Possibili sviluppi futuri per lamisura di V � �

Alessio Sarti @ BaBar Italia

INFN and University of Ferrara

Capri 11-04-2003

Alessio Sarti @ BaBarIt Capri – p.1/15

PresentStatus

The present BR result is:��� ��� � �� � � � � �

�� � � � � �

B

�� � ��� �� ������� � � ! � � " ! � #$ ! � #% �'& $ ! ( )* +� , * -� � � � . � � � �

� - � � � / 0 21 #Dominant contribution to sys error comes

from uncertainty on 34 parameter

(Fermi motion; De Fazio - Neubert)

Source

5� ��

MC statistics 4.5

Tracking efficiency 1.0

Photon resolution 4.7687 interactions 1.0

Electron identification 1.0

Muon identification 1.0

K

9identification 2.3�;: <=> composition, ?@ A fits 3.8

Binning effects 1.2B C DFE � B � E 3.0G

(Detector systematic error) 8.7

Alessio Sarti @ BaBarIt Capri – p.2/15

Closer look at theosyserror

Source

5� ��

Modeling of

� � �= �HI� 4.4

Hadronization error 3.0, � � � H� branching fractions 2.8, � � � H� with ssbar contents 3.7G(Modeling of

� � �;� � H� ) 5.5

Theoretical error (

J

and

K8L ) 17.5

Summary (sys on

MN O 4 M

):

Detector + MC modeling sys: 6.9%

Theo sys: 10.4% (dominant)

How to reduce theo sys?

Alessio Sarti @ BaBarIt Capri – p.3/15

Possiblefutur escenarios

Given different lumi scenarios: 80fb

1 $

, 160fb

1 $240fb

1 $:

Pure statistical error will drop by a

P Q R

factor.

MC statistical error can be reduced at will and SP5 will be validated soon

At present: detector and

SUT V S� contributions to sys error are

’subdominant’

How to reduce theo sys error?

Use of additional ( W� ) cut (?X

5% error)

Constraint on Y , directly from our data (?

X

5% error)

Change model forZ� , extraction (Ciuchini et al) (?

X

5% error)

Alessio Sarti @ BaBarIt Capri – p.4/15

[

cut: MC study of � \^] _Fitted

` � a C D variation vs b c cut

cut2q0 2 4 6 8 10 12 14

-0.01

0

0.01

0.02

0.03

0.04

0.05

scan2q

bin:0.0014st1

bin:0.0073th11

scan2q

Statistical error increases (*5 7)!

de f e sys variation

cut2q0 2 4 6 8 10 12 14

0.05

0.1

0.15

0.2

0.25

tot∈

error = 150 MeVbM var: -0.168∈

tot∈

Sys error ( g ,) is reduced (-16%)

h

Fitting tecnique may be not adequate for high i j cuts:3lk higly affected

Statistical error extraction is not meaningful

Alessio Sarti @ BaBarIt Capri – p.5/15

[

cut: Fit failur es?

mk distribution heavily depends on i j cut: default and i j=10GeVj

fit are shown

Mx(GeV)0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

0

50

100

150

200

250

300

350

400

450

data events

b->ulnub->clnuotherdata

data events

Mx(GeV)0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

0

50

100

150

200

250

300

350

400

450

data events

b->ulnub->clnuotherdata

data events

Mx(GeV)0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

-20

0

20

40

60

80

100

data events subtracted

scaled MC

data subtr.

data events subtracted

Mx(GeV)0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

0

20

40

60

80

100

120

140

160

data events

b->ulnub->clnuotherdata

data events

Mx(GeV)0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

0

10

20

30

40

50

60

70

80

data events

b->ulnub->clnuotherdata

data events

Mx(GeV)0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

0

10

20

30

40

data events subtracted

scaled MC

data subtr.

data events subtracted

Alessio Sarti @ BaBarIt Capri – p.6/15

[

cut: results

b c cut method can be tested looking at stat + sys error

(stat error computed in b c bins looking at events with ?m n $� o o)

cut2q0 2 4 6 8 10 12

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

)-1

error = 150 MeV (160fbbM

Sys error

Stat + sys

)-1

Sys Error + Stat(160fb

L = 160fb

p qh

Stat error needs to be estimated in

proper way

cut2q0 2 4 6 8 10 12

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

)-1

error = 150 MeV (240fbbM

Sys error

Stat + sys

)-1

Sys Error + Stat(240fb

L = 240fb

p qh

With high statistics this method can be

competitive

Alessio Sarti @ BaBarIt Capri – p.7/15

Constraining �

(hep-ex:)

Looking at transitions: b

�

ul� we have:

r sut v r s� � r suw x ? c t 1 � � r sut r w s � v y c w z !

Switching to b C.o.M.:

? c t1 � ? t { w v y c w z ! x ? t z { w v | r w |were ? t is the pole mass.34 can be extracted by a direct

measurement on data and used to

constrain Fermi Motion sys.�

How ? t measured is related to Fermi

Motion parameters?�

Possible a direct fit on data (how to control

biases)?�

Tried a } c fit

2 2.5 3 3.5 4 4.5 5 5.5 60

50

100

150

200

250

300

M(b) mbtmpGB

Nent = 2510

Mean = 4.768

RMS = 0.3396

M(b) mbtmpGB

Nent = 2510

Mean = 4.768

RMS = 0.3396

| { w v r w |

at generator level.

Alessio Sarti @ BaBarIt Capri – p.8/15

~ ~ [

fit

Tried a

5 } c � $

extraction method against ?m and

| { w v r w |distributions

-0.5 -0.4 -0.3 -0.2 -0.1 08

10

12

14

16

18

mxgen

} c s�c�a�n�Mx(GeV)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-0.4

-0.3

-0.2

-0.1

-0

0.1

0.2

0.3

0.4

0.5

0.6

data events subtracted

= 9.4612χ

= 4.684bm

data events subtracted

-0.5 -0.4 -0.3 -0.2 -0.1 -015

16

17

18

19

20

21

22

mqbgen

} c s�c�a�n�

(GeV)bm4 4.2 4.4 4.6 4.8 5 5.2 5.4

0

0.1

0.2

0.3

0.4

0.5

0.6

data events subtracted

= 16.3952χ

= 4.65bm

data events subtracted

Green line shows

��� j�� �

intersection (

��� deviations)Alessio Sarti @ BaBarIt Capri – p.9/15

Constraining � (cont’d)

Constraint on

�� sys from*� � ��� � * measurement.

Sys reduced if � � g , � �

40-60 MeV

Direct fit has stat power (bias

needs to be under control)��� �

fit gives � � g , � � 120

MeV

Alessio Sarti @ BaBarIt Capri – p.10/15

Ciuchini et al. Method

Under certain assumptions (hep-ph/0204140) and using a ’smart’

variables choice:

� � �g� � � � � �g� � � �0 � � � � � 0�� � �� � � (1)

and looking at ratio of differential BRs:

* +� ,+T , *� �   �¢¡ £ �

¤� ` C D¦¥ �¤ §¤� `: ¨¤ª© * © � §� « � � � ¡ £ � (2)

  �¡ £ � � � 0 � ¬ - � � 0 � � / 0 21 #

« � � � ¡ £ � is small and shape function has been factorized away!

Alessio Sarti @ BaBarIt Capri – p.11/15

C.e.a.variables

0

50

100

150

200

250

300

csiCiuc data events after all cuts: enrichedh400000Nent = 0 Mean = 0.804RMS = 0.1211Under = 0Over = 0Integ = 1393

= 0.57472χ

csiCiuc data events after all cuts: enrichedh400000Nent = 0 Mean = 0.804RMS = 0.1211Under = 0Over = 0Integ = 1393

0 0.10.20.30.40.50.60.70.80.9 10.5

1

1.5

0

100

200

300

400

500

600

700

800

csiCiuc data events after all cuts: depletedd400000Nent = 0 Mean = 0.7821RMS = 0.1206Under = 0Over = 0Integ = 3320

= 1.05962χ

csiCiuc data events after all cuts: depletedd400000Nent = 0 Mean = 0.7821RMS = 0.1206Under = 0Over = 0Integ = 3320

0 0.10.20.30.40.50.60.70.80.9 10.5

1

1.5

0

50

100

150

200

250

300

wCiuc data events after all cuts: enrichedh400000Nent = 0 Mean = 1.057RMS = 0.2631Under = 0Over = 0Integ = 1394

= 1.13662χ

wCiuc data events after all cuts: enrichedh400000Nent = 0 Mean = 1.057RMS = 0.2631Under = 0Over = 0Integ = 1394

00.20.40.60.811.21.41.61.822.20.5

1

1.5

0

100

200

300

400

500

600

700

wCiuc data events after all cuts: depletedd400000Nent = 0 Mean = 1.102RMS = 0.2187Under = 0Over = 0Integ = 3305

= 0.49422χ

wCiuc data events after all cuts: depletedd400000Nent = 0 Mean = 1.102RMS = 0.2187Under = 0Over = 0Integ = 3305

00.20.40.60.811.21.41.61.822.20.5

1

1.5

0

50

100

150

200

250

xCiuc data events after all cuts: enrichedh400000Nent = 0 Mean = 0.7317RMS = 0.2196Under = 0Over = 0Integ = 1387

= 1.04682χ

xCiuc data events after all cuts: enrichedh400000Nent = 0 Mean = 0.7317RMS = 0.2196Under = 0Over = 0Integ = 1387

0 0.20.40.60.8 1 1.21.41.61.8 20.5

1

1.5

0

100

200

300

400

500

600

xCiuc data events after all cuts: depletedd400000Nent = 0 Mean = 0.7049RMS = 0.2076Under = 0Over = 0Integ = 3318

= 0.71002χ

xCiuc data events after all cuts: depletedd400000Nent = 0 Mean = 0.7049RMS = 0.2076Under = 0Over = 0Integ = 3318

0 0.20.40.60.8 1 1.21.41.61.8 20.5

1

1.5

Alessio Sarti @ BaBarIt Capri – p.12/15

C.e.a.(cont’d)

Possible problems/future tests:

Method allows to recompute

  �¢¡ £ � for any given upper cut ong � : how to deal with low g � resonances? A lower cut on g � isprobably needed (and analysis results should be tested againstg � cut changes)

If lower cut on g � applied ( ­¯® °): statistics is reduced � 50%.

Can be competitive with lumi± 0 � �² 1 $

Higher twists terms can have large contributions: ’flatness’ of ratio

of partial BRs needs to be checked (equality should hold for any� �³ �� � �ª� )

Alessio Sarti @ BaBarIt Capri – p.13/15

Unfolding

Fig.105 MOCA input distribution and spline fit0

500

1000

1500

2000

2500

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

ID 105

Fig.104 User function USFUN(X)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

ID 104

Fig.106 Unfolding distribution f mult(x)0

500

1000

1500

2000

2500

3000

3500

4000

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

ID 106

Fig.501 Measured variable Y(1)0

20

40

60

80

100

0 0.5 1 1.5 2 2.5 3 3.5 4

ID 501

Tried unfolding method

from Blobel

(hep-ex/02008022)

Working on subtracted

data

upl;lowl True m ´ ; Fitted f µ·¶¸¹

upr;lowr Extracted f¸ º »½¼ ¾¿ ; Fitted

data

Needs to work on it: test

dependencies on binning,

orthogonal functions used

in fit, . . .

Code is in fortran: need to

work with ASCII files

Alessio Sarti @ BaBarIt Capri – p.14/15

Conclusions

ÀÁ PRL ready for coll wide review!

Statistical error (80

ÂÃÅÄ Æ

): 6%

Theo sys error dominates: 10.4%

A cleaner theo way to extract

ÇÉÈ Ê is needed (aiming a 5% error).

Under study:

Combination of Ë Ì and ͽΠcut

Í Ê constraint on analysis data

Ciuchini et al. method

Unfolding (low priority)

Alessio Sarti @ BaBarIt Capri – p.15/15