1. Measurement of B + K + νν David Doll Ilya Narsky David Hitlin 1 California Institute of Technology Department of Energy Review July 24, 2007

2. Theory These FCNC (Flavor Changing Neutral Current) decays proceed via Z- penguin or W-box diagrams within the Standard Model, and are consequently quite suppressed. SM predicts a Branching Fraction of 3.8 x 10 -6. Several other theories (multi-Higgs doublet model, minimal super- symmetry (SUSY), etc.) imply an increase in b svv, which invites an investigation of this channel. 2 Caltech DOE ReviewD. Doll July 24, 2007

3. Previous Analyses For This Mode Semileptonic tag: B (K + νν)<7.0*10 -5 Hadronic tag : B (K + νν)<6.7*10 -5 Combined: B (K + νν)<5.2*10 -5 Previous BABAR results (BAD 738, 2004) Most Recent Belle results, as of May (FPCP 2007): 3 Caltech DOE ReviewD. Doll July 24, 2007

4. Rectangular Cuts We used the BumpHunter package included in StatPatternRecognition (I. Narsky) to achieve a set of Rectangular Cuts that optimizes the Punzi Figure of Merit (, Phystat 2003) We are using Runs 1 through 5 data (319 fb -1 ) We are using the semileptonic tagged events, with the D 0 decaying via Kπ, Kπππ, Kππ 0 B+B+ ν ν K+K+ B+B+ D0D0 l+l+ ν 4 Caltech DOE ReviewD. Doll July 24, 2007

5. Cuts 5 Number of Charged Tracks on the Signal Side = 1 SignalBackground Remaining Neutral Energy < 0.365 Signal Background Also: Netq<1.5 P* lep >1.24 GeV/c P K >1.79 GeV/c R2All <0.50 E total >4.4 GeV E miss > 3.32 GeV Expectations Based on MC: Signal : 1.65 events Background : 28.2 events Caltech DOE ReviewD. Doll July 24, 2007

6. Multivariate Classifier We are going to try to improve our results by using the multivariate classifier Random Forest with 36 input variables, also included in the StatPatternRecognition software package. Early results from runs 1 and 2 showed a clear improvement with the Random Forest, we are now working on runs 1 to 5. 6 Caltech DOE ReviewD. Doll July 24, 2007

7. Double Tag Sample With the more complex multivariate classifier, we a need a more powerful way of estimating uncertainties. The Double Tag Sample consists of those events in which both sides decay via the D 0 l + ν channel. The strategy is to reconstruct one side (the usual semileptonic tag side), then substitute the particles on the second tag side to match the signal : D 0 =>K +, l + =>ν This gives us a very signal like control sample in the data to help evaluate the uncertainties in our classifier. 7 Caltech DOE ReviewD. Doll July 24, 2007

8. Double Tag Sample Checks: Because of the differences between the particles in the double tag sample and in the signal, several of the double tag variables needed to be ‘tweaked’ so that they resemble their Kνν counterparts. Also it was important that we see similar correlations between variables. 2 nd Fox- Wolfram moment (R2All) Signal MCDouble Tag Data 8 Caltech DOE ReviewD. Doll July 24, 2007

9. Double Tag vs. Signal cut efficiencies As a further important check, we took the cuts from the Bump Hunter, and compared the efficiencies of each cut between Sig. Monte Carlo, and the Double Tag Sample. CutSig. MCSig. MC eff.Double Tag Data Double Tag eff. Double Tag MC Double MC eff. (just B+B- MC) Initial Size29895144901130841 Ntrkleft and pstarlep redundant 29895144901130841 R2all264340.88438070.848112410.859 Etotal240260.804 (0.909)35150.783 (0.923)103300.789 (0.919) Ptrkleft139240.466 (0.579)20280.452 (0.577)61840.473 (0.599) Emissevt131440.44 (0.944)18270.407 (0.901)55920.427 (0.904) Etotleft107350.359 (0.817)1638.365 (0.897)5088.389 (0.910) 9 Caltech DOE ReviewD. Doll July 24, 2007

10. The Next Steps Apply the Random Forest algorithm to runs 1 through 5 MC Unblind the Data for both the Random Forest and the Rectangular Cuts. Evaluate the systematic uncertainties using the double tag sample. 10 Caltech DOE ReviewD. Doll July 24, 2007