Doing Serial Gaussian Fit 
****************************************
Minimizer is Minuit2 / Migrad
Chi2                      =      40217.9
NDf                       =        39409
Edm                       =  3.38976e-08
NCalls                    =           75
Constant                  =      120.018   +/-   0.105817    
Mean                      =   0.00114402   +/-   0.000709328 
Sigma                     =     0.979817   +/-   0.000519995    (limited)
****************************************
Minimizer is Minuit2 / Migrad
MinFCN                    =      20355.3
Chi2                      =      40710.5
NDf                       =        39997
Edm                       =  8.97826e-10
NCalls                    =           65
Constant                  =      120.024   +/-   0.105551    
Mean                      =  0.000138332   +/-   0.000716607 
Sigma                     =      0.99985   +/-   0.000537073    (limited)
Real time 0:00:00, CP time 0.220
Doing Vectorized Gaussian Fit 
****************************************
Minimizer is Minuit2 / Migrad
Chi2                      =      40217.9
NDf                       =        39409
Edm                       =  3.38976e-08
NCalls                    =           75
Constant                  =      120.018   +/-   0.105817    
Mean                      =   0.00114402   +/-   0.000709328 
Sigma                     =     0.979817   +/-   0.000519995    (limited)
****************************************
Minimizer is Minuit2 / Migrad
MinFCN                    =      20355.3
Chi2                      =      40710.5
NDf                       =        39997
Edm                       =  8.97826e-10
NCalls                    =           65
Constant                  =      120.024   +/-   0.105551    
Mean                      =  0.000138332   +/-   0.000716607 
Sigma                     =      0.99985   +/-   0.000537073    (limited)
Real time 0:00:00, CP time 0.180
Doing Serial Polynomial Fit 
****************************************
Minimizer is Minuit2 / Migrad
Chi2                      =        37690
NDf                       =        38075
Edm                       =  3.47827e-15
NCalls                    =           72
A                         =     0.202001   +/-   0.00176461  
B                         =     0.268032   +/-   0.0153893   
C                         =      1.05504   +/-   0.0248331   
****************************************
Minimizer is Minuit2 / Migrad
MinFCN                    =      20527.5
Chi2                      =        41055
NDf                       =        39997
Edm                       =  8.16351e-08
NCalls                    =           90
A                         =     0.149763   +/-   0.00165111  
B                         =     0.880262   +/-   0.0135143   
C                         =       0.6066   +/-   0.0181308   
Real time 0:00:00, CP time 0.150
Doing Vectorized Polynomial Fit 
****************************************
Minimizer is Minuit2 / Migrad
Chi2                      =        37690
NDf                       =        38075
Edm                       =  3.15424e-16
NCalls                    =           70
A                         =     0.202001   +/-   0.00176461  
B                         =     0.268032   +/-   0.0153893   
C                         =      1.05504   +/-   0.0248331   
****************************************
Minimizer is Minuit2 / Migrad
MinFCN                    =      20527.5
Chi2                      =        41055
NDf                       =        39997
Edm                       =  8.16253e-08
NCalls                    =           90
A                         =     0.149763   +/-   0.0016511   
B                         =     0.880262   +/-   0.0135143   
C                         =       0.6066   +/-   0.0181308   
Real time 0:00:00, CP time 0.150
 
 
#include <iostream>
 
 
   
 
 
   int nbins = 40000;
   auto h1 = 
new TH1D(
"h1",
"h1",nbins,-3,3);
 
 
   std::cout << "Doing Serial Gaussian Fit " << std::endl;
   auto f1 = 
new TF1(
"f1",
"gaus");
 
 
   std::cout << "Doing Vectorized Gaussian Fit " << std::endl;
   auto f2 = 
new TF1(
"f2",
"gaus",-3,3,
"VEC");
 
   
   
   
   
 
   auto f3 = 
new TF1(
"f3",
"[A]*x^2+[B]*x+[C]",0,10);
 
   f3->SetParameters(0.5,3,2);
   f3->SetNpx(nbins*10);
   
   auto h2 = 
new TH1D(
"h2",
"h2",nbins,0,10);
 
   h2->FillRandom("f3",10*nbins);
   std::cout << "Doing Serial Polynomial Fit " << std::endl;
   f3->SetParameters(2,2,2);
   h2->Fit(f3);
   h2->Fit(f3,"L+");
 
   std::cout << "Doing Vectorized Polynomial Fit " << std::endl;
   auto f4 = 
new TF1(
"f4",
"[A]*x*x+[B]*x+[C]",0,10);
 
   f4->SetVectorized(true);
   f4->SetParameters(2,2,2);
   h2->Fit(f4);
   h2->Fit(f4,"L+");
 
   
   h2->Rebin(nbins/100);
   h2->Scale(100./nbins);
   ((
TF1 *)h2->GetListOfFunctions()->At(0))->SetTitle(
"Chi2 Fit");
   ((
TF1 *)h2->GetListOfFunctions()->At(1))->SetTitle(
"Likelihood Fit");
   
 
}
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Option_t Option_t SetLineColor
R__EXTERN TStyle * gStyle
static void SetDefaultMinimizer(const char *type, const char *algo=nullptr)
Set the default Minimizer type and corresponding algorithms.
virtual void SetNpx(Int_t npx=100)
Set the number of points used to draw the function.
1-D histogram with a double per channel (see TH1 documentation)
virtual void FillRandom(const char *fname, Int_t ntimes=5000, TRandom *rng=nullptr)
Fill histogram following distribution in function fname.
virtual TFitResultPtr Fit(const char *formula, Option_t *option="", Option_t *goption="", Double_t xmin=0, Double_t xmax=0)
Fit histogram with function fname.
virtual TH1 * Rebin(Int_t ngroup=2, const char *newname="", const Double_t *xbins=nullptr)
Rebin this histogram.
TList * GetListOfFunctions() const
virtual void Scale(Double_t c1=1, Option_t *option="")
Multiply this histogram by a constant c1.
TObject * At(Int_t idx) const override
Returns the object at position idx. Returns 0 if idx is out of range.
void SetOptFit(Int_t fit=1)
The type of information about fit parameters printed in the histogram statistics box can be selected ...