[#1] INFO:Eval -- RooRealVar::setRange(mean) new range named 'refrange_fft_model' created with bounds [-3,3]
[#1] INFO:NumericIntegration -- RooRealIntegral::init(gx_Int[mean,x]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(model_mean_Int[mean]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#0] WARNING:Eval -- The FFT convolution 'model' will run with 50 bins. A decent accuracy for difficult convolutions is typically only reached with n >= 1000. Suggest to increase the number of bins of the observable 'mean'.
[#1] INFO:Caching -- RooAbsCachedPdf::getCache(model) creating new cache 0x5c284d0 with pdf gx_CONV_model_mean_CACHE_Obs[mean,x]_NORM_mean for nset (mean) with code 0
[#1] INFO:NumericIntegration -- RooRealIntegral::init(gx_Int[mean,x]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(model_mean_Int[mean]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#0] WARNING:Eval -- The FFT convolution 'model' will run with 50 bins. A decent accuracy for difficult convolutions is typically only reached with n >= 1000. Suggest to increase the number of bins of the observable 'mean'.
[#1] INFO:Caching -- RooAbsCachedPdf::getCache(model) creating new cache 0x3e123d0 with pdf gx_CONV_model_mean_CACHE_Obs[x,mean]_NORM_x_mean for nset (x,mean) with code 1
[#1] INFO:NumericIntegration -- RooRealIntegral::init(gx_Int[mean,x]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(model_mean_Int[mean]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#0] WARNING:Eval -- The FFT convolution 'model' will run with 50 bins. A decent accuracy for difficult convolutions is typically only reached with n >= 1000. Suggest to increase the number of bins of the observable 'mean'.
[#1] INFO:Caching -- RooAbsCachedPdf::getCache(model) creating new cache 0x3e11c50 with pdf gx_CONV_model_mean_CACHE_Obs[x,mean]_NORM_x_mean for nset (x,mean) with code 1
[#1] INFO:Fitting -- RooAbsPdf::fitTo(model_Int[mean]_Norm[mean,x]_wrapped_pdf) fixing normalization set for coefficient determination to observables in data
[#1] INFO:Fitting -- using generic CPU library compiled with no vectorizations
[#1] INFO:Fitting -- Creation of NLL object took 26.2137 ms
[#1] INFO:Fitting -- RooAddition::defaultErrorLevel(nll_model_Int[mean]_Norm[mean,x]_wrapped_pdf_genData) Summation contains a RooNLLVar, using its error level
[#0] WARNING:Minimization -- RooAbsMinimizerFcn::synchronize: WARNING: no initial error estimate available for a: using 0.5
[#0] WARNING:Minimization -- RooAbsMinimizerFcn::synchronize: WARNING: no initial error estimate available for sigma: using 0.2
[#1] INFO:Minimization -- [fitFCN] No discrete parameters, performing continuous minimization only
sigma=0.5, [#1] INFO:NumericIntegration -- RooRealIntegral::init(model_mean_Int[mean]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#0] WARNING:Eval -- The FFT convolution 'model' will run with 50 bins. A decent accuracy for difficult convolutions is typically only reached with n >= 1000. Suggest to increase the number of bins of the observable 'mean'.
[#1] INFO:Caching -- RooAbsCachedPdf::getCache(model) creating new cache 0x636d780 with pdf gx_CONV_model_mean_CACHE_Obs[mean] for nset () with code 2

prevFCN = 2171.275755  a=2.017, 
prevFCN = 2171.275755  a=1.983, 
prevFCN = 2171.275755  a=2.172, 
prevFCN = 2171.861215  a=1.84, 
prevFCN = 2174.775121  a=2.017, 
prevFCN = 2171.275755  a=1.983, 
prevFCN = 2171.275755  a=2, sigma=0.5067, 
prevFCN = 2171.291807  sigma=0.4934, 
prevFCN = 2171.265264  sigma=0.5029, 
prevFCN = 2171.281998  sigma=0.4971, 
prevFCN = 2171.270547  sigma=0.4843, 
prevFCN = 2171.259881  a=2.172, 
prevFCN = 2171.692149  a=1.84, 
prevFCN = 2175.249474  a=2.017, 
prevFCN = 2171.259881  a=1.983, 
prevFCN = 2171.259881  a=2.172, 
prevFCN = 2171.692149  a=1.84, 
prevFCN = 2175.249474  a=2, sigma=0.4871, 
prevFCN = 2171.26042  sigma=0.4815, 
prevFCN = 2171.260367  a=2.003, sigma=0.4688, 
prevFCN = 2171.275519  a=2.001, sigma=0.479, 
prevFCN = 2171.261603  a=2, sigma=0.482, 
prevFCN = 2171.260187  a=2, sigma=0.4832, 
prevFCN = 2171.259943  a=2, sigma=0.4838, 
prevFCN = 2171.259893  a=2, sigma=0.484, 
prevFCN = 2171.259883  a=2, sigma=0.4841, 
prevFCN = 2171.259881  a=2, sigma=0.4842, 
prevFCN = 2171.25988  a=2.017, 
prevFCN = 2171.25988  a=1.983, 
prevFCN = 2171.25988  a=2.172, 
prevFCN = 2171.691427  a=1.84, 
prevFCN = 2175.251788  a=2.017, 
prevFCN = 2171.25988  a=1.983, 
prevFCN = 2171.25988  a=2, sigma=0.487, 
prevFCN = 2171.260398  sigma=0.4814, 
prevFCN = 2171.260398  sigma=0.4842, 
prevFCN = 2171.25988  a=2.017, 
prevFCN = 2171.25988  a=1.983, 
prevFCN = 2171.25988  a=2.172, 
prevFCN = 2171.691427  a=1.84, 
prevFCN = 2175.251788  a=2.083, 
prevFCN = 2171.25988  a=1.92, 
prevFCN = 2172.379556  a=2, sigma=0.487, 
prevFCN = 2171.260398  sigma=0.4814, 
prevFCN = 2171.260398  a=2.002, sigma=0.4842, 
prevFCN = 2171.25988  a=1.998, 
prevFCN = 2171.25988  a=2, sigma=0.4848, 
prevFCN = 2171.259901  sigma=0.4836, 
prevFCN = 2171.259901  sigma=0.4843, 
prevFCN = 2171.259881  sigma=0.4841, 
prevFCN = 2171.259881  a=2.083, sigma=0.487, 
prevFCN = 2171.260398  a=2.041, sigma=0.4842, 
prevFCN = 2171.25988  a=2.02, sigma=0.4842, 
prevFCN = 2171.25988  a=2.01, sigma=0.4842, 
prevFCN = 2171.25988  a=2.005, sigma=0.4842, 
prevFCN = 2171.25988  a=2.003, sigma=0.4842, 
prevFCN = 2171.25988  a=2.001, sigma=0.4842, 
prevFCN = 2171.25988  a=2.001, sigma=0.4842, 
prevFCN = 2171.25988  a=2, sigma=0.4842, 
prevFCN = 2171.25988  a=2, sigma=0.4842, 
prevFCN = 2171.25988  a=2, sigma=0.4842, 
prevFCN = 2171.25988  a=2, sigma=0.4842, 
prevFCN = 2171.25988  a=2, sigma=0.4842, 
prevFCN = 2171.25988  a=2.083, 
prevFCN = 2171.25988  a=1.92, 
prevFCN = 2172.379556  a=2, sigma=0.4843, 
prevFCN = 2171.259881  sigma=0.4841, 
prevFCN = 2171.259881  a=2.002, sigma=0.4842, 
prevFCN = 2171.25988  a=1.998, 
prevFCN = 2171.25988  a=2, sigma=0.4842, 
prevFCN = 2171.25988  sigma=0.4842, 
prevFCN = 2171.25988  a=2.083, sigma=0.4843, 
prevFCN = 2171.259881  a=2, sigma=0.4842, [#1] INFO:NumericIntegration -- RooRealIntegral::init(gx_Int[mean,x]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(model_mean_Int[mean]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#0] WARNING:Eval -- The FFT convolution 'model' will run with 50 bins. A decent accuracy for difficult convolutions is typically only reached with n >= 1000. Suggest to increase the number of bins of the observable 'mean'.
[#1] INFO:Caching -- RooAbsCachedPdf::getCache(model) creating new cache 0x66bdcb0 with pdf gx_CONV_model_mean_CACHE_Obs[x,mean]_NORM_x_mean for nset (x,mean) with code 1
[#1] INFO:NumericIntegration -- RooRealIntegral::init(gx_Int[mean,x]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#1] INFO:NumericIntegration -- RooRealIntegral::init(model_mean_Int[mean]) using numeric integrator RooIntegrator1D to calculate Int(mean)
[#0] WARNING:Eval -- The FFT convolution 'model' will run with 50 bins. A decent accuracy for difficult convolutions is typically only reached with n >= 1000. Suggest to increase the number of bins of the observable 'mean'.
[#1] INFO:Caching -- RooAbsCachedPdf::getCache(model) creating new cache 0x5f2f050 with pdf gx_CONV_model_mean_CACHE_Obs[x,mean]_NORM_x for nset (x) with code 3