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David Doll 1. Outline Thesis topic: b →sγ Motivation Previous Analysis Babar overview Subdetector introduction and impact on thesis Previous work: Introduction.

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Presentation on theme: "David Doll 1. Outline Thesis topic: b →sγ Motivation Previous Analysis Babar overview Subdetector introduction and impact on thesis Previous work: Introduction."— Presentation transcript:

1 David Doll 1

2 Outline Thesis topic: b →sγ Motivation Previous Analysis Babar overview Subdetector introduction and impact on thesis Previous work: Introduction to Random Forest method Applications to thesis topic and plans 2

3 b →sγ Motivation Flavor changing neutral current decay (absent at tree level in SM) Precision test of SM Different models may enhance or suppress the BF γ Source: U. Haisch, FPCP Conference Taipei, 2008 3

4 For photon energy cut E γ > 1.6 GeV in B meson rest frame NNLO theoretical calculation HFAG experimental results (as of March 15, 2007) b →sγ Motivation Combined statistical and systematic error Systematic uncertainty associate with E cut = [1.8,2.0] Error associated with subtraction of events Source U. Haisch FPCP Violation 2008 4

5 b →sγ Motivation Photon energy spectrum In b quark rest frame (impossible to boost to), this would be a delta function (≈m b /2) b quark motion within meson smears this spectrum Spectral shape dependent on modeling of spectator quark In the framework of HQET, the parameters λ 1 and (or equivalently m b ) may be determined from the first two moments of the spectrum Beyond SM theories are not predicted to influence this much 5

6 Different Photon Models Shape function of b quark motion is universal Applicable to all decays involving tranistions to massless states (B →X s γ, B → X d γ, etc.) Different shape function models exist A.L. Kagan and M. Neubert propose an exponential shape function (KN model): where: Source Eur. Phys. Jour. C7, 5- 27 (1999) Gaussian Ansatz 6

7 Dependence on E cut 7 D. Benson, I.I. Bigi, and N. Uraltsev also investigate an exponential and a Gaussian ansatz (with minimal difference between the two) Use purely perturbative spectrum calculated by Z. Ligeti, M. Luke, A.V. Manohar, and M. Wise as a starting point Add nonperturbative pieces to the energy moments Finally, they investigate the effects of the minimum photon energy cut to evaluate a bias in m b and μ π 2 Complete bias Without perturbative corrections difference Source D. Benson, I.I. Bigi and N. Uraltsev FPCP Violation 2004

8 Other Photon Models B. Lange, M. Neubert, and G. Paz also present shape functions based on exponential, gaussian, and hyperbolic functions (hep-ph/0504071) They detail how to fit the parameters: 8 First and second moments are directly relatable to and μ π 2 and: Is a good model for Exponential, gaussian, or hyperbolic

9 Search Strategy for b →sγ Performing a sum of exclusive states 38 states total Update of former analysis published in Phys. Rev. D (2005) 052004 Based on 89.1 fb -1 of data collected at the Y(4S) 9 Source Babar doc

10 On CP Asymmetry Measurement Because of the final state reconstruction, a direct CP asymmetry measurement is possible with this strategy in modes of definite flavor (yellow) Recently investigated with ~80% of the total data Possible source of other new physics Search to be performed in another analysis 10 Source Babar doc

11 Former Analysis Procedure Reconstruct event candidates into the 38 different decay modes Use a Neural Network to reject continuum events based on event shape variables Set the lower cutoff energy at E γ > 1.9 GeV (or equivalently at M Xs between 0.6-2.8 GeV/c 2 ) to limit peaking B background Choose the ‘best’ candidate as the one that minimizes Δ E 11 Source Babar doc

12 Subtract off the continuum, generic BB, and cross-feed backgrounds by fitting the beam substituted mass, m ES and fit to signal on bin-by-bin basis in M Xs The peaking background contribution (cross-feed and generic BB) is fit with a Novosibirsk function: The continuum contribution is fit with an ARGUS function As a default signal model, they use the KN exponential model with m b = 4.65 GeV/c 2 and λ 1 =-0.30 GeV 2 /c 4 Kagan and Neubert recommend treating only the range between M Xs ={1.1 GeV/c 2, 2.8 GeV/c 2 } as a non-resonant spectrum Below M Xs = 1.1 GeV/c 2, they recommend using K*γ MC below 1.1 GeV/c 2 12 Former Analysis Procedure

13 Bin-by-bin fit to signal gives Partial Branching Fractions, PBF(M Xs ) Need to correct PBF(M Xs ) for fractional coverage of inclusive b →sγ decays to get Total Branching Fractions, TBF( M Xs ) Convert TBF(M Xs ) to TBF(E γ ) Fit TBF(E γ ) to different expected models, allowing extraction of inclusive Branching Fraction measurement to lower E γ Also extract shape function paramters, m b and λ 1 from model fit 13 Former Analysis Procedure

14 Impact of competing models Ideally, photon spectrum measurement is model independent Analysis strategy forbids this Need idea of total decay coverage in each M Xs bin Not able to extrapolate to a total BF without introducing some model dependencies Use a sample with a flat E γ distribution to reweight to any model chosen Measure parameters in all models considered (everyone’s happy) 14

15 Results of Former Analysis Quote results of KN fit, ‘kinetic’ model (BBU from above), and ‘shape function’ models (average of 3 BNP shapes from above) 15 Source Babar doc

16 PEP-II at SLAC e - (at 9.0 GeV) on e + (at 3.1 GeV) CM energy = 10.58, the mass of the Υ (3S) Lorentz boost of βγ = 0.56 B meson lifetime 1.5-1.6 ps → Δz ≈ 250-270 μm Turned off in April with a total of ~485 fb -1 at or just below the Υ(4S). Source Babar Doc 16

17 Babar Detector DIRC SVT DCH EMC IFR Solenoid Magnet (1.5 T) e + e - 17

18 Subsystems Overview SubsystemMeasured quantityMethod Silicon Vertex Tracker (SVT) Particle Tracking, Vertex Location, dE/dx Double sided silicon strips Drift Chamber (DCH) Particle Tracking, dE/dx Sense wires in helium- isobutane gas mixture Detector of Internally Reflected Cherenkov light (DIRC) Particle ID for particles of momentum greater than 700 MeV/c Cherenkov light measured on PMTs Electromagnetic Calorimeter (EMC) Energy, Shower Shape CsI(Tl) crystals, read out by Si Photo-diodes Instrumented Flux Return (IFR) Penetration, Shower Shape Streamer detection 18

19 SVT and DCH SVT 5 layers of double-sided silicon strip sensors φ measuring strips parallel to the beam, z measuring strips perpendicular to the beam 20-40 μm resolution in all 5 layers. DCH 7,104 small drift cells arranged in 40 cylindrical layers dE/dx measured by total charge deposited in each cell Source Babar doc SVTDCH 19

20 DIRC Particle ID for particles with momentum above 750 MeV/c 144 fused, synthetica silica bars arranged in a 12-sided polygon Readout by 11,000 PMTs Source Babar doc 20

21 IFR Segmented steel flux return (later also brass), instrumented in gaps Originally used resistive plate chambers (RPC) to detect streamers from ionizing particles Upgraded to limited streamer tubes (LST) starting in 2004 Muon efficiency Pion mis-id rate RPC data LST data Source Babar doc. 21

22 EMC Designed to operate over the energy range of 20MeV to 9GeV 6,580 CsI(Tl) crystals separated into 5,760 in the barrel, and 820 in the endcap 16.1 X 0 in the backward half of the barrel, to 17.6 X 0 in endcap Each crystal read out by two 1cm 2 Si photodiodes Calibration at low energy using a 6.13MeV photon source and at high energies using Bhabha events Studies of the low energy calibrations have shown light yield falloff to total around 8% or less after the run of the experiment (depending on crystal manufacturer). Angular resolution vs photon energy Source Babar doc. 22

23 B + →K + νν (or the benefits of a multivariate classifier) Performed search with D. Hitlin, I. Narsky, and B. Bhuyan Also a FCNC, and therefore highly suppressed in the SM 23 Standard Model BF Experimental Limit on BF (90% CL) arXiv:0708.4089v2 [hep-ex]

24 Analysis Procedure, Tagging Perform a ‘semileptonic’ tagged analysis Fully reconstruct the ‘tag B’ in the decay Look at the rest of the event for our signal 24 Tag BSignal B

25 Analysis Procedure, Cuts Separately pursued two different techniques to suppress background Standard Rectangular Cut method More sophisticated Multivariate technique with a Random Forest For Rectangular Cuts, separated the Monte Carlo (MC) into 3 sets: train, valid, test; in a 2:1:1 ratio Optimized the ‘Punzi’ Figure of Merit: 25 Where S is the number of signal, N σ is the sigma level of discovery, and B is the number of background

26 Rectangular Cut Results 26


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