Search for B + l + X with hadronic tagging method at Belle Experiment

Transcription

1 Search for + l + X with hadronic tagging method at elle Experiment Chanseok Park Yonsei University cspark@yhep.yonsei.ac.kr Feb 14th High KIAS-NCTS Joint workshop

2 Contents Motivation elle and KEK Hadronic tagging method Sample used for analysis Event selection Optimization Preliminary result Calibration in E ECL sideband Summary 2

3 Motivation + b W? l u + l + X X? - X : invisible, neutral, massive, spin 1/2 particle - We search for X in the mass range of GeV/c 2 - World first try-out 3

4 elle and KEK Data collected with elle detector at KEK asymmetric e + e - collider Total of 711 fb -1 of data collected at Υ(4S) 772M pairs e + : 3.5 GeV e - : 8.0 GeV elle detector KEK -Factory 4

5 elle and KEK Data collected with elle detector at KEK asymmetric e + e - collider Total of 711 fb -1 of data collected at Υ(4S) 772M pairs e + : 3.5 GeV e - : 8.0 GeV elle detector KEK -Factory 5

6 Hadronic tagging method : low momentum ~0.3 GeV/c Y(4S) e+e- (spherical) - e+e- collision at Y(4S) energy ¾ are continuum backgrounds - y requiring completely reconstructed meson at one side Continuum suppression e+e- (Jet-like) continuum Y (4S) - Very few wellreconstructed events E e+e- CM (GeV) 6

7 Hadronic tagging method - Signal lepton - Tagged Signal D K - Y(4S) Ex) D + (K - π + π + ) π - π - Invisible X >96% of Y(4S) with nothing else produced one -meson is completely reconstructed from known b c decays without ν efficiency is low, but purity is high Good way to reconstruct modes with invisible particle 7

8 Sample used for analysis Data ; 711fb -1 at Y(4S) resonance 772 Millions of meson pairs Signal MC mode Mass of X Amount + e + X 0.1, 0.2, 1.8 GeV 2,000,000 events for each mass of X + μ + X 0.1, 0.2, 1.8 GeV 2,000,000 events for each mass of X ackground MC Separately generated! Mode Process Amount Generic MC, qq 5 streams Rare b s, d, leptonic 50 streams Ulnu X u lν 20 streams eνγ + eνγ 1000 streams μνγ + μνγ 1000 streams π + K 0 + π + K streams π 0 eν + π 0 eν 300 streams π 0 μν + π 0 μν 300 streams 8

9 Event selection Particle Identity L e > 0.9 L μ > 0.9 Track quality Dz < 2 cm Dr < 0.5 cm Continuum suppression cosθ thrust < 0.9 for + e + X cosθ thrust < 0.8 for + μ + X E ECL Quality of tagged- meson ΔE < 0.05 GeV M bc > 5.27 GeV/c 2 O N > e p l sideband E ECL Sideband linded Region p l (GeV/c) E ECL : Remaining energy of ECL calorimeter (tagged- & signal lepton) p l : signal lepton s momentum in the signal rest frame 9

10 Event selection Fitting Signal Fitting ackground p l peak changes by mass of X p l cut should be optimized for each mass of X Fitting Peaking ackground 10

11 Optimization (obtaining Yield) G : Fit p l sideband extrapolate PDF G est Data side S( MC) S( MC) sig side Feldman-Cousins method 1. Relative uncertainty of ε sig 2. Estimated G and uncertainty 3. # of observed events POLE U.L. Uncertainty from PDG(F), PDF, systematic, etc 11

12 Optimization Mean of upper limit of branching fraction based on MC for each p l creteria + e + X M(X) : 1.8 GeV/c 2 + μ + X M(X) : 1.8 GeV/c 2 12

13 e mode Preliminary result μ mode elle preliminary elle preliminary Sideband Signal region elle preliminary + e + X M(X) : 1.8 GeV Sideband Signal region elle preliminary + μ + X M(X) : 1.8 GeV 13

14 Calibration in E ECL sideband + e + X + μ + X There are some disagreement between Data and MC, about p l > 2.2 GeV/c for E ECL sideband region(0.5 < E ECL < 1.0 GeV). Get Calibration Factor!! 14

15 Calibration in E ECL sideband 1.8 < p l < < p l < < p l < 2.3 E ECL cut : 0.5 < E ECL < 2.0 GeV (ecause we want more statistics) Data/MC ratio is fitted to linear function Ratio function : R(p l ) = p 0 + p 1 ( p l ) when p 0 and p 1 is parameter To fit well, we apply error to bins where no events (but MC exist) 15

16 Calibration in E ECL sideband Originally we use Data & MC ratio in p l sideband region to scale expectation of G So we use this ratio fitting function to scale G expectation. Calibration factor R* is used for scaling. We use ratio fitting function when fitting range 1.8 < p l < 2.65 GeV/c Old : G est Data side S( MC) S( MC) sig side New : G est R* Data side S( MC) S( MC) sig side 16

17 Summary * We search for + l + X, where X can be any invisible (and possibly massive) spin-1/2 particle. * We successfully suppressed background by help of hadronic tagging method. * In preliminary results, the upper limits are O(10-6 ) * Recently, estimated background is calibrated by difference between Data and MC in E ECL sideband. Thank you for listening! 17

18 ACK UP 18

19 Skim procedure S KIM PATH Hadronic Tagging LX_SKIM ANALYSIS_CODE L X _ S K I M 1 charged particle not used in Full_recon call it c (Charge of c) x (Charge of tagged ) = -1 Momentum of c(la frame) >1.0 GeV 19

20 p l sideband ( + e + X) 20

21 p l sideband ( + μ + X) 21

22 Fitting PDFs (MC) - 1D ML fit for p l was done (1.8~3.0 GeV/c) - Cuts for all remaining variables are same - Using simple function as much as possible Some modes in Ulnu are scaled 22

23 + e + X ackground PDF : Gaussian Ulnu :.G. Rare : Exp eνγ :.G. π0eν : G +.G. 23

24 + μ + X ackground PDF : Gaussian Ulnu : Gaussian μνγ :.G. Rare : Exp + Argus πk0 : G + G π0μν : G +.G. 24

25 Signal PDF Signal PDF : G + G +.G + e + X + μ + X M(X) : 0.1 GeV/c 2 M(X) : 1.0 GeV/c 2 M(X) : 1.8 GeV/c 2 25

26 Optimization Control of variables to be optimized p l high cut move(0.01 GeV level) p l low cut move(0.01 GeV level) Remain cut fixed We give 1,000 values have Poisson distribution for estimated G These values are chosen for Yields Yield > 6 cases are ignored (too high U.L. can disturb mean) 16.0% uncertainty of signal efficiency assumed We don t need to consider E ECL contribution to p l distribution G est > 3.0 case not considered 26

27 Optimization e mode pl cut(gev/c) G_est 0.1(GeV) 2.52<pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< μ mode pl cut(gev/c) G_est 0.1(GeV) 2.58<pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl< <pl<

28 Expectation of ranching Fraction Statistical uncertainty PDG uncertainty PDF uncertainty Data in sideband MC(signal)/MC(side) Estimated # of G Signal efficiency and uncertainty Estimate.F.(or upper limit) for observed events when open box. 28

29 Summary Table ( + e + X) M(X) pl cut G_est Efficiency( ) Observed event U.L. (10^{-6}) 0.1 (GeV) 2.52 < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ±

30 Summary Table ( + μ + X) M(X) pl cut G_est Efficiency( ) Observed event U.L. (10^{-6}) 0.1 (GeV) 2.58 < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ± < pl < ± ±

31 Recent progress Lifetime acceptance of X We suppose that X have no experimental signature. Also, we set X is not decaying particle. X should pass ECL(CsI) calorimeter with no decay. M(X) p_{x}^{lab} Lifetime γβct = (p/m)ct > 2716mm (in Lab frame) So, we study for X to have lifetime more than sec case

32 Recent progress Compare Data & MC in E ECL sideband region E ECL + e + X p l sideband E ECL Sideband linded Region p l (GeV/c) + μ + X 32

33 Trial for understanding E ECL sideband From last AM, we try 3 kinds of approaching method to understand data events for E ecl Sideband region with high p l. 1 : We use lepton s momentum in LA frame. And draw their Phi & Theta value. 2 : We suppose they are from QED background like e + e - τ + τ - 3 : We give off-timing cut for data & 911-veto for MC, and look whether there are any better agreement. 33