Difference between revisions of "Lmd eta three pion hlu"

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==Revive==
 
==Revive==
 
===channel===
 
===channel===
γ p → p π+π-π+&pi-
+
γ p → p π+π-π+π-
 +
 
 
selecting p η prime production and η prime decay to π+π-η
 
selecting p η prime production and η prime decay to π+π-η
 +
 +
<table>
 +
<tr>
 +
<td>[[File:Mm p before cut.png|thumb|left|300px|before selecting &eta; prime]]</td>
 +
<td>[[File:Mm p after cut.png|thumb|left|300px|after selecting &eta; prime. A mistake makes the selection too too tight]]</td>
 +
</tr>
 +
</table>
 +
===Production channels===
 +
<table>
 +
<tr>
 +
<td>[[File:Mm2vsmmppipi.png|thumb|left|300px|Missing mass square vs. missing mass of p&pi;+&pi;-]]</td>
 +
<td>[[File:Mm ppippim.png|thumb|left|300px|Fit &eta; in spectrum of missing mass of p&pi;+&pi;- and yield 6349]]</td>
 +
</tr>
 +
</table>
 +
 +
===With or Without photon===
 +
<table>
 +
<tr>
 +
<td>[[File:Mpvsmmpppipi.png|thumb|left|300px|Missing momentum vs. missing mass of p&pi;+&pi;- after selecting missing mass around zero]]</td>
 +
<td>[[File:Im pippim.png|thumb|left|300px|Invariant mass of &pi;+&pi;- after requiring missing momentum smaller than 50 MeV]]</td>
 +
</tr>
 +
</table>
 +
===Upper Limit===
 +
<table>
 +
<tr>
 +
<td>[[File:Fit im pippim.png|thumb|left|300px|Fit of invariant mass of &pi;+&pi;- around &eta; and yield upper limit of 1.64 with statistical uncertainty of bin at 5.12 and fitted yield -12.5]]</td>
 +
</tr>
 +
</table>
 +
 +
Therefore, the upper limit is 1.64/6349*0.28(fraction of all charged mode)=7.0e-5
  
 
==reference==
 
==reference==
 
[http://fr.arxiv.org/pdf/hep-ex/0411030v2 upper limit of eta with KLOE detector]
 
[http://fr.arxiv.org/pdf/hep-ex/0411030v2 upper limit of eta with KLOE detector]

Latest revision as of 17:31, 30 October 2015

old

Statistics

Fit missing mass of proton

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fit missing mass of proton with linear background from g11 data. ~1.3M events
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fit missing mass of proton with linear background from g12 data. ~1.5M events

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:

Formula.gif

Test of the formula

10M events of Gaussian distribution were generated for a histograms:

Standard gaussian fit.png

side-band subtracted and plot together with the function:

Side band subtracted standard gaussian fit.png

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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fit of side-band subtracted Gaussian. bin width:1, Sigma: 1 Original magnitude: 3.98942e+02; fitted magnitude: 3.31002e+02
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fit of side-band subtracted Gaussian. bin width:0.5, Sigma: 1 Original magnitude: 1.99471e+02; fitted magnitude: 1.91370e+02
Error creating thumbnail: File missing
fit of side-band subtracted Gaussian. bin width:2, Sigma: 1 Original magnitude: 7.97884e+02; fitted magnitude: 2.49810e+02
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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fit of standard Gaussian with second-order polynomial background.
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fit of side-band subtracted Gaussian. bin width:0.5, Sigma: 1 Original magnitude: 1.99471e+04; fitted magnitude: 1.98083e+04
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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compare beam energy between data (red) and simulation (blue)
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compare momentum of proton between data (red) and simulation (blue)
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compare polar angle of proton between data (red) and simulation (blue)
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compare azimuthal angle between data (red) and simulation (blue)
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compare momentum of pi+ between data (red) and simulation (blue)
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compare polar angle of pi+ between data (red) and simulation (blue)
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compare azimuthal angle of pi+ between data (red) and simulation (blue)
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compare momentum of pi- between data (red) and simulation (blue)
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compare polar angle of pi- between data (red) and simulation (blue)
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compare azimuthal angle between data (red) and simulation (blue)

eta

Error creating thumbnail: File missing
compare beam energy between data (red) and simulation (blue)
Error creating thumbnail: File missing
compare momentum of proton between data (red) and simulation (blue)
Error creating thumbnail: File missing
compare polar angle of proton between data (red) and simulation (blue)
Error creating thumbnail: File missing
compare azimuthal angle of proton between data (red) and simulation (blue)
Error creating thumbnail: File missing
compare momentum of pi+ between data (red) and simulation (blue)
Error creating thumbnail: File missing
compare polar angle of pi+ between data (red) and simulation (blue)
Error creating thumbnail: File missing
compare azimuthal angle of pi+ between data (red) and simulation (blue)
Error creating thumbnail: File missing
compare momentum of pi- between data (red) and simulation (blue)
Error creating thumbnail: File missing
compare polar angle of pi- between data (red) and simulation (blue)
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compare azimuthal angle of pi- between data (red) and simulation (blue)

Feldman-Cousin Method

Feldman cousin upper limit.png

Revive

channel

γ p → p π+π-π+π-

selecting p η prime production and η prime decay to π+π-η

Error creating thumbnail: File missing
before selecting η prime
Error creating thumbnail: File missing
after selecting η prime. A mistake makes the selection too too tight

Production channels

Error creating thumbnail: File missing
Missing mass square vs. missing mass of p&pi;+π-
Error creating thumbnail: File missing
Fit η in spectrum of missing mass of p&pi;+π- and yield 6349

With or Without photon

Error creating thumbnail: File missing
Missing momentum vs. missing mass of p&pi;+π- after selecting missing mass around zero
Error creating thumbnail: File missing
Invariant mass of π+π- after requiring missing momentum smaller than 50 MeV

Upper Limit

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Fit of invariant mass of π+π- around η and yield upper limit of 1.64 with statistical uncertainty of bin at 5.12 and fitted yield -12.5

Therefore, the upper limit is 1.64/6349*0.28(fraction of all charged mode)=7.0e-5

reference

upper limit of eta with KLOE detector