Monte Carlo Tuning of LUARLW Mode

Transcription

1 Monte Carlo Tuning of LUARLW Mode Lei Xia 1, Guangshun Huang 1, Ronggang Ping 2, Xi an Xiong 2 1 University of Science and Technology of China 2 Institute of High Energy Physics, Chinese Academy of Sciences The BESIII Collaboration Summer Meeting 2015 June 16 th 2015 Shanghai Jiao Tong University

2 Outline Introduction Data Preparing Data Set Event Selection Background estimation Choice of Distributions Fitting Result by LUARLW Model Fitting results of the parameters added in simultaneous Parameters determined by fit Tuning results Tuning results at 4.6 GeV Tuning results at 3.65GeV Summary June 16th 2015 Monte Carlo Tuning of LUARLW Mode 2

3 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 3 Introduction Motivation Overview of tuning

4 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 4 Motivation Tune the LUNDARLW parameters, to get the optimum values of the parameters. To ensure the accuracy of simulations, it is necessary to study the parameters of LUNDARLW for each energy point. Assumption: The detector simulation is so well that the difference between MC simulation and data is negligible, and any difference seen is due to the imperfection of event generator.

5 Overview of tuning strategy To get the parameters by simultaneously fitting to data distributions. Models of fit to the experimental data. Quadratic approximation: f p 0 + δp, x = a 0 0 x + n i=1 a 1 n i x δp i + n i=1 j=1 a 2 ij x δp i δp j MC p 0 + δp, x Parameter vector: p = p 0 + δp Observable : x June 16th 2015 Monte Carlo Tuning of LUARLW Mode 5

6 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 6 Overview of tuning strategy Solving a system linear equation: P a = MC Where P is the matrix of parameters, a is the vector of coefficients a 0,1,2. The optimum values of the parameters p i s, their errors σ i s and the correlated coefficients ρ ij s are determined from a standard χ 2 fit of the analytic approximation to the data using MINUIT.

7 Data Preparing Boss version & Data sets Event Selection Background estimation Choice of Distributions June 16th 2015 Monte Carlo Tuning of LUARLW Mode 7

8 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 8 Boss version & Data sets Boss version: Boss6.6.4.p01 Data sets: Feb, 5th, 2014 Mar, 9th, 2014 s = 4.6 GeV. Online Luminosity: 506 pb 1 MC Sample events for each parameter of LUNDARLW Model.

9 Selection Criteria(I) Veto Bhabha and digam Two showers with maximum energy deposition. θ 1 + θ < 10 and E > 0.65E beam Good hadron tracks V r < 0.5cm, V z < 5cm, cos θ < 0.93 Momentum < E beam + 5σ de dx mea de dx proton < 10 σ proton If E P > 0.8, E < 0.65E beam Veto e ± from gamma conversion, if M e + e < 0.1 and angle e + e < 15 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 9

10 Selection Criteria(II) Hardon event candidates For N track 2 : Visible energy > 0.25E beam For N track = 2 : Veto: θ 1 + θ < 15, φ 1 + φ < 10 Number of isolated photon 2 Veto: both tracks are electrons 0.75 < E P < For N track = 3 : Veto: Angle of two largest energetic tracks, θ 1st + θ 2nd 180 < 15, φ 1st + φ 2nd 180 < 10 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 10

11 Background estimation Sources of background: bhabha e + e μ + μ e + e γγ e + e τ + τ e + e e + e + X Scale to the same luminosity of data scale = L σ Event Number TABLE I Background estimation, cross section and number of events QED Cross section(nb) Number of events bhabha e + e μ + μ e + e γγ e + e τ + τ e + e e + e + X June 16th 2015 Monte Carlo Tuning of LUARLW Mode 11

12 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 12 Choice of Distributions The distributions selected should be sensitive to the parameters in the investigation. For a given distribution MC(x), the sensitivity is defined as: Sensitivity S i x = δmc x MC x pi δp i p i ln MC x ln p i Check Sensitivities for each Parameter: pi

13 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 13 Choice of Distributions FIGURE I. Sensitivity and Choice of Distributions We choose this one! Yes No

14 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 14 Choice of Distributions FIGURE I. Sensitivity and Choice of Distributions We choose this one! Yes No

15 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 15 Fitting Result by Lund Area Law Model Fitting results of the parameters added in simultaneous Parameters determined by Fit

16 Fitting Result We take these distribution into consideration. TABLE II. Choice of distribution Distribution Choice ngamma V Egamma X gam_cos V Nnh V Etrk V pmag V costheta V phi X rapidity V pseudrapidity V xf V xper V sph V apl V thr V opl V fw10 X fw20 X fw30 X fw40 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 16 X

17 Fitting results of the parameters added in simultaneous June 16th 2015 Monte Carlo Tuning of LUARLW Mode 17 FIGURE II. Sensitivity and Choice of Distributions Black: Data Red: Fitting Result

18 Fitting results of the parameters added in simultaneous June 16th 2015 Monte Carlo Tuning of LUARLW Mode 18 FIGURE II. Sensitivity and Choice of Distributions Black: Data Red: Fitting Result

19 Fitting results of the parameters added in simultaneous June 16th 2015 Monte Carlo Tuning of LUARLW Mode 19 FIGURE II. Sensitivity and Choice of Distributions Black: Data Red: Fitting Result

20 Parameters determined by fit We got a group of parameters. TABLE III. Parameters determined by fit parameters Value PARJ(11) PARJ(12) PARJ(14) PARJ(15) PARJ(16) PARJ(17) PARJ(1) PARJ(2) PARJ(21) RALPA(15) RALPA(16) RALPA(17) June 16th 2015 Monte Carlo Tuning of LUARLW Mode 20

21 Tuning results June 16th 2015 Monte Carlo Tuning of LUARLW Mode 21

22 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 22 Tuning results Tuning results at 4.6 GeV Tuning results at 3.65 GeV

23 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 23 Tuning results at 4.6 GeV Using fit7 parameters produced by the MINUIT to produce MC and compared with data. FIGURE III. Compare of fitting result and tuning result for nch Black:Data Green:MC Red:Fitting Result

24 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 24 Tuning results at 4.6 GeV FIGURE IV. Tuning results Black: Data Green: MC

25 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 25 Tuning results at 4.6 GeV FIGURE IV. Tuning results Black: Data Green: MC

26 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 26 Tuning results at 4.6 GeV FIGURE IV. Tuning results Black: Data Green: MC

27 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 27 Tuning results at 4.6 GeV FIGURE IV. Tuning results Black: Data Green: MC

28 Tuning results at 3.65 GeV Parameters for simulation TABLE IV. Parameters for simulation parameters Group1 Group2 Group3 PARJ(11) PARJ(12) PARJ(14) PARJ(15) PARJ(16) PARJ(17) PARJ(1) PARJ(2) PARJ(21) RALPA(15) RALPA(16) RALPA(17) June 16th 2015 Monte Carlo Tuning of LUARLW Mode 28

29 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 29 Tuning results at 3.65 GeV FIGURE V. Tuning results at 3.65 GeV Black: Data Green: MC Group1 Group2 Group3

30 Summary June 16th 2015 Monte Carlo Tuning of LUARLW Mode 30

31 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 31 Summary We have a preliminary fitting result and tuning result in several energy points. We got several groups of parameters and could extend to other energy points. The first tuning is accomplished but we need more combination on sensitivity for the best result to further work. Simulate the hadron generate process and contribute to R value measure.

32 Thank You! June 16th 2015 Monte Carlo Tuning of LUARLW Mode 32

33 Backup TABLE IV. Parameters in tuning Observable Range Description Sphericity (sph) 0 S 1 S = 0: two jet event S = 1: isotropic event A measurement of the summed 2 p with respect to an eventaxis. Aplanarity (apl) 0 A 0.5 A = 0: a planar event A = 0.5: isotropic event It measures the transverse momentum component out of the event plane. Thrust (thr) 0.5 A 1 T = 0.5: isotropic event T = 1: two jets event Oblateness (obl) O 0 O = 0: an event symmetrical around the thrust axis O > 0: a planar event June 16th 2015 Monte Carlo Tuning of LUARLW Mode 33

34 Backup TABLE IV. Parameters in tuning Observable Range Description Fox-Wolfram moments h 10 h 40 H i I = 0,1,2,3,4 H 0 = 1: mass is zero H 1 = 0 momentum is balanced H 2,4,6 = 1 and H 1,3,5 = 0: two jet events Etrk E = m 2 + p 2 x + p 2 y + p2 z Charged track energy Ptrk cos θ p = p x 2 + p y 2 + p z 2 p z p Charged track momentum Azimuthal angle distribution June 16th 2015 Monte Carlo Tuning of LUARLW Mode 34

35 June 16th 2015 Monte Carlo Tuning of LUARLW Mode 35 Backup TABLE IV. Parameters in tuning Observable Range Description ϕ cos 1 p x p cos θ rapidity 1 2 ln E + p z E p z Pseudorapidity 1 2 ln p + p z p p z x 2p F z W x per 2p W Azimuthal angle distribution Assuming pion W: Total energy of event p : Transverse momentum