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Status of B→D(Kππ°)K Analysis

Minakshi NayakDepartment of Physics, IIT Madras

Chennai

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Motivation• BBBB→→→→[K[K[K[K+ + + + ππππ----ππππ°°°°]]]]DDDDK plays a very important role in K plays a very important role in K plays a very important role in K plays a very important role in

the measurement of the measurement of the measurement of the measurement of φφφφ3333 ....

• ΦΦΦΦ3 3 3 3 measured in Bmeasured in Bmeasured in Bmeasured in B---- →→→→DKDKDKDK---- tree level decay:tree level decay:tree level decay:tree level decay:

• Branching fraction measured inBranching fraction measured inBranching fraction measured inBranching fraction measured in BBBB→→→→[K[K[K[Kππππππππ0]]]]DDDDK decay K decay K decay K decay ≈≈≈≈ (13.9 (13.9 (13.9 (13.9 ±±±± 0.5) 0.5) 0.5) 0.5) ％％％％

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Motivation

� Followed ADS method to search for decay B→D(Kππ°)K� Observables measured via ADS method:

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CLEO: R Kππ0 = 0.84±0.07, δ = (227+14-17)o

BABAR: RADS = 0.0091+0.0082-0.0076

+0.0014-0.0037

N.Lowery et al. CLEO Collabaration, PRD 80, 031105(R ),(2009)

BABAR Collabaration, J. P. Lees et al. PRD84, 01200 2(2011)

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Data Samples� Generated 100,000 signal MC events for B→[Kππ0]D K

using Evtgen Program� 55,000 skimmed continuum MC & 50,000 skimmed

generic MC taken as background sample� Control Sample used: B→[Kππ0]π

Event Reconstruction

�Reconstructed signal D by identifying K, π, π0

� D combined with K or π to form B→DK or B→Dπ

�We detect both suppressed B decay as well as favored B decay

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Event Selection• Impact parameter (dr) in the x-y plane: |dr| < 0.2 cm• Impact parameter (dz) along z axis: |dz| < 1.5 cm• Pion selection: L(K/π) < 0.6• Kaon selection: L(K/π) ≥ 0.6• Eγ > 50 MeV• Pπ0 > 0.4 GeV/c• D0 reconstruction mass: |M(Kππ0) – 1.865 GeV/c2| <

0.015 GeV/c2

• ∆E = Ebeam –E*B : [-0.2, 0.2] GeV

• Beam constrained mass (Mbc): [5.2, 5.29] GeV/c2

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Multiple Candidate Selection

22

2

0

0

−+

−=

bc

Bbc

K

DK MMMM

σσχ

ππ

ππ

bcσ

2χOne Candidate per event selected on the basis of mi nimum defined as:

0ππσKWhere = 2.74 MeV/c 2 and = 10.5 MeV/c 2 are taken as errors of

and bcM

0ππKM

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Selection Performance

• After best candidate selection the efficiency of signal = 10.12 %

• Using Truth Matching, fraction of correctly reconstructed events ≈ 85 %

• Incorrectly reconstructed events ≈ 15 %• Incorrectly reconstructed π0 ≈ 93.7 %

• Incorrectly reconstructed D 0 ≈ 6.3 %

• incorrectly reconstructed B < 0.1 %

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Discussion of vetoes

• D*± veto

• ∆M = D*± - D0 used to suppress background from D* ± →Dπ±

• Applying ∆M > 0.15 GeV/c2 , loss of signal efficiency ≈ 0.5 %

Distribution of ∆M for signal (red) and charm background (magenta)

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• Double miss-ID

•The decay mode causes a p eaking background for mode

• We applied cut:

+−++ → KKDB )( 0ππ++−+ → KKDB )( 0ππ

220 /020.0|/865.1)(| cGeVcGeVKM exchange >−ππ

• Single miss-ID

++ → KDB )( 0πππ• Another possible background comes from the decay mode

• We reject such events by applying cut:220 /020.0|/865.1)(| cGeVcGeVM >−πππ

• Loss of signal efficiency due to both the requireme nts is around 0.15%

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Mbc & ∆E Distributions

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Mbc & ∆E Distribution for generic MC in favored mode

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Mbc & ∆E Distribution for generic MC in suppressed mode

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Continuum Suppression• e+e-→ΥΥΥΥ(4S) → (Integrated Luminosity = 711 fb-1) are spherical in

shape

• Backgrounds from e+e- → continuum, looks jet like

• For improving qq suppression we use 9 parameters as input to Neural Network

BB

qq

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Neurobayes Input:

1. LR(KSFW)

� LR approach to discriminate between signal and background using KSFW package

� KSFW variable :

— signal

— uds

— cc

— uds+cc

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2.� Cosine of angle between B-flight direction and

beam axis

� Signal:

� Background: Flat

Bθcos

θ2cos1−

— signal

— uds

— cc

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3.� Cosine of angle between thrust axis of B

decay and remaining events

� Signal: Flat

� Background: Strong peak at 1

|cos| Tθ

— signal

— uds

— cc

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4.� Vertex separation between reconstructed B

and tag-side B

� Signal: wider since B meson has longer life time

� Background: narrower

z∆

— signal

— uds

— cc

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5. |qr|� Absolute value of B flavor tagging information

� Signal: events near 1

� Background: very less events near 1

— signal

— uds

— cc

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6. ∆Q� Charge difference between sum of charges of

particles in D hemisphere and the one in the opposite hemisphere

� Signal: < ∆Q> ~ 0

� Background: < ∆Q> ≠ 0— signal

— uds

— cc

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7. QBQK� Product of charge of B candidate and the sum

of charges of all K candidates not used in B reconstruction

� KID > 0.6

� Signal: events tends to have Q BQK < 0

� Background: More events Q BQK = 0

— signal

— uds

— cc

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8.

� cosine of angle between daughter K direction and opposite direction to B in D rest frame

KDθcos

— signal

— uds

— cc

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9.

� cosine of angle between daughter D direction and opposite direction to Υ(4S) in B rest frame

DBθcos

— signal

— uds

— cc

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Neural Network Method

� Multivariate algorithm used for analysis of correlated data

� Very good tool to maximize separation between signal and background

� Calculated output as a function of all nine variables as NN input

� Input layers combined with preprocessors to give better classification between signal and background and make output stable

� Output is determined from MC to separate signal from background

� Output is used to optimize figure of merit (S/√S+B)

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Training Output

� Training is used to obtain output to get larger separation between input and output

� NN well trained ↔ Signal loss minimized

Each sample contains 100,000 events

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Test:

NN Output for signal MC

NN Output for uds MC NN Output for cc MC

To reject over training we obtain output for independent samples using results of training

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Significance of variables used for training

Each Sample contains 37,000 events

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To do:

�Perform training with higher statistics

�Optimize cut on NN output

�Signal extraction by applying fit on ∆E & NB

�Check for B- → Dπ-

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THANK YOUTHANK YOU