.28 5.285 5.29
20
40
60
80
100
120
bcM
mixedcharged
uuddss
cc
0.2− 0.15− 0.1− 0.05− 0 0.05 0.1 0.15 0.2
20
40
60
80
100
120
140
160
180
200
E∆0.4 0.45 0.5 0.55 0.6 0.65 0.7
50
100
150
200
250
300
350
400
ηM
0.85 0.9 0.95 1 1.05 1.1 1.15
200
400
600
800
1000
1200
1400
1600
'ηM0.45 0.46 0.47 0.48 0.49 0.5 0.51 0.52 0.53 0.54 0.55
100
200
300
400
500
600
S0KM
20− 10− 0 10 20 30 40 50
1
10
210
/KπLL∆
)-π+π(S
0) K-π+π) γγ(η'( η→0B
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 13 / 23
Background distributionsBest candidates, after selections
5.25 5.255 5.26 5.265 5.27 5.275 5.28 5.285 5.29
5
10
15
20
25
30
35
40
bcM
mixedcharged
uuddsscc
0.2− 0.15− 0.1− 0.05− 0 0.05 0.1 0.15 0.2
5
10
15
20
25
30
35
40
45
E∆0.4 0.45 0.5 0.55 0.6 0.65 0.7
100
200
300
400
500
ηM
0.85 0.9 0.95 1 1.05 1.1 1.15
100
200
300
400
500
600
700
'ηM0.08 0.1 0.12 0.14 0.16 0.18 0.2
20
40
60
80
100
120
140
160
0πM0.45 0.46 0.47 0.48 0.49 0.5 0.51 0.52 0.53 0.54 0.55
20
40
60
80
100
120
140
160
180
S0K
M
)-π+π(S
0) K-π+π) 0π-π+π(η'( η→0B
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 14 / 23
Continuum suppression
0.5− 0.4− 0.3− 0.2− 0.1− 0 0.1 0.2 0.3 0.4 0.5
50
100
150
200
250
300
350
BDTBDT
mixedcharged
uuddsscc
Signal
)-π+π(S
0) K-π+π) γγ(η'( η→0B
Signal efficiency
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Ba
ck
gro
un
d r
eje
cti
on
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
MVA Method:
BDT
Background rejection versus Signal efficiency
Working points
Tight: BDT > 0.124, εsignal = 50%, (1− εbackground ) = 97.5%,
Loose: BDT > −0.055, εsignal = 95%, (1− εbackground ) = 58%,
no cut: just include the BDT in the likelihood not yet
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 15 / 23
Likelihood fit
Multi dim. extended maximum likelihood fit to extract S and C.
Pdf is of the form:P i
j = Tj
(∆t i , σi
∆t , ηiCP
)︸ ︷︷ ︸
time-dep part
∏k Qk,j (x
ik )︸ ︷︷ ︸
time integrated
time-dependent part, taking into account mistag rate (ηf = ±1 is CP state):
f (∆t) =e−|∆t|/τ
4τ
{1∓∆w ± (1− 2w)
×[− ηf Sf sin(∆m∆t)− Cf cos(∆m∆t)
]}variables (xk ) used, in addition to ∆t
Mbc
∆E
Mη′ removed, not useful
Cont. Suppr. not yet
Parameters:
effective tagging efficiency:Q = ε(1− 2w)2 = 0.33
w = 0.21, ∆w = 0.02
∆t resolution as shown previouslyconvoluted.
τ , ∆m from PDGS.Lacaprara (INFN Padova) B
0 → η′K
0S WG3 18/05/2016 16 / 23
PDF fit results examples
t (ps)∆25− 20− 15− 10− 5− 0 5 10 15 20 25
Eve
nts
/ ( 1
ps
)
0
10000
20000
30000
40000
50000
t"∆A RooPlot of " / ndf = 224.5182χ
0.0014± = -2.51264 CPC
0.0039± = -0.06275 CPS
t"∆A RooPlot of "
(GeV)bcM5.25 5.255 5.26 5.265 5.27 5.275 5.28 5.285 5.29
Eve
nts
/ ( 0
.001
GeV
)
0
5000
10000
15000
20000
25000
30000
35000
"bcA RooPlot of "M / ndf = 5.2122χ
0.015± = 0.949 Cf 0.000025 GeV± = 5.279630
Cµ
0.00027 GeV± = 5.27733 T
µ 0.000012 GeV± = 0.002488 Cσ 0.000089 GeV± = 0.003056 Tσ
"bcA RooPlot of "M
E (GeV)∆0.1− 0.08− 0.06− 0.04− 0.02− 0 0.02 0.04 0.06 0.08 0.1
Eve
nts
/ ( 0
.005
GeV
)
0
5000
10000
15000
20000
25000
30000
35000
E"∆A RooPlot of " / ndf = 42.9652χ
0.0029± = 0.8108 Cf 0.000034 GeV± = -0.0072407
Cµ
0.00019 GeV± = -0.015109 T
µ 0.000039 GeV± = 0.011661 Cσ
0.00021 GeV± = 0.03224 Tσ
E"∆A RooPlot of "
Signal
t (ps)∆25− 20− 15− 10− 5− 0 5 10 15 20 25
Eve
nts
/ ( 1
ps
)
0
10000
20000
30000
40000
50000
t"∆A RooPlot of " / ndf = 224.5182χ
0.0014± = -2.51264 CPC
0.0039± = -0.06275 CPS
t"∆A RooPlot of "
(GeV)bcM5.25 5.255 5.26 5.265 5.27 5.275 5.28 5.285 5.29
Eve
nts
/ ( 0
.001
GeV
)
0
1000
2000
3000
4000
5000
6000
7000
"bcA RooPlot of "M / ndf = 57.1582χ
0.000050± = 1.000000 Cf 0.000015 GeV± = 5.279280
Cµ
0.026 GeV± = 5.289 T
µ 0.000012 GeV± = 0.003023 Cσ
0.052 GeV± = 0.009 Tσ
0.27± = -90.000 ξ 0.035±n = 0.545
0.0017± = 0.9477 Pf
"bcA RooPlot of "M
E (GeV)∆0.1− 0.08− 0.06− 0.04− 0.02− 0 0.02 0.04 0.06 0.08 0.1
Eve
nts
/ ( 0
.005
GeV
)
0
500
1000
1500
2000
2500
3000
3500
4000
E"∆A RooPlot of " / ndf = 2.3942χ
0.0060± = 0.4112 Cf 0.00014 GeV± = -0.008252
Cµ
0.00060 GeV± = -0.010825 T
µ 0.00018 GeV± = 0.01380 Cσ
0.0011 GeV± = 0.0687 Tσ
E"∆A RooPlot of "
SxF
t (ps)∆25− 20− 15− 10− 5− 0 5 10 15 20 25
Eve
nts
/ ( 1
ps
)
0
2
4
6
8
10
12
14
16
t"∆A RooPlot of " / ndf = 0.4702χ
0.32± = -0.015 CPC
0.66± = 0.76 CPS
t"∆A RooPlot of "
(GeV)bcM5.25 5.255 5.26 5.265 5.27 5.275 5.28 5.285 5.29
Eve
nts
/ ( 0
.001
GeV
)
0
2
4
6
8
10
12
14
16
"bcA RooPlot of "M / ndf = 1.0732χ
0.00051 GeV± = 5.27876 µ 0.00044 GeV± = 0.00282 σ
87± = -90.0 ξ 0.59±n = 0.37
0.083± = 0.862 Pf
"bcA RooPlot of "M
E (GeV)∆0.1− 0.08− 0.06− 0.04− 0.02− 0 0.02 0.04 0.06 0.08 0.1
Eve
nts
/ ( 0
.005
GeV
)
0
1
2
3
4
5
6
7
8
E"∆A RooPlot of " / ndf = 0.4342χ
2.0± = -2.68 1
p
23± = -61.8 2
p
E"∆A RooPlot of "
Peaki
ngbkg
nd
t (ps)∆25− 20− 15− 10− 5− 0 5 10 15 20 25
Eve
nts
/ ( 1
ps
)
0
500
1000
1500
2000
2500
t"∆A RooPlot of " / ndf = 1.6972χ
0.018 ps± = 0.014 CBias
0.069 ps± = 0.512 TBias
0.0078± = 0.5000 Cf
0.0074± = 0.1406 Of
0.023 ps± = 0.539 CScale
0.074 ps± = 2.745 TScale
t"∆A RooPlot of "
(GeV)bcM5.25 5.255 5.26 5.265 5.27 5.275 5.28 5.285 5.29
Eve
nts
/ ( 0
.001
GeV
)
0
50
100
150
200
250
300
350
"bcA RooPlot of "M / ndf = 0.9942χ
2.9± = -29.23 ξ 0.000031 GeV± = 5.286930 endE
"bcA RooPlot of "M
E (GeV)∆0.1− 0.08− 0.06− 0.04− 0.02− 0 0.02 0.04 0.06 0.08 0.1
Eve
nts
/ ( 0
.005
GeV
)
0
50
100
150
200
250
E"∆A RooPlot of " / ndf = 0.8442χ
0.19± = -1.107 1
p
3.7± = 2.3 2
p
E"∆A RooPlot of "
Contin
uum
∆T Mbc ∆EStill problem to fit the continuum suppression variable: very spiky
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 17 / 23
Toy MC
Testing fit machinery with Toy MC;
Yield estimated for L = 300 fb−1
width of distribution related to the expected statistical uncertainty;
check also for bias (RMS/√Ntoys)
Toys now are converging, fixed problem with ∆t resolution
input CP asymmetry parameter: S=0.7 C=0.0
testing two different CS scenarios:I TightI Loose
Partially embedded toysI Signal and SXF from MC;I Continuum and Peaking background from pdf;
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 18 / 23
Toy results B0 → η′(→ ηγγπ+ π−)K0
S+−
Tight CS
Tight: εsig ,sxf ,peaking = 50%, εcontinuum = 2.5%
Nsig = 195, Nsxf = 8, Ncont = 83, Npeak = 15
0.2− 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.60
10
20
30
40
50
60
70
Toy results - dtSig_S Entries 1000
Mean 0.0079± 0.703
Std Dev 0.00559± 0.25
Toy results - dtSig_S
0.6− 0.4− 0.2− 0 0.2 0.4 0.60
10
20
30
40
50
60
70
80
Toy results - dtSig_C Entries 1000
Mean 0.00539± 0.0103
Std Dev 0.00381± 0.17
Toy results - dtSig_C
0 50 100 150 200 250 300 350 4000
20
40
60
80
100
120
140
Toy results - nSig Entries 1000
Mean 0.403± 195
Std Dev 0.285± 12.7
Toy results - nSig
Par Bias RMS
S (0.7) 0.703± 0.008 0.25C (0.0) 0.010± 0.005 0.17nSig 195.4± 0.4 12.7
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 19 / 23
Toy results B0 → η′(→ ηγγπ+ π−)K0
S+−
Loose CS
Loose: εsig ,sxf ,peaking = 95%, εcontinuum = 42%
Nsig = 390, Nsxf = 14, Ncont = 1400, Npeak = 28
0.2− 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.60
10
20
30
40
50
60
70
80
Toy results - dtSig_S Entries 1000
Mean 0.00572± 0.703
Std Dev 0.00405± 0.181
Toy results - dtSig_S
0.6− 0.4− 0.2− 0 0.2 0.4 0.60
10
20
30
40
50
60
70
80
90
Toy results - dtSig_C Entries 1000
Mean 0.00441± 0.00198
Std Dev 0.00312± 0.139
Toy results - dtSig_C
0 100 200 300 400 500 600 700 8000
20
40
60
80
100
120
Toy results - nSig Entries 1000
Mean 0.804± 390
Std Dev 0.568± 25.4
Toy results - nSig
Par Bias RMS
S (0.7) 0.703± 0.005 0.18C (0.0) 0.002± 0.004 0.14nSig 389.8± 0.8 25.4
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 20 / 23
Toy results B0 → η′(→ η3ππ+π−)K0
S+−
Loose CS
Loose: εsig ,sxf ,peaking = 95%, εcontinuum = 42%
Nsig = 106, Nsxf = 25, Ncont = 360, Npeak = 27
0.2− 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.60
10
20
30
40
50
Toy results - dtSig_S Entries 995
Mean 0.0105± 0.708
Std Dev 0.00742± 0.33
Toy results - dtSig_S
0.6− 0.4− 0.2− 0 0.2 0.4 0.60
10
20
30
40
50
Toy results - dtSig_C Entries 995
Mean 0.00785± 0.00127
Std Dev 0.00555± 0.246
Toy results - dtSig_C
0 20 40 60 80 100 120 140 160 180 2000
5
10
15
20
25
30
35
40
45
Toy results - nSig Entries 995
Mean 0.587± 110
Std Dev 0.415± 18.5
Toy results - nSig
Par Bias RMS
S (0.7) 0.708± 0.010 0.330C (0.0) −0.013± 0.008 0.246nSig 110.2± 0.6 18.5
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 21 / 23
Comparison with Belle/BaBar
This analysis? Belle[Belle(2014)] BaBar[BABAR(2009)]
mode (340 M B B) (772 M B B) (467 M B B)
η′ → π±η Nsig σS σC Nsig σS σC Nsig σC σS
ηγγK0S
+−390 0.18 0.14 648 0.15 0.098 472 0.17 0.11
ηγγK0S
00Just started 104 0.21† 0.18† 105 0.34 0.30
η3πK0S
+−106 0.33 0.25 174 0.26 0.18 171 0.26 0.20
η3πK0S
00Will try Not used
η′ → ρ0γK0S
+−Not yet 1411 0.098 0.069 1005 0.12 0.09
η′ → ρ0γK0S
00Not yet 162 0.21† 0.18† 206 0.33 0.26
?Very preliminary estimate based on toy MC, L = 300 fb−1
Warning: no machine background yet†Results combining η′ → π±ηγγ and η′ → ρ0γTODO: toys with Belle statistics for direct comparison
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 22 / 23
Summary
First presentation on sensitivity study for ��CP in B0 → η ′K0S channel;
not complete, yet, but preliminary results are encouraging;I comparison with Belle and BaBar results looks fine;
many thing to do:I complete K0
S → π+π− channels;I study K0
S → π0π0 final states;
I add η′ → ρ0γK0S
+−/K0
S
00channel;
I systematics uncertainties evaluation;I documentationI . . .
More results (and work) for next B2TIP workshop.
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 23 / 23
Additional stuff
Additional or backup slides
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 1 / 16
Selection
good candidate selection B0 → η′(→ ηγγπ+ π−)K0
S+−
:
Reconstruct decay chain with mass constrains for η, η′, K0S,
I vertex only (w/o mass) for B0
� η → γγ :
I gamma:all: 0.06 < Eγ < 6 GeV,−150 < clustime < 0, E9/E25 > 0.75
I M(ηγγ) ∈ [0.52, 0.57] GeV;
� η′ → ηγγπ+π−:
I pi:all
I ∆logL(π,K) > −10; new
I d0(π±) < 0.08mm;
I z0(π±) < 0.1mm;
I N hitsPXD (π±) > 1
I M(η′) ∈ [0.93, 0.98] GeV;
� K0 → π+π−:
I K S0:mdst
I M(K0S → π+π−) ∈ [0.48, 0.52] GeV;
� B0 → η′(→ ηγγπ+ π−)K0
S+−
I Mbc > 5.25 GeV;
I |∆E | < 0.1 GeV;
I P-valuevtx (B0, η′,K 0
S ) > 1 · 10−5
if Ncands > 1, select candidate with highest P-valuevtx (B0, η′, η,K 0
S )
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 2 / 16
Selection breakdonw
1.000
0.570
0.463 0.458
0.4140.392 0.378 0.378 0.376 0.370 0.361
0.3360.305 0.294
0.011
InputSkim Reco bc
M E∆ ηM
'ηM
K_SM
/K)πLogL(
∆0
d0
z N Hits vtxP TRUE
SXF0
0.2
0.4
0.6
0.8
1
Events statistics
Selections MC true /SXF
)-π+π(S
0) K-π+π γγ
η'( η→0BEvents statistics
Combinatorics
Cands mult.: 1.88Good cands mult.: 1.06
Efficiency %skim 57.0preselection 46.1good cands 30.5MC true 29.4SXF 1.1
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 3 / 16
Signal distribution B0 → η′(→ ηγγπ+ π−)K0
S+−
bcM5.25 5.255 5.26 5.265 5.27 5.275 5.28 5.285 5.290
10000
20000
30000
40000
50000
60000
70000
Mbc
All cands
MC match
Good cands
" MC match
Mbc
E∆0.2− 0.15− 0.1− 0.05− 0 0.05 0.1 0.15 0.20
10000
20000
30000
40000
50000
60000
ηM0.4 0.45 0.5 0.55 0.6 0.65 0.70
10000
20000
30000
40000
50000
60000
70000
80000
90000
'ηM0.85 0.9 0.95 1 1.05 1.1 1.150
50
100
150
200
250
310×
S0
KM
0.45 0.46 0.47 0.48 0.49 0.5 0.51 0.52 0.53 0.54 0.551
10
210
310
410
510
/KπLL∆20− 10− 0 10 20 30 40 501
10
210
310
410
)-π+π(S
0) K-π+π γγη'( η→0B
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 4 / 16
Selection
good candidate selection B0 → η′(→ η3ππ+π−)K0
S+−
:
Reconstruct decay chain with mass constrains for η, η′, K0S,
I vertex only (w/o mass) for B0
� π0:
I gamma:all: 0.06 < Eγ < 6 GeV,−150 < clustime < 0, E9/E25 > 0.75
I M(π0) ∈ [100, 150] MeV
� η → π+π−π0:
I pi:all
I ∆logL(π,K) > −10; new
I M(η3π) ∈ [0.52, 0.57] GeV;
I d0(π±) < 0.08mm;
I z0(π±) < 0.1mm;
I N hitsPXD (π±) > 1
� η′ → η3ππ+π−:
I M(η′) ∈ [0.93, 0.98] GeV;
� K0 → π+π−:
I K S0:mdst
I M(K0S → π+π−) ∈ [0.48, 0.52] GeV;
� B0 → η′(→ η3ππ+π−)K0
S+−
I Mbc > 5.25 GeV;
I |∆E | < 0.15 GeV;
I P-valuevtx (B0, η′,K 0
S ) > 1 · 10−5
if Ncands > 1, select candidate with highest P-valuevtx (B0, η′, η,K 0
S )
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 5 / 16
Selection breakdonw
1.000
0.572
0.464
0.398
0.265
0.1870.170 0.164 0.164 0.163 0.162 0.161 0.158 0.151
0.121
0.030
InputSkim Reco bc
M E∆ ηM
'ηM
0πM
K_SM
/K)πLogL(
∆0
d0
z N Hits vtxP TRUE
SXF0
0.2
0.4
0.6
0.8
1
Events statistics
Selections MC true /SXF
)-π+π(S
0) K-π+π π3
η'( η→0BEvents statistics
Combinatorics
Cands mult.: 21.5Good cands mult.: 1.45
Efficiency %skim 57.2preselection 46.2good cands 15.1MC true 12.1SXF 3.0
Reco eff is as good as ηγγ channel.
50% eff drop due to poor resolution on Mbc , ∆E , Mη all coming from π0 reconstruction
in η → π+π−π0 decay
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 6 / 16
Signal distribution B0 → η′(→ η3ππ+π−)K0
S+−
bcM5.25 5.255 5.26 5.265 5.27 5.275 5.28 5.285 5.291
10
210
310
410
510
Mbc
All cands
MC match
Good cands
" MC match
Mbc
E∆0.2− 0.15− 0.1− 0.05− 0 0.05 0.1 0.15 0.21
10
210
310
410
510
ηM0.4 0.45 0.5 0.55 0.6 0.65 0.71
10
210
310
410
510
610
'ηM0.85 0.9 0.95 1 1.05 1.1 1.151
10
210
310
410
510
610
0πM0.08 0.1 0.12 0.14 0.16 0.18 0.21
10
210
310
410
510
610
/KπLL∆20− 10− 0 10 20 30 40 501
10
210
310
410
510
)-π+π(S
0) K-π+π π3
η'( η→0B
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 7 / 16
Final state with K0S → π0π0
A very preliminary study has been performed also with K0S → π0π0
decay.
The efficiency is roughly 12 of that of the corresponding
K0S → π+π−channel.
events combinatorics is larger for ηγγ (∼ 5 cands/ev), huge for η3π
(∼ 30 cand/ev)
SXF ∼stable, but fractionally larger due to lower signal efficiency.
∆t resolution is same as for K0S → π+π−.
B0 → η′(→ ηγγπ+ π−)K0
S
00
Efficiency %MC true 13.5SXF 2.2
B0 → η′(→ η3ππ+π−)K0
S
00
Efficiency %MC true 6.0SXF 3.8
not used in Belle
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 8 / 16
Vtx reco: signal and tag sideB0 → η′(→ ηγγπ
+ π−)K0S
+−
z (signal) (cm)∆0.05− 0.04− 0.03− 0.02− 0.01− 0 0.01 0.02 0.03 0.04 0.050
100
200
300
400
500
600
700
800
900
/ ndf 2χ 93.4 / 91
Prob 0.41
norm 0.1± 15.6
C
Bias 0.000097± 0.000393
C
σ 0.00015± 0.00371
T
Bias 0.000216± 0.000459
T
σ 0.00054± 0.00924
O
Bias 0.00056± 0.00166
O
σ 0.0013± 0.0269
Cf 0.03± 0.33
Tf 0.022± 0.368
Fit
Core
Tail
Outlier
mµz: 127.55 ∆mµ: 8.00 zBias
z (tag) (cm)∆0.05− 0.04− 0.03− 0.02− 0.01− 0 0.01 0.02 0.03 0.04 0.050
200
400
600
800
1000
1200
1400
1600
1800
2000
/ ndf 2χ 121 / 91
Prob 0.0208
norm 0.1± 16.1
C
Bias 0.000043± 0.000524
C
σ 0.00007± 0.00235
T
Bias 0.00011± 0.00104
T
σ 0.00026± 0.00605
O
Bias 0.00052±0.00199 −
O
σ 0.0006± 0.0185
Cf 0.025± 0.497
Tf 0.021± 0.383
Fit
Core
Tail
Outlier
mµz: 56.96 ∆mµ: 4.23 zBias
)-π+π(S
0) K-π+π γγ
η'( η→0B
Standard
z (signal) (cm)∆0.05− 0.04− 0.03− 0.02− 0.01− 0 0.01 0.02 0.03 0.04 0.050
200
400
600
800
1000
1200
/ ndf 2χ 110 / 91
Prob 0.0839
norm 0.1± 15.2
C
Bias 0.000111± 0.000355
C
σ 0.000± 0.006
T
Bias 0.0001± 0.0002
T
σ 0.00011± 0.00228
O
Bias 0.00035± 0.00101
O
σ 0.0004± 0.0212
Cf 0.017± 0.472
Tf 0.013± 0.207
Fit
Core
Tail
Outlier
mµz: 101.07 ∆mµ: 5.35 zBias
z (tag) (cm)∆0.05− 0.04− 0.03− 0.02− 0.01− 0 0.01 0.02 0.03 0.04 0.050
200
400
600
800
1000
1200
1400
1600
1800
/ ndf 2χ 117 / 91
Prob 0.0348
norm 0.1± 15.5
CBias 0.00004± 0.00052
C
σ 0.00007± 0.00233
T
Bias 0.00011± 0.00104
Tσ 0.00025± 0.00604
OBias 0.00054±0.00211 −
O
σ 0.0006± 0.0186
Cf 0.02± 0.49
Tf 0.02± 0.39
Fit
Core
Tail
Outlier
mµz: 57.22 ∆mµ: 4.09 zBias
)-π+π(S
0) K-π+π γγ
η'( η→0B
WithK0S
z (signal) (cm)∆0.05− 0.04− 0.03− 0.02− 0.01− 0 0.01 0.02 0.03 0.04 0.050
10000
20000
30000
40000
50000
60000
70000
80000
/ ndf 2χ 940 / 91
Prob 0
norm 0.8± 609
C
Bias 0.000017± 0.000435
C
σ 0.00004± 0.00534
T
Bias 0.000006± 0.000233
T
σ 0.0000± 0.0024
O
Bias 0.000± 0.005
O
σ 0.0001± 0.0156
Cf 0.005± 0.357
Tf 0.005± 0.583
Fit
Core
Tail
Outlier
mµz: 42.44 ∆mµ: 5.93 zBias
z (tag) (cm)∆0.05− 0.04− 0.03− 0.02− 0.01− 0 0.01 0.02 0.03 0.04 0.050
10000
20000
30000
40000
50000
60000
70000
/ ndf 2χ 1.02e+03 / 91
Prob 0
norm 0.8± 608
C
Bias 0.000007± 0.000498
C
σ 0.00001± 0.00245
T
Bias 0.00002± 0.00103
T
σ 0.00005± 0.00625
O
Bias 0.00008±0.00122 −
O
σ 0.0001± 0.0179
Cf 0.004± 0.527
Tf 0.003± 0.356
Fit
Core
Tail
Outlier
mµz: 56.17 ∆mµ: 4.88 zBias
)-π+π(S
0) K-π+π γγ
η'( η→0B
WithBS &
K0S
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 9 / 16
Vtx reco: signal and tag sideB0 → η′(→ η3ππ
+π−)K0S
+−
z (signal) (cm)∆0.05− 0.04− 0.03− 0.02− 0.01− 0 0.01 0.02 0.03 0.04 0.050
5000
10000
15000
20000
25000
/ ndf 2χ 774 / 91
Prob 0
norm 0.6± 318
C
Bias 0.000025± 0.000762
C
σ 0.000± 0.006
T
Bias 0.000016± 0.000333
T
σ 0.00002± 0.00242
O
Bias 0.00008± 0.00432
O
σ 0.0001± 0.0174
Cf 0.004± 0.465
Tf 0.003± 0.296
Fit
Core
Tail
Outlier
mµz: 76.55 ∆mµ: 14.81 zBias
z (tag) (cm)∆0.05− 0.04− 0.03− 0.02− 0.01− 0 0.01 0.02 0.03 0.04 0.050
5000
10000
15000
20000
25000
30000
35000
/ ndf 2χ 674 / 91
Prob 0
norm 0.6± 319
C
Bias 0.000010± 0.000499
C
σ 0.00002± 0.00245
T
Bias 0.000027± 0.000983
T
σ 0.00007± 0.00616
O
Bias 0.00011±0.00148 −
O
σ 0.0001± 0.0179
Cf 0.006± 0.511
Tf 0.005± 0.369
Fit
Core
Tail
Outlier
mµz: 56.71 ∆mµ: 4.41 zBias
)-π+π(S
0) K-π+π π3
η'( η→0B
Stan
dard
z (signal) (cm)∆0.05− 0.04− 0.03− 0.02− 0.01− 0 0.01 0.02 0.03 0.04 0.050
5000
10000
15000
20000
25000
30000
35000
40000
45000
/ ndf 2χ 1.34e+04 / 91
Prob 0
norm 0.5± 301
C
Bias 0.00004± 0.00112
C
σ 0.00006± 0.00435
T
Bias 0.000007± 0.000199
T
σ 0.00001± 0.00205
O
Bias 0.000± 0.005
O
σ 0.0001± 0.0142
Cf 0.008± 0.285
Tf 0.009± 0.624
Fit
Core
Tail
Outlier
mµz: 38.10 ∆mµ: 8.98 zBias
z (tag) (cm)∆0.05− 0.04− 0.03− 0.02− 0.01− 0 0.01 0.02 0.03 0.04 0.050
5000
10000
15000
20000
25000
30000
35000
/ ndf 2χ 634 / 91
Prob 0
norm 0.5± 301
C
Bias 0.000028± 0.000995
C
σ 0.00007± 0.00619
T
Bias 0.000010± 0.000496
T
σ 0.00002± 0.00246
O
Bias 0.00012±0.00148 −
O
σ 0.0002± 0.0179
Cf 0.005± 0.366
Tf 0.006± 0.515
Fit
Core
Tail
Outlier
mµz: 56.70 ∆mµ: 4.43 zBias
)-π+π(S
0) K-π+π π3
η'( η→0B
With
BS
&K
0S
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 10 / 16
Background reduction (before CS cut)
B0 → η′(→ ηγγπ+ π−)K0
S+−
Sample # Ev (M) Skim (M) εskim pre-sel sel εsel
uu 1284 10.8 0.84 · 10−2 235388 3324 2.69 · 10−6
dd 321 2.88 0.90 · 10−2 66138 983 3.06 · 10−6
ss 306 3.82 1.25 · 10−2 67205 734 2.40 · 10−6
cc 1063 18.3 1.72 · 10−2 255161 3852 3.62 · 10−6
B0B0 429 .153 0.036 · 10−2 1949 55 0.13 · 10−6
B+B− 420 .169 0.04 · 10−2 1818 18 0.043 · 10−6
total 3620 36.2 0.95 · 10−2 627659 8966 2.34 · 10−6
B0 → η′(→ η3ππ+π−)K0
S+−
Sample # Ev (M) Skim (M) εskim pre-sel sel εsel
uu 1284 10.8 0.84 · 10−2 515531 442 0.34 · 10−6
dd 321 2.88 0.90 · 10−2 213980 172 0.54 · 10−6
ss 306 3.82 1.25 · 10−2 84005 52 0.17 · 10−6
cc 1063 18.3 1.72 · 10−2 1.94 · 106 1593 1.50 · 10−6
B0B0 429 .131 0.036 · 10−2 34668 60 0.14 · 10−6
B+B− 420 .324 0.04 · 10−2 38631 9 0.02 · 10−6
total 3620 36.3 0.95 · 10−2 2.75 · 106 2259 0.59 · 10−6
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 11 / 16
Background distributionAll candidates
5.25 5.255 5.26 5.265 5.27 5.275 5.28 5.285 5.29
1000
2000
3000
4000
5000
6000
bcM
mixedcharged
uuddss
cc
0.2− 0.15− 0.1− 0.05− 0 0.05 0.1 0.15 0.2
2000
4000
6000
8000
10000
12000
14000
E∆0.4 0.45 0.5 0.55 0.6 0.65 0.7
2000
4000
6000
8000
10000
12000
14000
16000
18000
ηM
0.85 0.9 0.95 1 1.05 1.1 1.15
5000
10000
15000
20000
25000
30000
'ηM0.45 0.46 0.47 0.48 0.49 0.5 0.51 0.52 0.53 0.54 0.55
10000
20000
30000
40000
50000
60000
70000
80000
90000
S0KM
20− 10− 0 10 20 30 40 50
1
10
210
310
410
/KπLL∆
)-π+π(S
0) K-π+π) γγ(η'( η→0B
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 12 / 16
Background distributionAll candidates
5.25 5.255 5.26 5.265 5.27 5.275 5.28 5.285 5.29
10000
20000
30000
40000
50000
bcM
mixedcharged
uuddsscc
0.2− 0.15− 0.1− 0.05− 0 0.05 0.1 0.15 0.2
20
40
60
80
100
120
140
310×
E∆0.4 0.45 0.5 0.55 0.6 0.65 0.7
50
100
150
200
250
300
350
400
450
310×
ηM
0.85 0.9 0.95 1 1.05 1.1 1.15
100
200
300
400
500
600
310×
'ηM0.08 0.1 0.12 0.14 0.16 0.18 0.2
100
200
300
400
500
310×
0πM0.45 0.46 0.47 0.48 0.49 0.5 0.51 0.52 0.53 0.54 0.55
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
2400
310×
S0K
M
)-π+π(S
0) K-π+π) 0π-π+π(η'( η→0B
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 13 / 16
Yields
Expected yield for L = 300 fb−1
NB
0 = 2 · NB
0B
0 = 2 · L · 1.1 nb · 0.486 = 2 ∗ 170 · 106 = 340 · 106
NB
0→η′K0 = NB
0 · BR(= 6.6 · 10−5) = 22000, NB
0→η′K0S
= 11000
B0 → η′(→ ηγγπ+ π−)K0
S+−
N =NB
0→η′K0S· BR · ε
=11000 · (0.43 · 0.40 · 0.69) · .30
=390 events
SXF =15
Continuum ≈3300
Peaking ≈30
Before Continuum Suppression cut
B0 → η′(→ η3ππ+π−)K0
S+−
N =NB
0→η′K0S· BR · ε
=11000 · (0.43 · 0.23 · 0.69) · .15
=112 events
SXF =27
Continuum ≈850
Peaking ≈27
Before Continuum Suppression cut
Background
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 14 / 16
Continuum Suppression correlation matrix
100−
80−
60−
40−
20−
0
20
40
60
80
100
B0_ThrustB
B0_ThrustO
B0_CosTBTO
B0_CosTBz
B0_R2B0_cc1
B0_cc2B0_cc3
B0_cc4B0_cc5
B0_cc6B0_cc7
B0_cc8B0_cc9
B0_mm2B0_et
B0_hso00
B0_hso02
B0_hso04
B0_hso10
B0_hso12
B0_hso14
B0_hso20
B0_hso22
B0_hso24
B0_hoo0B0_hoo1
B0_hoo2B0_hoo3
B0_hoo4
B0_ThrustBB0_ThrustO
B0_CosTBTOB0_CosTBz
B0_R2B0_cc1B0_cc2B0_cc3B0_cc4B0_cc5B0_cc6B0_cc7B0_cc8B0_cc9
B0_mm2B0_et
B0_hso00B0_hso02B0_hso04B0_hso10B0_hso12B0_hso14B0_hso20B0_hso22B0_hso24B0_hoo0B0_hoo1B0_hoo2B0_hoo3B0_hoo4
Correlation Matrix (signal)
100 2 2 1 3 3119 7 2 2 1 2 1 1 2 1 2 1 2100 17 3 65 12 16 14 9 1102025 5 8 3 32 4 5 27 5 12 9 4 4 1 54 2 20 2 17100 8 61 8 24 30 25 15 183746 8 14 7 52 6 16 63 17 12 29 12 11 1 15 2 4 1 3 8100 4 2 1 9 910 8 3 5 8 742 1 3 71213 6 9 311 5 1 3 65 61 4100 9 20 24 16 216344854 101422 61 13 52 20 1 26 1417 33 1 16 31 8 2 910059 9 3 5 2 6 7 6 11 18 1 5 3 6 1 4 219 12 24 1 2059100 7 1 1 2 4 5 414 18 2 14 11 21 36 37 16 18 11 20 1 18 8 7 16 30 9 24 9 7100 3 3 5 7 822 31 4 22 6 31 44 15 24 20 6 30 25 9 2 14 25 9 16 1 3100 1 1 4101118 34 8 22 7 31 3219 18 6 2 30 1 21 2 1 2 9 1510 2 1 1100 2101219 32 13 1625 28 1730 16 3 30 2 17 2 1 1 816 2 3 1 100 4 5 617 26 16 127 22 24 12 5 5 25 7 2 2
1018 334 4 5 4 2 4100 14 22 142011 211311 11 9 6 23 1 3 22037 548 5 71010 5 100 712 16 1636 12 1724 8 911 6 20 1 42546 854 4 81112 6 710014 15 1643 29 1627 19 1010 5 21 6 1 1 5 8 7 10 314221819171412141005524 55321388411379 164 235
2 8 144214 5 18 31 34 32 26 22 16 1555100 31 4 1 83 47 9 51 11 1 86 1 41 1 8 1 3 7 122 2 2 4 8 13 16 14 16 1624 31100 5 5 6 2 2 2 2 1 29 5 7 5 1 32 52 3 61 6 14 22 22 16 1203643 4 5100 7 2 16 2 2 5 3 2 20 3 2 4 6 7 7 11 6 7252711 12 29 1 5 7100 1 2 3 1 1 1 1 5 1 5 161213 6 21 31 31 28 22 21 17 1655 83 6 2 1100 52 17 58 15 1 87 1 42 11 2 27 6313 52 11 36 44 32 17 13242732 47 2 16 2 52100 42 36 43 19 46 42 1 15
5 17 6 20 18 37 1519302411 8 1913 9 2 2 3 17 42100 16 29 20 15 15 1 13 12 12 9 1 1 16 24 18 16 12 11 9 1088 51 2 2 1 58 36 16100 41 11 75 61 34 9 29 26 5 18 20 6 5 9111041 11 2 5 15 43 29 41100 44 23 45 33 4 12 3 14 3 11 6 2 3 5 6 6 513 1 1 3 1 19 20 11 44100 3 16 22 4 111117 6 20 30 30 30 25 23 20 2179 86 29 87 46 15 75 23 3100 53 2 18 1 1 1 1 1 2 1 1 1 5 2 1 1 100 28 2
1 54 15 5 33 4 18 25 21 17 7 1 4 664 41 7 20 1 42 42 15 61 45 16 53 100 1 54 2 2 1 2 2 2 3 1 2 1 5 1 1 1 2 28 1100 1 20 4 1 16 2 8 9 1 2 2 135 8 3 5 11 15 13 34 33 22 18 2 54 1100
Linear correlation coefficients in %
100−
80−
60−
40−
20−
0
20
40
60
80
100
B0_ThrustB
B0_ThrustO
B0_CosTBTO
B0_CosTBz
B0_R2B0_cc1
B0_cc2B0_cc3
B0_cc4B0_cc5
B0_cc6B0_cc7
B0_cc8B0_cc9
B0_mm2B0_et
B0_hso00
B0_hso02
B0_hso04
B0_hso10
B0_hso12
B0_hso14
B0_hso20
B0_hso22
B0_hso24
B0_hoo0B0_hoo1
B0_hoo2B0_hoo3
B0_hoo4
B0_ThrustBB0_ThrustO
B0_CosTBTOB0_CosTBz
B0_R2B0_cc1B0_cc2B0_cc3B0_cc4B0_cc5B0_cc6B0_cc7B0_cc8B0_cc9
B0_mm2B0_et
B0_hso00B0_hso02B0_hso04B0_hso10B0_hso12B0_hso14B0_hso20B0_hso22B0_hso24B0_hoo0B0_hoo1B0_hoo2B0_hoo3B0_hoo4
Correlation Matrix (background)
100 2 3 8 10 381816 5 5 2 4 8 2 3 1 5 4 2 4 2 4 4 2 4 3 2 2100 41 88 9 26 22 13 325344649 4 5 8 45 25 10 53 30 8 21 14 5 51 3 32 3 41100 6 49 6 18 20 8 115263443 1 3 1 33 20 4 41 18 19 9 1 5 19 5 12 8 6100 9 10 4171713 5 8 863 2 1 42919 612 4 523 3 7 6 3 10 88 49 9100 19 21 13 32139454951 112121 41 3414 44 39 9 21 1720 2 36 2 33 38 9 6 10 19100571313 3 5 3 1 6 6 14 19 3 6 21 2 3 2 1 5 6 4 918 26 18 4 2157100 6 1 2 3 6 6 619 22 6 22 22 25 36 33 18 20 18 25 1 33 2 2516 22 2017 1313 6100 8 4 3 4 9 422 37 7 12 5 36 46 17 23 11 5 34 1 31 2 9 5 13 817 313 1 8100 511 1 2 515 36 8 1113 34 2221 18 3 6 31 21 2
3 11321 2 4 5100 1 1 1 20 30 13 419 30 726 18 3 5 32 2 10 5 52515 539 3 3 311 1100 2 11 418 17 19 713 151220 15 6 8 23 3 5 13
3426 845 5 6 4 1 1 2100 5 714 19 121917 161412 13 7 3 20 9 5 8 24634 49 6 9 2 1 11 5100 8 6 13 1423 8 1318 613 9 16 116 211
4943 51 3 6 4 5 4 7 810011 13 1718 1 927 3 8 8 6 16 515 1 8 4 4 1 8 11 1192215201814 611100473419 548341587583479 572 546 8 5 36321 6 22 37 36 30 17 19 13 1347100 29 17 1 80 53 10 48 7 81 44 4 9 2 8 1 221 6 6 7 8 13 19 12 14 1734 29100 55 23 812 5 13 2 3510 14 8 2 3 45 33 1 41 14 22 12 11 4 719231819 17 55100 64 4 4 8 8 7 2013 35 7 20 1 25 20 4 34 19 22 513191317 8 1 5 1 23 64100 7 2 3 3 310 18 4 21 5 10 42914 3 25 36 34 30 15 16 13 948 80 8 4 7100 69 21 55 14 5 83 3 50 1 15 4 53 4119 44 6 36 46 22 71214182734 5312 4 2 69100 57 38 33 21 55 4 63 2 34 2 30 18 6 39 21 33 1721262012 315 10 5 21 57100 13 30 28 17 37 2 36 4 8 12 9 2 18 23 18 18 15 13 6 887 48 13 8 55 38 13100 60 33 77 70 45 2 21 19 4 21 3 20 11 3 3 6 713 858 7 2 8 3 14 33 30 60100 62 31 2 69 3 58 4 14 9 5 17 2 18 5 6 5 8 3 9 634 7 3 5 21 28 33 62100 15 4 42 49 4 5 12320 1 25 34 31 32 23 20 16 1679 81 35 20 3 83 55 17 77 31 15100 2 65 3 27 2 5 3 2 5 1 1 2 3 1 5 5 101310 3 4 2 4 2100 38 3 4 51 19 7 36 6 33 31 21 10 5 9161572 44 14 35 18 50 63 37 70 69 42 65 100 5 69 3 3 5 6 2 4 2 2 2 5 2 1 5 4 8 7 4 1 2 2 3 3 38 5100 1 2 32 12 3 33 9 25 9 513 811 846 9 2 20 21 15 34 36 45 58 49 27 3 69 1100
Linear correlation coefficients in %
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 15 / 16
Bibliography I
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′K
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π0
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http://link.aps.org/doi/10.1103/PhysRevD.79.052003.
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′K
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0, K
0S K
0S K
0S and B
0 → j/ψK0
decays. PRL, 98:031802, 2007. doi:10.1103/PhysRevLett.98.031802. URL http://link.aps.org/doi/10.1103/PhysRevLett.98.031802.
S.Lacaprara (INFN Padova) B0 → η
′K
0S WG3 18/05/2016 16 / 16