Preliminary results for the BR(KS

M. Martini and S. Miscetti

Talk Layout

• Short summary of strategy for the measurement

• DATA-MC QCAL calibration

• Signal/background fit repeated in different conditions: - with cos() - without cos()

• Determination of efficiency for the signal

• Study of normalization sample

• Preliminary estimate of BR

• First look at background shapes

• Plans/prospects

Data sample and preselection• We have analized 1.6 fb-1 of DATA (2001-2002-2004 and part of 2005 sample). 400 pb-1 still missing on 2005.

• whole production of neukaon MC 2001-2002 used for the bkg (450 pb-1 )

• ksr04 used for the signal

• Started using the preliminary sample 100 pb-1 of the 2004 MC. Not yet for shapes .. Checking rates only.

• From NA48 results: BR(KS) = 2.78x10-6, we expect to have tagged 1550 signal events with Kcrash.

• Kcrash events

• Preselection: consists of requiring 2 “and only 2” prompt photons with E>7 MeV, cos() < 0.95 and T<min(5, 2 ns)

• Qcal veto

DATA

BKG

Example fit 2d chi2 ...

- KS tagged from Kcrash (122x106 events)- 2 prompt photons required (496000 events)

The major background is constituted by KS20 with 2 lost photons.

To disentangle signal from background we use:

- Kinematic fit (2<20)

- We then look at the scatter plot M vs , where:

- Opening angle between the two photons in the KS cms - Reconstructed mass

DATA

SIG

Example fit 2d chi2 ...

- KS tagged from Kcrash (122x106 events)- 2 prompt photons required (496000 events)

The major background is constituted by KS20 with 2 lost photons.

To disentangle signal from background we use:

- Kinematic fit (2<20)

- We then look at the scatter plot M vs , where:

- Opening angle between the two photons in the KS cms - Reconstructed mass

2001 2002

ev ev real fraction ev ev real fraction 2 47389 47389 56483 56483

ALL 3 9099 1819800 0,252505 10742 2148400 0,24836994 4 25115 5023000 0,696961 30519 6103800 0,70564162 5 1821 364200 0,050534 1989 397800 0,04598844

2 16178 16178 0,341387 19212 19212 0,34013774

QCAL 3 4959 991800 0,545005 5991 1198200 0,557717374 22633 4526600 0,901175 28536 5707200 0,935024085 1416 283200 0,777595 1693 338600 0,8511815

• After splash filter

• events with 3, 4, 5 prescaled of 400

• QCAL (-5 < Tqcal < 5) ns

Data vs MC: QCAL rates (2001-2002)

DT=Tqcal-Rqcal/c (ns)

2004 2005ev ev real fraction ev ev real fraction

2 230600 230600 161680 161680 ALL 3 42750 8550000 0,250132 28970 5794000 0,2502808

4 120730 24146000 0,706395 81680 16336000 0,7056587 5 7430 1486000 0,043473 5100 1020000 0,0440605

2 45730 45730 0,198309 34640 34640 0,2142504

QCAL 3 16641 3328200 0,389263 11132 2226400 0,38425964 97809 19561800 0,810147 64958 12991600 0,79527425 5466 1093200 0,735666 3672 734400 0,72

• After splash filter

• events with 3, 4, 5 prescaled of 400

• QCAL (5 < Tqcal < 35) nsDT=Tqcal-Rqcal/c (ns)

Data vs MC: QCAL rates (2004-2005)

Extraction of losses...

-We defined two windows, early and late in T:

- Early: (-50 ; -40) ns - Late: (70 ; 80) ns

anno gamma All Splah Err Splah Early Err Early Late Err Late Fearly Fearly (%) Err Fearly Mean Err Mean2001 2 47676 47384 217,6787 3189 56,471 2780 52,7257053 0,067 6,730 0,123123 6,690872 0,0935342001 3 11287 9099 95,38868 626 25,02 556 23,5796522 0,069 6,880 0,2842772001 4 25714 25115 158,4771 1646 40,571 1534 39,1663121 0,066 6,554 0,166752001 5 1938 1821 42,67318 141 11,874 103 10,1488916 0,077 7,743 0,6768532002 2 56809 56483 237,6615 1948 44,136 1827 42,7434205 0,034 3,449 0,079477 3,302367 0,059082002 3 11455 10742 103,6436 332 18,221 323 17,9722008 0,031 3,091 0,1722242002 4 30737 30519 174,6969 956 30,919 953 30,8706981 0,031 3,132 0,1028862002 5 2049 1986 44,56456 68 8,2462 46 6,78232998 0,034 3,424 0,4222662004 2 231910 230500 480,1042 35420 188,2 36890 192,067696 0,154 15,367 0,087699 15,20027 0,0666692004 3 43895 42747 206,7535 6437 80,231 6776 82,3164625 0,151 15,058 0,2013242004 4 121190 120730 347,4622 18040 134,31 18630 136,491758 0,149 14,942 0,1192732004 5 7598 7433 86,21485 1117 33,422 1108 33,286634 0,150 15,028 0,482242005 2 162590 161680 402,0945 27090 164,59 28100 167,630546 0,168 16,755 0,109998 16,58384 0,0842592005 3 29801 28974 170,2175 4749 68,913 4988 70,6257743 0,164 16,391 0,2565972005 4 82007 81678 285,7936 13332 115,46 13850 117,686023 0,163 16,323 0,1524672005 5 5193 5095 71,37927 829 28,792 842 29,0172363 0,163 16,271 0,609352

TOT eventsAfter splash filter

Event in window

Ploss in window

N=2,3,4 weighted mean

Extraction of losses...

- For the moment we have only used the early window (we can use late fraction as systematic)

- since we have difference between 2001-2002 and 2004-2005 sample, we calculate different values of Ploss:

2001-2002 Ploss = (4.85 0.07)% 2004-2005 Ploss = (15.7 0.07)%

-QCAL is evaluated as:

QCAL(DATA) = 1 - Ploss

QCAL data/MC efficiency

- For each period all numbers with N=2,3,4 fit with the following stuff ...

- we calculate the ratio:

We found compatible value of R for the different DATA sample

2001 Ploss 0,669087 0,009353

Ng Ndata all Ndata veto Fdata QV Efdata QV Nmc all Nmc veto Fmc QV Efmc QV

2 47389 217,69 31211 176,6664 65,861276 0,480119 95598 309,1893 49670 222,8677 51,95715 0,2873823 9099 95,389 4140 64,34283 45,499505 0,852977 16607 128,8681 5755 75,86172 34,65406 0,530084 25115 158,48 2482 49,81967 9,8825403 0,207937 44661 211,3315 1321 36,34556 2,957838 0,082576

Qcal vetoed DATA Qcal vetoed MC

)(

)(

MCF

PlossDATAFR

QV

QV

QCAL data/MC efficiency results

Using the results on Ploss for the different DATA sample, we can extract the QCAL efficiency:

Sample QCAL(DATA)

2001-2002 (95.16 0.07)%

2004-2005 (84.23 0.07)%

MC checks for QCAL efficiency

QCAL rejected events:

1) ALL events2) Cos accepted events3) Cos rejected events4) Energy of accepted

events

MC checks for QCAL efficiency

QCAL survived events:

1) ALL events2) Cos accepted events3) Cos rejected events4) Energy of accepted

events

MC checks for QCAL efficiency

We still have events impinging the QCAL that survived QCAL cut.

We can improve the simulation studying these events.

cos>0 ; cos<0

Signal-background fit

2001-2002 sample

weights from fit

•• DATA-- MC all Signal Background

Signal-background fit

2004-2005 sample

weights from fit

•• DATA-- MC all Signal Background

Fit using costheta (preliminary)

cos

2001-2002 sample: comparison without and with cos in the fit

cos

2004-2005 sample: comparison without and with cos in the fit

cos

cos

Fit using costheta (preliminary)

2001-2002 sample: comparison without and with cos in the fit

cos

Fit using costheta (preliminary)

2004-2005 sample: comparison without and with cos in the fit

cos

Fit using costheta (preliminary)

Fit results and analysis efficiency

Sample 2001-2002 2004-2005

Nsig 143.920.1 462.8 34.7

(2)=(63.30.7)%

The analysis efficiency (2 cut after kcrash and acceptance selection) is the same for the two samples since up to now we have used MC 2001-2002 only. :

Signal Acceptance, Total Efficiency

Using KSR04 MC production, we evaluate the signal efficiency requiring KL-far events and counting events with N=2

Using the standard efficiency curves, we obtain:

ACC)(N=2) = 83.2 0.2stat (1)

The systematic error has been evaluated varying the cone (0.6, 0.7, 0.8) and using the maximum variation from (1):

ACC(N=2) = 83.2 0.2stat 0.1sys

tot (kcrash given) = (acc) * (qcal) * (ana)

For the moment statistics and systematics together.

Normalization sample

Kcrash with N=4, N=3,4,5 prescaled of 400.Splash filter applied.

Stability plot shown with N= 4

Sample Int. Luminosity

2001 158 pb-1

2002 189 pb-1

2004 748 pb-1

2005 527 pb-1

Kcrash counter stability (2001-2002)

2001 2002

2004 2005

Kcrash counter stability (2004-2005)

KS20 efficiency

Using a sample of 160 Kevents, extracted from 2001 and 2002 statistics, we calculate KS20 efficiency using events with a KLfar definition:

(N=2) = ( 65.0 0.02stat )%

Using the same method applied for the signal, we can evaluate a first systematics on this parameter:

(N=2) = ( 65.0 0.2stat 0.1sys )%

Kcrash Final normalization

Using KS20 efficiency, we can extract the number of Kcrash of the normalization sample

Total number of Kcrash: 159.8 x 106

We can compare this results with Ncrash obtained integrating N= 3, 4, 5:

Ncrash(3, 4, 5) = 159.5 x 106

Sample Ncrash

2001 15.83 x 106

2002 18.91 x 106

2004 74.58 x 106

2005 50.46 x 106

First BR estimate

TOT(2001-2002) = (50.1 ± 0.6)%

Ncrash = 34.7 x 106 BR(KS20) = (31.05 ± 0.14)%

BR(KS ) = 2.57 x 106

TOT(2004-2005) = (44.4 ± 0.5)%

Ncrash = 125.1 x 106

BR(KS ) = 2.59 x 106

Combined result:

BR(KS ) = (2.58 ± 0.17) x 106

Fast simulation of Background

To study the fit uncertainty as a function of MC statistics we have developed a method based on “hit or miss”.The procedure is only based on MC signal and background.

Recipe:

- use the original 2d-distribution from sig and bkg, to create 2 smoothed distribution

- Use hit or miss to create N different distribution for signal and background for different MC statistics

- create a fake data distribution using sig and bkg from hit or miss with entries from fit

- repeat the fit procedure N times for each statical point.

Fast simulation of Background

Metti qualche plot di preparazione per hit or miss

Hit or miss

Signal and bkg statistical error as a function of the used MC statistics

Actual stat: Mcfact=1

Using twice MC statistics we can lower signal uncertainty of a factor 10%

Hit or miss

Stability of signal and bkg event as a function of MC used statistics.

Look at background shapes

DATA-MCcomparison for bkg enriched samples with 2 > 100, ?

Plans-prospects

• We need to study the systematics on the spectra shapes:

1) apply MC energy scale 2) calibration check with background dominated samples 3) calibration with KS20

4) effect of DATA-MC differences on QCAL efficiency

• We will process the few missing pb-1 of data and the 2004-2005 MC generated so far.• We are working on “fixing” the QCAL simulation to answer to point 4)• Study on the tag bias• Meeting with referees• Start writing documentation and planning for a pre-xmass blessing