antonio falabella - infn sezione di ferrara · 2013. 12. 20. · antonio falabella universit a di...

The LHCb experiment Flavour Tagging Tuning on data Development of SS proton algorithm Conclusion

Flavour tagging developments for the LHCb experiment

Antonio Falabella

Universita di FerraraDottorato di Ricerca - Ciclo 26

December 16, 2013

1/25


Overview

1 The LHCb experiment

2 Flavour Tagging

3 Tuning on data

4 Development of SS proton algorithm

5 Conclusion

2/25


The LHCb detector

VELO

TRACKER

RICH1

MAGNET

MUON

CALORIMETERSRICH2

LHCb measurements

Improve the determination ofCKM matrix parameters inb-meson decays.

New Physics from rare decaysof B and D mesons.

Detector requirements

Very efficient Trigger system (Twolevels L0(hardware), HTL(software))

Mass resolution and Particle ID

(RICHs, CALOs and MUON)

Excellent Vertexing and Tracking

(VELO and TRACKING SYSTEM)

3/25


Flavour Tagging

Why Flavour Tagging

CP violation studies usually involve the study of time-dependent ratesasymmetries.

A(t) =N(B0 → f )(t)− N(B0 → f )(t)

N(B0 → f )(t) + N(B0 → f )(t)

The determination of this observable rely on the knowledge of the productionB flavour.

How to perform Flavour Tagging

p-p collisions produce b − b pairs by strong interactions. The signal-Bflavour can be inferred by the decay of the accompanying b−hadron decay(Opposite Side tagging) or exploiting the fragmentation process of the bto the signal B meson (Same Side tagging).

4/25


Flavour Tagging algorithms

OS : muon, electron, kaon and inclusive vertex

SS : pion, kaon, proton

5/25


Flavour Tagging - Some definitions

The flavour is determined by the charge of the particle used to tag.

Flavour tagging performances are quantified by:

εeff = (1− 2ω)2 · εtag , ω =W

R + W, εtag =

R + W

R + W + U

The error on the asymmetry A can be calculated (Am is the measuredasymmetry)

Am ∝ (1− 2ω)e−(∆mqσt )2/2A(t) , σA ∝√

1− A2m√

εtagN(1− 2ω)

which shows that to minimize the statistical error the εeff need to bemaximized

Measure and optimize (εeff ) the performances of tagging algorithms usingflavour specific control channels:B+ → J/ΨK+,B0 → D∗−µ+νµ,B0 → J/ΨK∗0, B0 → D−π+

6/25


Calculation and Calibration of the predicted mistag

Per-event mistag estimation (η)

Using per-event mistag (η) is proved to improve tagging power(+20/30%).

For each tagger it is estimated using multivariate methods (Neuralnetworks, BDT,...) that uses as inputs kinematical and geometricalinformation on taggers and is trained to identify the correct chargecorrelation.

When more than one tagging algorithm give a decision they are combinedto give a final decision according to individual decisions and probabilities.

η calibration

To provide a correct estimation of the mistag η must be calibrated

The calibration is made assuming a linear dependencyω = p0 + p1(η− < η >).

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Tuning on data

Contributions to tagging from my 2012 studies: new NNet structure,training on 2012 data

with 2010 and 2011 data training was not possible due to the lack ofstatisticsavoid complications due to different variable distribution between MC/dataPerformance improvements

year statistics εtagD2 tagging physics results

2010 35pb−1 1.97± 0.18 OS ∆ms , φs

2.38± 0.18 OS + SSπ sin(2β)

2011 0.37fb−1 2.07± 0.11 OS2011 1.0fb−1 2.35± 0.06 OS ∆md , sin(2β),B0

s → DsK ,B → hh

2011 + 2012 3.0fb−1 2.80± 0.082011 + 2012 3.0fb−1 0.47± 0.04 SSp

8/25


Development of SS proton algorithm

Idea behind Same Side algorithms is to use the charge correlation betweena B and a closed-by track to infer B flavour at production.

Two possible sources of charge correlation:Origin in the decay of a higher mass resonanceCorrelation in the b hadronization process ”associate production” (AP)

Bu and Bd cases

For B+ you can have a companion π,K or p track with the same chargecorrelation, while in B0 you have π and p with opposite charge correlation.Studying a B neutral channel a SSπ and SSp tagging algorithm can thanbe developed

Main focus of my studies is on SS proton

9/25


Implementation details

Tuning on data

I used 2012 data (2fb−1). Data sample

corresponds to B0 → D−(→ Kππ)π+

Train a Boosted Decision Tree (BDT → backup)to select right/wrong charge closed-by tracks

B0p → wrong signB0p → right sign

The BDT is trained from a set of input variablesand using per-event sWeights (→ backup) to takeinto account the background contamination

B0 → D−(→ Kππ)π+

)2m (MeV/c5200 5250 5300 5350 5400

)2E

vent

s / (

2 M

eV/c

0

2000

4000

6000

8000

10000

12000

14000

16000 2637±N_bkg = 70101

13465±N_sig = 357917

Signal

Background

Total

)2m (MeV/c

5200 5250 5300 5350 5400

Pul

l

-5

0

5

# of entries = 428018

# of Bd signal candidates =357917

S/B = 5.11

10/25


Preselection Cuts and Multiplicity

Cannot use all B candidates for training.For mixed event the correlation is opposite→ cut on decay time: For t < 2.2psfraction of non oscillated events 0.93

t [ps]0 5 10

Mix

ing

Asy

mm

etry

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

Track related cut:PIDp > 5 → selecting protonsGhost Prob < 0.5,IPPU > 9

B plus track system related cuts:dQ < 1050MeV where dQ = MB+track −mB −mtrack

∆φ < 1.2, ∆η < 1.2

m, t, pidP,CUTS m m, t dQ,∆φ,∆η, Ghost Prob, IPPU

Signal events 357801 227420 111275Multiplicity 5.2 5.1 1.6

ε 0.84 0.53 0.41

Preselection Cuts → reduce number of tagging tracks and the multiplicityper candidate

11/25


Input Variables

The best set of input variables to train the BDT that I found are:For B plus track system: dQ, pT (B + comp), ∆φ, ∆ηFor the companion track: PIDp, p, pT , IPχ2,Ghost Prob, IPPUFor the B: pT

For the event : Ntracks in PV

dQ [F]200 400 600 800 1000 1200

33.3

F /

(1/N

) dN

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0.0014 RightWrong

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

Input variable: dQ

log(B+track_PT) [F]8.5 9 9.5 10 10.5 11

0.07

98 F

/ (1

/N)

dN

0

0.2

0.4

0.6

0.8

1

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

Input variable: log(B+track_PT)

log(Track_PT) [F]6 6.5 7 7.5 8 8.5 9

0.08

45 F

/ (1

/N)

dN

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

Input variable: log(Track_PT)

log(B_PT) [F]7.5 8 8.5 9 9.5 10 10.5 11

0.09

88 F

/ (1

/N)

dN

0

0.2

0.4

0.6

0.8

1

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

Input variable: log(B_PT)

log(Track_P) [F]7.5 8 8.5 9 9.5 10 10.5 11 11.5 12

0.12

5 F

/ (1

/N)

dN

0

0.1

0.2

0.3

0.4

0.5

0.6

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

Input variable: log(Track_P)

log(Dphi) [F]-8 -6 -4 -2 0

0.25

1 F

/ (1

/N)

dN0

0.1

0.2

0.3

0.4

0.5

0.6

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

Input variable: log(Dphi)

Small differences between right and wrong charge correlated tracks

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Input Variables

log(Deta) [F]-8 -6 -4 -2 0

0.26

F /

(1/N

) dN

0

0.1

0.2

0.3

0.4

0.5

U/O

-flo

w (

S,B

): (

-0.0

, 0.0

)% /

(0.0

, 0.0

)%

Input variable: log(Deta)

log(Track_PIDp) [F]2 2.5 3 3.5 4 4.5

0.08

09 F

/ (1

/N)

dN

0

0.1

0.2

0.3

0.4

0.5

0.6

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

Input variable: log(Track_PIDp)

log(N_tracks) [I]2 2.5 3 3.5 4 4.5 5 5.5

0.09

46 I

/ (1

/N)

dN

0

0.2

0.4

0.6

0.8

1

1.2

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

Input variable: log(N_tracks)

log(Track_IPCHI2) [F]-10 -8 -6 -4 -2 0 2

0.36

6 F

/ (1

/N)

dN

0

0.05

0.1

0.15

0.2

0.25

0.3

U/O

-flo

w (

S,B

): (

0.0,

-0.

0)%

/ (0

.0, 0

.0)%

Input variable: log(Track_IPCHI2)

Small differences between right and wrong charge correlated tracks

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BDT Training and Variable Ranking

Use all tracks

Use event number to split the sample (EVEN=training, ODD=test)

AdaBoost for training

ssProton response

-0.4 -0.2 0 0.2 0.4 0.6 0.8 1

dx / (1

/N)

dN

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2Right (test sample)

Wrong (test sample)

Right (training sample)

Wrong (training sample)

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

TMVA overtraining check for classifier: ssProton

Rank Variable Variable Importance1 PIDp 1.706e-012 log(pcomp) 1.267e-013 log(pT comp) 1.207e-01

4 dQ 1.177e-015 log(∆phi) 1.064e-016 log(∆eta) 7.947e-027 log(pTB ) 7.550e-028 log(Ntracks inPV ) 7.201e-029 log(pTB+comp) 6.780e-02

10 log(IPχ2comp) 6.318e-02

14/25


Performances

Performances computed in both samples to test overtraining

In the plots εtag (1− 2ω)2 as a function of BDT cut for training (EVEN)and for testing samples (ODD)

ω = W /R + W for t < 2.2ps (slightly overestimated due to B mixing)

In case of multiple candidates choose the one with largest BDT

Average εtagD2 vs BDT cut

BDT-1 -0.5 0 0.5 1

eD2

[%]

0

0.05

0.1

0.15

0.2

0.25

Best average tagging power for BDT > 0.5 (EVEN) : εtagD2 = 0.23%

Best average tagging power for BDT > 0.5 (ODD): εtagD2 = 0.13%

15/25


Fit to oscillation

Unbiased determination of ω from the fit to B oscillation (no time cut). In caseof multiple proton candidates choose the one with highest BDT response

I define a binning to have the same statistics in each one:

BDT bin [−1.0, 0.0] [0.0, 0.15] [0.15, 0.3] [0.3, 0.4] [0.4, 0.55] [0.55, 0.7] [0.7, 0.8] [0.8, 1.]ω[%](Odd) 49.5± 0.6 49.4± 0.6 47.2± 0.6 45.1± 0.6 43.4± 0.7 41.4± 1.0 37.2± 1.7 25.3± 1.8ω[%](Even) 50.9± 0.5 49.6± 0.6 46.2± 0.6 46.7± 0.8 42.8± 0.7 38.0± 1.0 33.0± 1.6 26.5± 1.9

[−1.0, 0.0] [0.0, 0.15] [0.15, 0.3] [0.3, 0.4]

t [ps]0 5 10

Mix

ing

Asy

mm

etry

-0.6

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0

0.2

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t [ps]0 5 10

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etry

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t [ps]0 5 10

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etry

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0

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[0.4, 0.55] [0.55, 0.7] [0.7, 0.8] [0.8, 1.0]

t [ps]0 5 10

Mix

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etry

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0

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t [ps]0 5 10

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etry

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0

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t [ps]0 5 10

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0

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t [ps]0 5 10

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0

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0.6

Oscillation clearly visible; amplitude increase as a function of the BDT output

Mistag determination from each bin compatible in the two subsamples

16/25


Calibration of the BDT output

Find per-event ω estimation as a function of BDT output

Plot ω VS BDT for each BDT bin (ODD sample) → polynomial (left plot)

η = pol(BDT ) should be already calibrated (middle plot)

BDT-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

ω

0

0.1

0.2

0.3

0.4

0.5

0.6

/ ndf 2χ 6.485 / 5p0 0.003608± 0.4892 p1 0.01313± -0.08681 p2 0± 0 p3 0± 0 p4 0± 0 p5 0.04131± -0.2945

/ ndf 2χ 6.485 / 5p0 0.003608± 0.4892 p1 0.01313± -0.08681 p2 0± 0 p3 0± 0 p4 0± 0 p5 0.04131± -0.2945

) Calibration SSprotonωMistag (

η0 0.1 0.2 0.3 0.4 0.5 0.6

ω

0

0.1

0.2

0.3

0.4

0.5

0.6

/ ndf 2χ 3.778 / 4

p0 0.003163± 0.4547

p1 0.08536± 1.016

> η< 0± 0.453

/ ndf 2χ 3.778 / 4

p0 0.003163± 0.4547

p1 0.08536± 1.016

> η< 0± 0.453


heta__etaEntries 79323

Mean 0.453

RMS 0.03839

η0 0.1 0.2 0.3 0.4 0.5 0.6

a.u

.

0

0.02

0.04

0.06

0.08

0.1

0.12heta__eta

Entries 79323

Mean 0.453

RMS 0.03839

p1 p0 < η > ε[%] εeff [%]Odd Sample 1.015± 0.085 0.454± 0.003 0.453 31.3± 0.1 0.471± 0.045Even Sample 1.236± 0.085 0.449± 0.003 0.453 31.2± 0.1 0.624± 0.051

17/25


Validation on B → Dπ 2011 unbiased sample

Use B → Dπ 2011 sample (1fb−1) forvalidation

Similar data analysis (no BDTtraining, only performances evaluationand calibration cross-check)

S/B similar to 2012 data

)2m (MeV/c5200 5250 5300 5350 5400

)2E

vent

s / (

2 M

eV/c

0

1000

2000

3000

4000

5000

6000

7000 4710±N_bkg = 30034

24006±N_sig = 153217

Signal

Background

Total

)2m (MeV/c

5200 5250 5300 5350 5400

Pul

l

-5

0

5

# of entries = 183251

# of Bd signal candidates=153217

S/B = 5.10

18/25


Validation on Dπ 2011

Using the BDT trainied on 2012 data, and the same polynomial function

η0 0.1 0.2 0.3 0.4 0.5 0.6

ω

0

0.1

0.2

0.3

0.4

0.5

0.6

/ ndf 2χ 6.25 / 4

p0 0.003308± 0.4625

p1 0.08778± 1.05

> η< 0± 0.4519

/ ndf 2χ 6.25 / 4

p0 0.003308± 0.4625

p1 0.08778± 1.05

> η< 0± 0.4519


heta__etaEntries 183078

Mean 0.458

RMS 0.03299

η0 0.1 0.2 0.3 0.4 0.5 0.6

a.u

.

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14 heta__etaEntries 183078

Mean 0.458

RMS 0.03299

p1 p0 < η > ε[%] εeff [%]1.050± 0.087 0.462± 0.003 0.452 33.0± 0.1 0.418± 0.046

Calibration: p1 OK, p0− < η > compatible with 0 in ∼ 3σ

Performances: compatible with 2012 unbiased sample

19/25


Validation on B0 → J/ψK ∗

Use J/ψK∗ for validation2012 data sample (2fb−1) (left plot)2011 data sample (1fb−1) (right plot)

2012 2011

)2m (MeV/c5240 5260 5280 5300 5320

)2E

vent

s / (

0.9

MeV

/c

0

2000

4000

6000

8000

10000

12000

14000 16642±N_bkg = 227250

18333±N_sig = 250101

Signal

Background

Total

)2m (MeV/c

5240 5260 5280 5300 5320

Pul

l

-5

0

5)2m (MeV/c

5240 5260 5280 5300 5320

)2E

vent

s / (

0.9

MeV

/c

0

1000

2000

3000

4000

5000

6000 17243±N_bkg = 78488

25917±N_sig = 118104

Signal

Background

Total

)2m (MeV/c

5240 5260 5280 5300 5320

Pul

l

-5

0

5

# of entries candidates = 477351

# of Bd signal candidates = 250101

S/B = 1.10

# of entries candidates = 196592

# of Bd signal candidates = 118104

S/B = 1.5020/25


Validation on J/ψK ∗ 2012 - Fit to oscillation

Oscillation clearly visible as a function of the BDT output

[−1.0, 0.0] [0.0, 0.15] [0.15, 0.3] [0.3, 0.4]

t [ps]0 5 10

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ing

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etry

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[0.4, 0.55] [0.55, 0.7] [0.7, 0.8] [0.8, 1.0]

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etry

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21/25


Validation on J/ψK ∗ 2012

Using the BDT trained on 2012 data, and the same polynomial function

2012 2011 2011+2012

η0 0.1 0.2 0.3 0.4 0.5 0.6

ω

0

0.1

0.2

0.3

0.4

0.5

0.6

/ ndf 2χ 2.053 / 4

p0 0.003165± 0.4632

p1 0.09358± 0.9419

> η< 0± 0.4581

/ ndf 2χ 2.053 / 4

p0 0.003165± 0.4632

p1 0.09358± 0.9419

> η< 0± 0.4581


η0 0.1 0.2 0.3 0.4 0.5 0.6

ω

0

0.1

0.2

0.3

0.4

0.5

0.6

/ ndf 2χ 4.243 / 4

p0 0.004419± 0.4677

p1 0.1276± 0.9451

> η< 0± 0.4574

/ ndf 2χ 4.243 / 4

p0 0.004419± 0.4677

p1 0.1276± 0.9451

> η< 0± 0.4574


η0 0.1 0.2 0.3 0.4 0.5 0.6

ω

0

0.1

0.2

0.3

0.4

0.5

0.6

/ ndf 2χ 1.017 / 4

p0 0.002726± 0.4634

p1 0.08002± 0.9339

> η< 0± 0.458

/ ndf 2χ 1.017 / 4

p0 0.002726± 0.4634

p1 0.08002± 0.9339

> η< 0± 0.458


p1 p0 < η > ε[%] εeff [%]2012 0.942± 0.093 0.463± 0.003 0.458 25.7± 0.1 0.23± 0.032011 0.945± 0.128 0.468± 0.004 0.457 26.0± 0.1 0.25± 0.04

2012+2011 0.934± 0.080 0.463± 0.003 0.458 26.0± 0.1 0.24± 0.02

Calibration: p1 OK, slight offset forp0 → p0− < η > compatible with 0in ∼ 3σ

Performances: smaller εeff due todifferent BpT spectrum

Reweighting → εeff [%] = 0.35± 0.02

B0 → Dπ B0 → J/ψK∗

Entries 79711

Mean 8374

RMS 3649

0 5000 10000 15000 20000 25000

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

Entries 79711

Mean 8374

RMS 3649

B_pT22/25


Conclusion

Development of a new SS tagging algorithm using protons based on aBDT:

Used B0 → D−(→ Kππ)π+ 2012 data sample for trainingFor the unbiased B0 → Dπ sample → εeff [%] = 0.471± 0.045For the B0 → J/ψK∗ sample (2011+2012) → εeff [%] = 0.24± 0.02

Calibration proved to be portable across different data samples and adifferent channel

23/25


My 3nd year of Ph.D.

I spent the 3nd of my Ph.D. covering two main activities:Development and optimization of a new same side proton FT algorithm forthe LHCb experiment;I worked with the offline computing group of the LHCb experiment and inparticular with the LHCbDirac developers group.

Report regularly at the Flavour Tagging working group meeting aboutresults and progresses of my studies

A partecipated with a poster contribution to Beauty 2013 Internationalconference in Bologna;

I partecipated to IFAE in Cagliari with a poster contribution on LHCbFlavour Tagging;

I partecipated to IUSS Niccolo Cabeo school about Beyond StandardModel physics in Ferrara;

24/25


Thank you!

25/25


BACKUP

26/25


sPlots technique1

Statistical tool to unfold data distributions

Event characterized by a set of variables:Discriminating variables: the distribution of all the sources of events areknownControl variables: the distribution of some sources of events are unknown

→ sPlots technique reconstruct the distribution of the control variables foreach source of events

used to unfold signal and background event in a data sample

for example given Fs (y) and Fb(y) distributions for the discriminatingvariable y for signal and background assuming Ns and Nb number ofevents for the two sources

1arxiv.org/abs/physics/040208327/25


sPlots technique - cont’d

Define the sWeight as:

Ws (y) =VssFs (y) + VsbFb(y)

NsFs (y) + NbFs (y), where V−1

ij =N∑

e=1

=Fi (ye)Fj (ye)

(NsFs (ye) + NbFs (ye))2

weighting the control variable disitribution x by the Ws (ye) gives the truedistribution for the signal component of x

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Boosted Decision Trees

Decision tree are binary tree classifiersan event is classified after repeating a yes/no decision on one singlevariablethe phase space is then split in many regions that can be classified assignal or background

boosting → build several trees: forestfinal decision is the weighted average of each individual decision treeboosting improve stability with respect to fluctuations of the training sample

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antonio falabella - infn sezione di ferrara · 2013. 12. 20. · antonio falabella universit a di...

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