Difference between revisions of "Lmd eta three pion hlu"
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− | ==Statistics== | + | ==old== |
− | ===Fit missing mass of proton=== | + | ===Statistics=== |
+ | ====Fit missing mass of proton==== | ||
<table> | <table> | ||
<tr> | <tr> | ||
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</table> | </table> | ||
− | ==More== | + | ===More=== |
[[File:Eta and eta prime two pion.pdf]] | [[File:Eta and eta prime two pion.pdf]] | ||
− | ==Form of side-band subtraction histograms== | + | ===Form of side-band subtraction histograms=== |
Question: Suppose there is an original function (e.g. Gaussian), what is the form of the function after bin-by-bin side-band subtraction | Question: Suppose there is an original function (e.g. Gaussian), what is the form of the function after bin-by-bin side-band subtraction | ||
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[[File:formula.gif]] | [[File:formula.gif]] | ||
− | ===Test of the formula=== | + | ====Test of the formula==== |
10M events of Gaussian distribution were generated for a histograms: | 10M events of Gaussian distribution were generated for a histograms: | ||
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</pre> | </pre> | ||
− | === Test with larg bin width === | + | ==== Test with larg bin width ==== |
<table> | <table> | ||
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</pre> | </pre> | ||
− | === apply on the data === | + | ==== apply on the data ==== |
bin width=1MeV yield/error:0.16384 limit:1.81985 | bin width=1MeV yield/error:0.16384 limit:1.81985 | ||
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bin width=3MeV yield/error:-2.48978 limit:0.321244 | bin width=3MeV yield/error:-2.48978 limit:0.321244 | ||
− | == Compare data and simu == | + | === Compare data and simu === |
− | === eta prime=== | + | ==== eta prime==== |
<table> | <table> | ||
<tr> | <tr> | ||
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</table> | </table> | ||
− | === eta=== | + | ==== eta==== |
<table> | <table> | ||
<tr> | <tr> | ||
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</table> | </table> | ||
− | ==Feldman-Cousin Method== | + | ===Feldman-Cousin Method=== |
[[File:Feldman cousin upper limit.png]] | [[File:Feldman cousin upper limit.png]] | ||
==Revive== | ==Revive== |
Revision as of 15:22, 30 October 2015
old
Statistics
Fit missing mass of proton
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More
File:Eta and eta prime two pion.pdf
Form of side-band subtraction histograms
Question: Suppose there is an original function (e.g. Gaussian), what is the form of the function after bin-by-bin side-band subtraction
Answer: Suppose the function is y=f(x). The side-band subtraction makes a new function:
Test of the formula
10M events of Gaussian distribution were generated for a histograms:
side-band subtracted and plot together with the function:
source code:
test(){ const float NEvent=1e7; const int NBin=50; const float Low=-5; const float High=5; const float BinWidth=(High-Low)/NBin; TH1D* h1=new TH1D("test","test",NBin,Low,High); h1->FillRandom("gaus",NEvent); TH1D* h2=h1->Clone("subtracted"); for(int i=2;i<100;i++){ float middle=h1->GetBinContent(i); float left=h1->GetBinContent(i-1); float right=h1->GetBinContent(i+1); float result=middle-(left+right)/2.0; float error=sqrt(middle+left/2.0+right/2.0); h2->SetBinContent(i,result); h2->SetBinError(i,error); } TF1* func=new TF1("fit","-[3]*[3]/2*(-gaus(0)/[2]/[2]+(x-[1])*(x-[1])*gaus(0)/([2]**4))"); func->FixParameter(2,1); func->FixParameter(0,NEvent/sqrt(2*3.1416)/1*BinWidth); func->FixParameter(1,0); func->FixParameter(3,BinWidth); h2->Fit("fit"); TCanvas* c1=new TCanvas("c1","c1",696,474); h2->Draw(); TCanvas* c2=new TCanvas("c2","c2",696,474); h1->Fit("gaus"); h1->Draw(); }
Test with larg bin width
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test(){ const float NEvent=1e3; const int NBin=5; const float Low=-5; const float High=5; const float BinWidth=(High-Low)/NBin; TRandom3 random(0); TH1D* h1=new TH1D("test","test",NBin,Low,High); h1->FillRandom("gaus",NEvent); TH1D* h2=h1->Clone("subtracted"); for(int i=2;i<100;i++){ float middle=h1->GetBinContent(i); float left=h1->GetBinContent(i-1); float right=h1->GetBinContent(i+1); float result=middle-(left+right)/2.0; float error=sqrt(middle+left/2.0+right/2.0); h2->SetBinContent(i,result); h2->SetBinError(i,error); } TF1* funcFix=new TF1("fit_fix","-[3]*[3]/2*(-gaus(0)/[2]/[2]+(x-[1])*(x-[1])*ga\ us(0)/([2]**4))"); funcFix->FixParameter(2,1); funcFix->FixParameter(0,NEvent/sqrt(2*3.1416)/1*BinWidth); funcFix->FixParameter(1,0); funcFix->FixParameter(3,BinWidth); funcFix->SetLineColor(kRed); h2->Fit("fit_fix"); TF1* func=new TF1("fit","-[3]*[3]/2*(-gaus(0)/[2]/[2]+(x-[1])*(x-[1])*gaus(0)/(\ [2]**4))"); func->FixParameter(2,1); // func->FixParameter(0,NEvent/sqrt(2*3.1416)/1*BinWidth); func->FixParameter(1,0); func->FixParameter(3,BinWidth); func->SetLineColor(kBlue); h2->Fit("fit","+"); TCanvas* c1=new TCanvas("c1","c1",696,474); h2->Draw(); TCanvas* c2=new TCanvas("c2","c2",696,474); h1->Fit("gaus"); h1->Draw(); }
Test with background
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test(){ const float NEvent=1e5; const float NSignal=1e5; const int NBin=20; const float Low=-5; const float High=5; const float BinWidth=(High-Low)/NBin; TRandom3 random(0); TH1D* h1=new TH1D("test","test",NBin,Low,High); h1->FillRandom("pol2",NEvent); h1->FillRandom("gaus",NSignal); TH1D* h2=h1->Clone("subtracted"); for(int i=2;i<100;i++){ float middle=h1->GetBinContent(i); float left=h1->GetBinContent(i-1); float right=h1->GetBinContent(i+1); float result=middle-(left+right)/2.0; float error=sqrt(middle+left/2.0+right/2.0); h2->SetBinContent(i,result); h2->SetBinError(i,error); } TF1* funcFix=new TF1("fit_fix","-[3]*[3]/2*(-gaus(0)/[2]/[2]+(x-[1])*(x-[1])*gaus(0)/([2]**4))"); funcFix->FixParameter(2,1); funcFix->FixParameter(0,NSignal/sqrt(2*3.1416)/1*BinWidth); funcFix->FixParameter(1,0); funcFix->FixParameter(3,BinWidth); funcFix->SetLineColor(kRed); h2->Fit("fit_fix"); TF1* func=new TF1("fit","-[3]*[3]/2*(-gaus(0)/[2]/[2]+(x-[1])*(x-[1])*gaus(0)/([2]**4))"); func->FixParameter(2,1); // func->FixParameter(0,NEvent/sqrt(2*3.1416)/1*BinWidth); func->FixParameter(1,0); func->FixParameter(3,BinWidth); func->SetLineColor(kBlue); h2->Fit("fit","+"); TCanvas* c2=new TCanvas("c2","c2",696,474); h2->Draw(); TCanvas* c1=new TCanvas("c1","c1",696,474); TF1* signal_back=new TF1("signal_back","gaus(0)+pol2(2)"); signal_back->SetParameters(NEvent/sqrt(2*3.1416)/1*BinWidth,0.0,1.0,0.0,0.0,0.0); h1->Fit("signal_back"); h1->Draw(); }
apply on the data
bin width=1MeV yield/error:0.16384 limit:1.81985
bin width=2MeV yield/error:-1.69772 limit:0.491618
bin width=3MeV yield/error:-2.48978 limit:0.321244
Compare data and simu
eta prime
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eta
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