1. A search for the rare decay B+ g K+nn
Steven Robertson
Stanford Linear Accelerator Center

2. Steven H. Robertson Stanford Linear Accelerator Center
Outline
Motivation
Hadronic B reconstruction
B+ g K+nn selection
Sideband samples and backgrounds
Results and future prospects
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

3. Steven H. Robertson Stanford Linear Accelerator Center
Introduction
Standard Model b g s nn process is a flavour-changing neutral-current process occurring via loop and box diagrams
Potential for significant enhancement from additional non-SM diagrams
Inclusive B g Xsnn is theoretically clean, but very difficult experimentally
Look instead for exclusive decay modes
Best published limit is from the CLEO experiment:
Br(B+ g K+nn)SM ~ 4 x 10-6
Br(B+ g K+nn) < 2.4 x at 90% CL
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

4. Steven H. Robertson Stanford Linear Accelerator Center
B+ g K+nn with BABAR
In B-factory environment, B decays are produced via
Current BABAR analysis based on 80.1 fb-1 data set
(86.9  1.0) x 106 BB pairs
e+ e- g Y(4S) g B+ B-
Need to determine that the Kaon is not accompanied by additional (charged or neutral) particles
Exclusively reconstruct hadronic B decays in order to identify tracks and clusters associated with the opposing B
Y(4S) B- B+ n K+
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

5. Steven H. Robertson Stanford Linear Accelerator Center
B reconstruction
Exclusively reconstruct B decay modes in hadronic final states:
ereco = (0.131   0.009)%
~ 114k reconstructed B mesons!
Energy
substituted mass:
difference:
B- g D0 X-
K- p+
K- p+ p0
K- p+ p- p+
B+ g K+nn simulation
X- system: up to three charged tracks (K, p) and two additional p0
Data and background simulation
mES (GeV/c2)
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

6. Steven H. Robertson Stanford Linear Accelerator Center
Event shape cuts
Reduce continuum backgrounds by exploiting topological differences between Y(4S) g BB and “continuum” events f e+ e- B
PB~320 MeV
Require angle between the thrust axes defined by the reconstructed B and by everything else to satisfy:
|cos qT|<0.8
Thrust magnitude:
|T| <0.925
(reject residual tt background)
B+ g K+nn simulation
Data and background simulation
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

7. B+ g K+nn signal selection
Signal “signature” is a single kaon and nothing else recoiling against the opposing reconstructed B
Require exactly one track, identified as a kaon, with charge opposite that of the reconstructed B
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

8. Kaon momentum spectrum
Buchalla et al. (hep-ph/ )
Faessler et al (hep-ph/ )
Phase-space
Signal kaon has a fairly hard momentum spectrum
Require PK >1.5 GeV to further reduce backgrounds
Differential decay rate (arbitrary units)
Effective q2 cut
nn invariant mass (q2/mB2)
Some theoretical uncertainty due to modeling of decay form factors
“New physics” also potentially has different spectrum
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

9. Calorimeter energy deposition
Limit the amount of calorimeter activity which is not associated with the reconstructed B decay products
Require no signal-side p0 candidates
Limit also the total extra calorimeter energy:
Eextra < 300 MeV
(Sum of all clusters with E>30MeV )
Eextra (GeV)
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

10. Steven H. Robertson Stanford Linear Accelerator Center
Missing momentum
Requiring low charged and neutral multiplicity enhances backgrounds from events with unreconstructed particles (i.e. outside of detector acceptance)
Require the event missing momentum vector to satisfy |cos qPmiss|<0.8
Signal-B selection efficiency
esig =(35.0 0.5  1.1)%
(Buchalla et al. model)
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

11. Steven H. Robertson Stanford Linear Accelerator Center
Control samples
Use data control samples from several “sideband” regions to validate MC modeling and background estimates
mES (and “large” mES)
Eextra >0.5 GeV
Ntrks=2
Ntrks=3
Various samples test different aspects of the analysis, e.g.
peaking vs combinatorial background
MC modeling of Eextra endpoint
Eextra (GeV)
“Blinding box”
Signal region
mES (GeV/c2)
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

12. Steven H. Robertson Stanford Linear Accelerator Center
Control samples
Eextra distribution in mES sideband
Dominated by continuum backgrounds
Onpeak data
Onpeak data
Offpeak data
Eextra (GeV)
Offpeak data
Eextra distribution in Ntrks=3 sideband
Dominated by peaking BB background
Eextra (GeV)
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

13. Steven H. Robertson Stanford Linear Accelerator Center
Control sample yields
All control samples consistent with Monte Carlo simulation at the level of the available statistics
Estimate combinatoric background in signal region by extrapolating mES sideband into signal region (adds an additional 1.0  0.4 background events)
MC/data type
Signal region
mES
Large mES
Eextra
Ntrks=2
Ntrks=3
B+B-
1.7  0.6
1.1  0.5
7.0  1.4
3.3  0.9
17.4  1.9
54.6  3.4
B0B0
1.4  0.6
0.6 0.4
0.9  0.5
3.5  1.0
uds
1.8  1.0
14.0  2.9
2.4 1.2
0.6 0.6
1.2  0.9 cc
11.1  2.6
1.9 1.0
3.1  1.4 tt
Onpeak data
Total MC
(80.1 fb-1) 3
2.7  0.7 7
4.8  1.7 31
33.5  4.2 10
8.8  2.0 21
20.7  2.3 55
62.4  3.9
Offpeak data
Continuum MC
(9.58 fb-1)
0.11 0.05
0.4  0.2 1
3.0  0.5
0.6  0.2
0.3  0.1
0.5  0.2
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

14. Steven H. Robertson Stanford Linear Accelerator Center
Results
Observe three events in signal region in 80.7 fb-1 of data consistent with expected background of 2.7  0.8
Overall selection efficiency of etot = (0.046 0.005)%
Limit computed using modified frequentist approach (Cousins & Highland)
Systematic uncertainties modeled in “toy” Monte Carlo by Gaussians
Limit set at branching fraction value at which 10% of toy experiments give less than observed number of signal candidates
Br (B+ g K+ nn) < 1.05 x10-4 at 90% CL
BABAR PRELIMINARY (March 2003)
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

15. B+ g K+nn (semileptonic reco)D0 B+ B- e- n
Previous BABAR analysis based on a semileptonic B- g D0 l- n X0 reconstruction sample
Search based on 50.7 fb-1 of data
Observed two events in signal region (treated as signal for limit determination)
expected background ~2.2
Eextra (GeV)
Br (B+ g K+ nn) < 9.4 x10-5 at 90% CL
BABAR PRELIMINARY
(Spring 2002)
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

16. Combined B+ g K+nn result
Analyses are statistically independent and so can be combined to give a single result:
Combined limit :
Semileptonic reconstruction:
Br (B+ g K+ nn) < 9.4 x at 90% CL (50.7 fb-1)
Hadronic reconstruction:
Br (B+ g K+ nn) < 10.5 x at 90% CL (80.7 fb-1)
Br(B+ g K+nn) < 7.0 x at 90% CL
Standard Model prediction: Br(B+ g K+nn) ~ 4 x 10-6
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

17. Conclusions and prospects
New B+ g K+nn search based on 80.1 fb-1 data set (87M BB pairs) using hadronic B reconstruction
Observe three events consistent with background prediction of 2.7  0.8
Combine this result with previous statistically independent result using semileptonic tags to give combined limit of
Expect significant increase in BABAR data set over next several years (factor of ~10 – 20 by 2009)
Add also B g K*nn and neutral B0 g K0(*)nn modes
Significantly increased sensitivity to b g s nn process
Br (B+ g K+ nn) < 1.05 x at 90% CL (Hadronic reconstruction)
Br (B+ g K+ nn) < 7.0 x at 90% CL (Combined limit)
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

18. The BABAR Collaboration
USA [35/276]
California Institute of Technology
UC, Irvine
UC, Los Angeles
UC, San Diego
UC, Santa Barbara
UC, Santa Cruz
U of Cincinnati
U of Colorado
Colorado State
Florida A&M
U of Iowa
Iowa State U
LBNL
LLNL
U of Louisville
U of Maryland
U of Massachusetts, Amherst
MIT
U of Mississippi
Mount Holyoke College
Northern Kentucky U
U of Notre Dame
ORNL/Y-12
U of Oregon
U of Pennsylvania
Prairie View A&M
Princeton
SLAC
U of South Carolina
Stanford U
U of Tennessee
U of Texas at Dallas
Vanderbilt
U of Wisconsin
Yale
The BABAR Collaboration
9 Countries
72 Institutions
554 Physicists
Italy [12/89]
INFN and U Bari
INFN and U Ferrara
Lab. Nazionali di Frascati dell' INFN
INFN and U Genova
INFN and U Milano
INFN and U Napoli
INFN and U Padova
INFN and U Pavia
INFN, SNS and U Pisa
INFN, Roma and U "La Sapienza"
INFN and U Torino
INFN and U Trieste
Norway [1/3]
U of Bergen
Russia [1/13]
Budker Institute, Novosibirsk
United Kingdom [10/80]
U of Birmingham
U of Bristol
Brunel University
U of Edinburgh
U of Liverpool
Imperial College
Queen Mary & Westfield College
Royal Holloway, University of London
U of Manchester
Rutherford Appleton Laboratory
Canada [4/16]
U of British Columbia
McGill U
U de Montréal
U of Victoria
China [1/6]
Inst. of High Energy Physics, Beijing
France [5/50]
LAPP, Annecy
LAL Orsay
LPNHE des Universités Paris 6/7
Ecole Polytechnique
CEA, DAPNIA, CE-Saclay
Germany [3/21]
U Rostock
Ruhr U Bochum
Technische U Dresden
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

19. Steven H. Robertson Stanford Linear Accelerator Center
The BABAR Detector
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

20. B+ g K+nn (hadronic tags)
Limit also the extra neutral energy:
Eextra < 300 MeV
Observe three events in the signal region
Predicted background: (2.7  0.8)
Overall efficiency: (0.046 0.005)%
B+ g K+nn simulation
Data
Data
Br (B+ g K+ nn) < 1.05 x10-4
at 90% CL
BABAR PRELIMINARY
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

21. Systematic uncertainties
Systematic uncertainty estimates derived from MC - data comparison
Dominated by MC statistics and tag B reconstruction efficiency
Source
se / e (%)
Signal MC statistics
4.9
Tag B yield 7
Track reconstruction
1.3
Kaon particle ID 2
Eextra modeling
MC generator model 3
Total 10
Source
s bg/bg (%)
Generic MC statistics 27
Tag B yield 7
Track reconstruction 5
Eextra modeling 2
Total 29
Limit computed using modified frequentist approach (Cousins & Highland)
Uncertainties modeled in “toy” Monte Carlo by Gaussians
Limit set as value at which 10% of toy experiments yield less than observed number of signal candidates
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

22. Steven H. Robertson Stanford Linear Accelerator Center
MC and data samples
Analysis based on Run1+2 data set (80.7 fb-1)
Use all available generic MC (VubRemoveOrphans for <10.3.1a)
MC type
Equivalent lumi (fb-1)
B+ g K+nn
254k events
B+B-
288
B0B0
277
uds
131 cc
130 tt
137
Onpeak data
80.07
Offpeak data
9.58
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

23. Semileptonic tag sample
Comparatively high statistics due to large b g c l n branching ratio
~5500 events per fb-1
Missing neutrino reduces available kinematic constraints
use kinematics of D0 - l combination:
Possibility of additional photons from D(*)0 g D0 g/ p0 feeding into signal channel B- n D0 L- K- p+
from Y(4s)
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

24. B+ g K+nn (semileptonic tags)
Based on 50.7 fb-1 of BABAR data
Will be updated to full data set in the near future
Signal region defined as a box in Eextra and the reconstructed invariant mass of the tag-side D0
Observe two events in signal region (treated as signal for limit determination)
expected background ~2.2
Signal region
Sideband region
Br (B+ g K+ nn) < 9.4 x10-5 at 90% CL
BABAR PRELIMINARY
(Spring 2002)
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center

25. K – p separation with the DIRC
pions
>3s K – p separation up to ~4 GeV
kaons
31 December 2018
Steven H. Robertson Stanford Linear Accelerator Center