Organization and simultaneous fits: illustration use of ROOT.RooCustomizer and ROOT.RooSimWSTool interface in factory workspace tool in a complex standalone B physics example
Author: Clemens Lange, Wouter Verkerke (C++ version)
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Tuesday, March 19, 2024 at 07:16 PM.
import ROOT
w = ROOT.RooWorkspace("w")
Make signal model for CPV: A bmixing decay function in t (convoluted with a triple Gaussian resolution model) times a Gaussian function the reconstructed mass
w.factory(
"PROD::sig( BMixDecay::sig_t( dt[-20,20], mixState[mixed=1,unmix=-1], tagFlav[B0=1,B0bar=-1], "
"tau[1.54], dm[0.472], w[0.05], dw[0], "
"AddModel::gm({GaussModel(dt,biasC[-10,10],sigmaC[0.1,3],dterr[0.01,0.2]), "
"GaussModel(dt,0,sigmaT[3,10]), "
"GaussModel(dt,0,20)},{fracC[0,1],fracT[0,1]}), "
"DoubleSided ), "
"Gaussian::sig_m( mes[5.20,5.30], mB0[5.20,5.30], sigmB0[0.01,0.05] ))"
)
<cppyy.gbl.RooProdPdf object at 0xa2302d0>
Make background component: A plain decay function in t times an Argus function in the reconstructed mass
w.factory("PROD::bkg( Decay::bkg_t( dt, tau, gm, DoubleSided), " "ArgusBG::bkg_m( mes, 5.291, k[-100,-10]))")
<cppyy.gbl.RooProdPdf object at 0xa56ea50>
Make composite model from the signal and background component
w.factory("SUM::model( Nsig[5000,0,10000]*sig, NBkg[500,0,10000]*bkg )")
<cppyy.gbl.RooAddPdf object at 0xa638c60>
Introduce a flavour tagging category tagCat as observable with 4 states corresponding to 4 flavour tagging techniques with different performance that require different parameterizations of the fit model
ROOT.RooSimWSTool operation: - Make 4 clones of model (for each tagCat) state, will gain an individual copy of parameters w, and biasC. The other parameters remain common - Make a simultaneous pdf of the 4 clones assigning each to the appropriate state of the tagCat index category
ROOT.RooSimWSTool is interfaced as meta-type SIMCLONE in the factory. The $SplitParam() argument maps to the SplitParam() named argument in the ROOT.RooSimWSTool constructor
w.factory("SIMCLONE::model_sim( model, $SplitParam({w,dw,biasC},tagCat[Lep,Kao,NT1,NT2]))")
<cppyy.gbl.RooSimultaneous object at 0xa8e8b20>
Class ROOT.RooCustomizer makes clones of existing pdfs with certain prescribed modifications (branch of leaf node replacements)
Here we take our model (the original before ROOT.RooSimWSTool modifications) and request that the parameter w (the mistag rate) is replaced with an expression-based function that calculates w in terms of the Dilution parameter D that is defined D = 1-2*w
Make a clone model_D of original 'model' replacing 'w' with 'expr('0.5-D/2',D[0,1])'
w.factory("EDIT::model_D(model, w=expr('0.5-D/2',D[0,1]) )")
<cppyy.gbl.RooAddPdf object at 0xa744270>
Print workspace contents
w.Print()
RooWorkspace(w) w contents variables --------- (D,NBkg,Nsig,biasC,biasC_Kao,biasC_Lep,biasC_NT1,biasC_NT2,dm,dt,dterr,dw,dw_Kao,dw_Lep,dw_NT1,dw_NT2,fracC,fracT,k,mB0,mes,mixState,sigmB0,sigmaC,sigmaT,tagCat,tagFlav,tau,w,w_Kao,w_Lep,w_NT1,w_NT2) p.d.f.s ------- RooProdPdf::bkg[ bkg_t * bkg_m ] = 0.307193 RooProdPdf::bkg_Kao[ bkg_t_Kao * bkg_m ] = 0.307193 RooProdPdf::bkg_Lep[ bkg_t_Lep * bkg_m ] = 0.307193 RooProdPdf::bkg_NT1[ bkg_t_NT1 * bkg_m ] = 0.307193 RooProdPdf::bkg_NT2[ bkg_t_NT2 * bkg_m ] = 0.307193 RooArgusBG::bkg_m[ m=mes m0=5.291 c=k p=0.5 ] = 0.279062 RooDecay::bkg_t[ t=dt tau=tau ] = 1.10081 RooDecay::bkg_t_Kao[ t=dt tau=tau ] = 1.10081 RooDecay::bkg_t_Lep[ t=dt tau=tau ] = 1.10081 RooDecay::bkg_t_NT1[ t=dt tau=tau ] = 1.10081 RooDecay::bkg_t_NT2[ t=dt tau=tau ] = 1.10081 RooAddPdf::model[ Nsig * sig + NBkg * bkg ] = 1.88229/1 RooAddPdf::model_D[ Nsig * sig_model_D + NBkg * bkg ] = 1.5029/1 RooAddPdf::model_Kao[ Nsig * sig_Kao + NBkg * bkg_Kao ] = 1.88229/1 RooAddPdf::model_Lep[ Nsig * sig_Lep + NBkg * bkg_Lep ] = 1.88229/1 RooAddPdf::model_NT1[ Nsig * sig_NT1 + NBkg * bkg_NT1 ] = 1.88229/1 RooAddPdf::model_NT2[ Nsig * sig_NT2 + NBkg * bkg_NT2 ] = 1.88229/1 RooSimultaneous::model_sim[ indexCat=tagCat Kao=model_Kao Lep=model_Lep NT1=model_NT1 NT2=model_NT2 ] = 0.470573 RooProdPdf::sig[ sig_t * sig_m ] = 2.0398 RooProdPdf::sig_Kao[ sig_t_Kao * sig_m ] = 2.0398 RooProdPdf::sig_Lep[ sig_t_Lep * sig_m ] = 2.0398 RooProdPdf::sig_NT1[ sig_t_NT1 * sig_m ] = 2.0398 RooProdPdf::sig_NT2[ sig_t_NT2 * sig_m ] = 2.0398 RooGaussian::sig_m[ x=mes mean=mB0 sigma=sigmB0 ] = 1 RooProdPdf::sig_model_D[ sig_t_model_D * sig_m ] = 1.62247 RooBMixDecay::sig_t[ mistag=w delMistag=dw mixState=mixState tagFlav=tagFlav tau=tau dm=dm t=dt ] = 2.0398 RooBMixDecay::sig_t_Kao[ mistag=w_Kao delMistag=dw_Kao mixState=mixState tagFlav=tagFlav tau=tau dm=dm t=dt ] = 2.0398 RooBMixDecay::sig_t_Lep[ mistag=w_Lep delMistag=dw_Lep mixState=mixState tagFlav=tagFlav tau=tau dm=dm t=dt ] = 2.0398 RooBMixDecay::sig_t_NT1[ mistag=w_NT1 delMistag=dw_NT1 mixState=mixState tagFlav=tagFlav tau=tau dm=dm t=dt ] = 2.0398 RooBMixDecay::sig_t_NT2[ mistag=w_NT2 delMistag=dw_NT2 mixState=mixState tagFlav=tagFlav tau=tau dm=dm t=dt ] = 2.0398 RooBMixDecay::sig_t_model_D[ mistag=model_D_2 delMistag=dw mixState=mixState tagFlav=tagFlav tau=tau dm=dm t=dt ] = 1.62247 analytical resolution models ---------------------------- RooAddModel::gm[ x=dt (fracC * gm_11 + fracT * gm_12 + [%] * gm_13) ] = 1.25632 RooGaussModel::gm_11[ x=dt mean=biasC sigma=sigmaC msf=dterr ssf=dterr ] = 2.45126 RooGaussModel::gm_11_Kao[ x=dt mean=biasC_Kao sigma=sigmaC msf=dterr ssf=dterr ] = 2.45126 RooGaussModel::gm_11_Lep[ x=dt mean=biasC_Lep sigma=sigmaC msf=dterr ssf=dterr ] = 2.45126 RooGaussModel::gm_11_NT1[ x=dt mean=biasC_NT1 sigma=sigmaC msf=dterr ssf=dterr ] = 2.45126 RooGaussModel::gm_11_NT2[ x=dt mean=biasC_NT2 sigma=sigmaC msf=dterr ssf=dterr ] = 2.45126 RooGaussModel::gm_12[ x=dt mean=0 sigma=sigmaT msf=1 ssf=1 ] = 0.0613757 RooGaussModel::gm_13[ x=dt mean=0 sigma=20 msf=1 ssf=1 ] = 0.0199471 RooAddModel::gm_Kao[ x=dt (fracC * gm_11_Kao + fracT * gm_12 + [%] * gm_13) ] = 1.25632 RooAddModel::gm_Lep[ x=dt (fracC * gm_11_Lep + fracT * gm_12 + [%] * gm_13) ] = 1.25632 RooAddModel::gm_NT1[ x=dt (fracC * gm_11_NT1 + fracT * gm_12 + [%] * gm_13) ] = 1.25632 RooAddModel::gm_NT2[ x=dt (fracC * gm_11_NT2 + fracT * gm_12 + [%] * gm_13) ] = 1.25632 functions -------- RooFormulaVar::model_D_2[ actualVars=(D) formula="0.5-D/2" ] = 0.25
Make workspace visible on command line
ROOT.gDirectory.Add(w)