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cl95cms.C
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/* --------------------------------------------------------------------------------
Bayesian 95% CL limit and expected (average) 95% CL limit calculator for cross-section with Poisson statistics
The code shoudl be run from root in the compiled mode:
.L cl95cms.c++
Usage to get actual and expected limit respectively:
sigma95 = CL95(ilum, slum, eff, seff, bck, sbck, n, gauss = false, nuisanceModel = 0)
sigma95A = CLA(ilum, slum, eff, seff, bck, sbck, nuisanceModel = 0)
Inputs: ilum - Nominal integrated luminosity (pb^-1)
slum - Absolute error on the integrated luminosity
eff - Nominal value of the efficiency times acceptance (in range 0 to 1)
seff - Absolute error on the efficiency times acceptance
bck - Nominal value of the background estimate
sbck - Absolute error on the background
n - Number of observed events (not used for the expected limit)
gauss - if true, use Gaussian statistics for signal instead of Poisson; automatically false for n = 0. Always false for expected limit calculations
nuisanceModel - distribution function used in integration over nuisance parameters:
0 - Gaussian, 1 - lognormal, 2 - gamma; (automatically 0 when gauss == true)
Limits should be obtained & compared for each allowed value of the nuisanceModel parameter. The justification for doing this is given in
http://www.physics.ucla.edu/~cousins/stats/cousins_lognormal_prior.pdf
A flat prior is assumed for the cross-section.
Written by Greg Landsberg; v1.0 February 27, 2003
Modified by Greg Landsberg; v1.1 March 18, 2003 - some bugs fixed;
automatic graph scaling
Modified by Greg Landsberg; v1.2 May 15, 2003 - clean-up of the code
Modified by Greg Landsberg; v1.3 June 2, 2005 - further clean-up;
protection against numeric instability;
increased table for Poisson distribution;
Modified by Greg Landsberg; v1.3 June 2, 2010 - lognormal and Gamma nuisance
parameter integration
-------------------------------------------------------------------------------- */
#include <iostream>
#include <stdlib.h>
#include "TMath.h"
#include "TF1.h"
#include "TH1.h"
#include "TArrow.h"
#include "TCanvas.h"
bool VERBOSE = false;
using namespace std;
static Double_t A0, sA, B0, sB, epsilon, MaxSig = 100.;
static Double_t MinLike = 1.e-6, Precision = 1.e-5;
static Int_t N;
static bool lGauss = kFALSE, plot = kTRUE;
static Int_t I = 0;
static Double_t sigma_a = 0., sigma_b = 0., tau_a = 0., tau_b = 0.;
Double_t Likelihood(Double_t *x, Double_t *p);
Double_t Inner(Double_t *x, Double_t *par);
Double_t Outer(Double_t *x, Double_t *p);
Double_t Poisson(Double_t Mu, Int_t n);
Double_t CL95(Double_t ilum, Double_t slum, Double_t eff, Double_t seff, Double_t bck, Double_t sbck, Int_t n, Bool_t gauss = kFALSE, Int_t nuisanceModel = 0);
Double_t CLA(Double_t ilum, Double_t slum, Double_t eff, Double_t seff, Double_t bck, Double_t sbck, Int_t bckint = 0);
void UL_1fb(Double_t s, Double_t b, Int_t mass);
Double_t CL95(Double_t ilum, Double_t slum, Double_t eff, Double_t seff, Double_t bck, Double_t sbck, Int_t n, Bool_t gauss, Int_t nuisanceModel)
{
Double_t xmax = MaxSig;
Double_t x[1], p[1];
// Get the nominal values of input (nuisance) parameters and their uncertainties.
A0 = ilum*eff;
sA = sqrt(ilum*seff*ilum*seff + eff*slum*eff*slum);
B0 = bck;
sB = sbck;
epsilon = TMath::Max(Precision/ilum,1.e-4);
//
lGauss = gauss;
if (n == 0) lGauss = kFALSE;
// If using logNormal or gamma functions for nuisance parameters, must express these parameters in the way expected
// by the ROOT implementations of these functions. (See web page given above for details).
if (!gauss)
{
if (nuisanceModel < 0 || nuisanceModel > 2)
{
cout << "Incorrect nuissance parameter integration model. Gaussian will be used." << endl;
nuisanceModel = 0;
}
if (plot) cout << "Poisson 95% CL limit with ";
I = nuisanceModel;
if (nuisanceModel == 0)
{
if (plot) cout << "Gaussian ";
}
else if (nuisanceModel == 1) // Lognormal distribution is used
{
if (B0 > 0) sigma_b = TMath::Log(1. + sB/B0);
sigma_a = TMath::Log(1. + sA/A0);
if (plot) cout << "Lognormal ";
}
else if (nuisanceModel == 2) // Gamma distribution is used
{
if (plot) cout << "Gamma ";
if (sA > 0)
{
sigma_a = 1. + A0*A0/sA/sA;
tau_a = A0/sA/sA;
}
if (sB > 0)
{
sigma_b = 1. + B0*B0/sB/sB;
tau_b = B0/sB/sB;
}
}
} else if (plot) cout << "Gaussian 95% CL limit with Gaussian ";
if (plot) cout << "nuisance parameter integration will be used" << endl;
// Do some iteration to determine in which cross-section range 0 to xmax the likelihood function is sufficiently
// large to be of interest.
N = n;
x[0] = xmax;
p[0] = 0.;
//
Double_t delta = (Likelihood(x,p) - MinLike)/MinLike;
while (delta > 0)
{
if (plot) cout << "delta = " << delta << endl;
xmax = 2.*xmax;
x[0] = xmax;
delta = (Likelihood(x,p) - MinLike)/MinLike;
}
//
Double_t x1 = 0, x2 = xmax;
while (fabs(delta) > epsilon)
{
if (delta > 0) x1 = x[0];
else x2 = x[0];
x[0] = (x1 + x2)/2.;
delta = (Likelihood(x,p) - MinLike)/MinLike;
}
xmax = x[0];
//
Int_t i = 0;
if (xmax > 1)
{
while (xmax > 10.)
{
xmax /= 10.;
i++;
}
xmax = (Int_t)(xmax + 1)*pow(10,i);
}
else
{
while (xmax < 1.)
{
xmax *= 10.;
i++;
}
xmax = (Int_t)(xmax + 1)*pow(10,-i);
}
// Calculate the likelood function as a function of the cross-section
TF1 like("Likelihood",Likelihood,0.,xmax,0);
if (plot) cout << "Likelihood function is evaluated over [0," << xmax << "] " << endl;
// Integrate the likelihood function, and by iteration determine the cross-section value xmax below which
// 95% of the integral is contained.
Double_t Norm = like.Integral(0.,xmax,p,epsilon);
if (plot) cout << "likelihood normalization: " << Norm << endl;
//
// Double_t x1, x2, delta;
x1 = 0;
x2 = xmax;
xmax = (x1 + x2)/2.;
delta = like.Integral(0,xmax,p,epsilon)/Norm - 0.95;
//
while (fabs(delta) > epsilon)
{
if (delta < 0) x1 = xmax;
else x2 = xmax;
xmax = (x1 + x2)/2.;
delta = like.Integral(0,xmax,p,epsilon)/Norm - 0.95;
}
// Plot the likelihood function and show the upper limit.
x[0] = like.GetMaximumX(0.,MaxSig);
if (plot)
{
TCanvas c("Likelihood");
like.Draw();
TArrow arrow(xmax,Likelihood(x,p)/7.,xmax,0,0.04);
arrow.SetLineWidth(3.);
arrow.Draw();
c.Print("Likelihood.eps");
}
if (plot) cout << "Upper 95% C.L. limit on signal = " << xmax << " pb" << endl;
//
return xmax;
}
Double_t Likelihood(Double_t *x, Double_t *p)
// Calculate the likelihood to see N events as a function of the cross-section x[0].
// This corresponds to the probability that the cross-section x[0] is correct, given a flat prior.
// (N is a global parameter. The parameter p is dummy).
{
Double_t retval = 0.;
if (sB == 0.)
{
if (sA == 0.)
{
// In this case, there is zero uncertainty on the nuisance parameters,
// so the likelihood is simply a Poission (or Gaussian).
if (lGauss) retval = TMath::Gaus(N-B0-x[0]*A0,0.,TMath::Sqrt(N),kTRUE);
else retval = Poisson(B0+x[0]*A0,N);
}
else
{
// In this case, the parameter A0 (lumi*effi) is uncertain, so integrate
// the simple Poission over all possible values of A0, weighted by apriori
// prob. that that parameter is correct.
Double_t par[2];
// TF1 *in = new TF1("Inner",Inner,0.,TMath::Max(A0 + 5.*sA,2);
TF1 *in = new TF1("Inner",Inner,0.,A0 + 5.*sA,2);
par[0]=B0; // background value
par[1]=x[0]; // signal cross section value
if (I == 0)
{
Double_t low = A0 > 5.*sA ? A0 - 5.*sA : 0.;
retval = in->Integral(low,A0+5.*sA,par,epsilon);
} else {
Double_t low = 0., high = A0 + 5.*sA, tmp = 1.;
while (tmp > epsilon)
{
tmp = in->Integral(low,high,par,epsilon);
retval += tmp;
low = high;
high *= 2;
}
}
delete in;
}
}
else
{
// In this case, both the parameters A0 (lumi*effi) and B0 (background) are uncertain,
// so integrate over both of them.
// TF1 *out = new TF1("Outer",Outer,0.,TMath::Max(B0 + 5.*sB,1000.),1);
TF1 *out = new TF1("Outer",Outer,0.,B0 + 5.*sB,1);
if (I == 0)
{
Double_t low = B0 > 5.*sB ? B0 - 5.*sB : 0.;
retval = out->Integral(low,B0+5.*sB,x,epsilon);
} else {
Double_t low = 0., high = B0 + 5.*sB, tmp = 1.;
while (tmp > epsilon)
{
tmp = out->Integral(low,high,x,epsilon);
retval += tmp;
low = high;
high *= 2;
}
}
delete out;
}
return retval;
}
Double_t Outer(Double_t *x, Double_t *p)
// When calculating Poisson probabilities, allow for uncertainty in B0 (background) by summing over all possible
// values of this parameter, weighted by the apriori probability that it is correct.
// This function can make use of function Inner, which also takes into account uncertainty in lumi*effi.
{
Double_t retval;
if (sA == 0.)
{
// cout << "x, sigma_b, B0 = " << x[0] << ", " << sigma_b << ", " << B0 << endl;
if (lGauss) retval = TMath::Gaus(x[0],B0,sB,kTRUE)*TMath::Gaus(N-x[0]-p[0]*A0,0.,TMath::Sqrt(N),kTRUE);
else if (I == 0) retval = TMath::Gaus(x[0],B0,sB,kTRUE)*Poisson(x[0]+p[0]*A0,N);
else if (I == 1) retval = TMath::LogNormal(x[0], sigma_b, 0., B0)*Poisson(x[0]+p[0]*A0,N);
else if (I == 2) retval = TMath::GammaDist(x[0], sigma_b, 0., 1./tau_b)*Poisson(x[0]+p[0]*A0,N);
else return 0;
}
else
{
Double_t par[2];
// TF1 *in = new TF1("Inner",Inner,0.,TMath::Max(A0 + 5.*sA,1000.),2);
TF1 *in = new TF1("Inner",Inner,0.,A0 + 5.*sA,2);
par[0]=x[0]; // background value
par[1]=p[0]; // signal cross section value
Double_t low = A0 > 5.*sA ? A0 - 5.*sA : 0.;
if (I == 0) retval = TMath::Gaus(x[0],B0,sB,kTRUE)*in->Integral(low,A0+5.*sA,par,epsilon);
else if (I == 1) retval = TMath::LogNormal(x[0], sigma_b, 0., B0)*in->Integral(0.,A0+5.*sA,par,epsilon);
else if (I == 2) retval = TMath::GammaDist(x[0], sigma_b, 0., 1./tau_b)*in->Integral(0.,A0+5.*sA,par,epsilon);
else return 0;
delete in;
}
return retval;
}
Double_t Inner(Double_t *x, Double_t *par)
// When calculating Poisson probabilities, allow for uncertainty in A0 (lumi*effi) by summing over all possible
// values of this parameter, weighted by the apriori probability that it is correct.
{
Double_t sigma, B;
//
B = par[0];
sigma = par[1];
if (lGauss) return TMath::Gaus(x[0],A0,sA,kTRUE)*TMath::Gaus(N-B-sigma*x[0],0.,TMath::Sqrt(N),kTRUE);
else if (I == 0) return TMath::Gaus(x[0],A0,sA,kTRUE)*Poisson(B+sigma*x[0],N);
else if (I == 1) return TMath::LogNormal(x[0], sigma_a, 0., A0)*Poisson(B+sigma*x[0],N);
else if (I == 2) return TMath::GammaDist(x[0], sigma_a, 0., 1./tau_a)*Poisson(B+sigma*x[0],N);
else return 0;
}
Double_t Poisson(Double_t Mu, Int_t n)
// Calculate the Poission prob. of seeing n events given an expectation of Mu.
{
if (Mu <= 0) return n > 0 ? 0. : 1.;
Double_t logP;
//
// Tabulate values of -\sum log(i+2) up to n=1000 for faster calculation
//
// Double_t sum = 0.;
// for (Int_t j = 0; j < 999; j++)
// {
// sum -= log(j+2.);
// printf("%10f, ",sum);
// if (((j+1)/10)*10 == j + 1) printf("\n");
// }
Double_t logTable[999] = {-0.693147, -1.791759, -3.178054, -4.787492, -6.579251, -8.525161, -10.604603, -12.801827, -15.104413, -17.502308,
-19.987214, -22.552164, -25.191221, -27.899271, -30.671860, -33.505073, -36.395445, -39.339884, -42.335616, -45.380139,
-48.471181, -51.606676, -54.784729, -58.003605, -61.261702, -64.557539, -67.889743, -71.257039, -74.658236, -78.092224,
-81.557959, -85.054467, -88.580828, -92.136176, -95.719695, -99.330612, -102.968199, -106.631760, -110.320640, -114.034212,
-117.771881, -121.533082, -125.317271, -129.123934, -132.952575, -136.802723, -140.673924, -144.565744, -148.477767, -152.409593,
-156.360836, -160.331128, -164.320112, -168.327445, -172.352797, -176.395848, -180.456291, -184.533829, -188.628173, -192.739047,
-196.866182, -201.009316, -205.168199, -209.342587, -213.532241, -217.736934, -221.956442, -226.190548, -230.439044, -234.701723,
-238.978390, -243.268849, -247.572914, -251.890402, -256.221136, -260.564941, -264.921650, -269.291098, -273.673124, -278.067573,
-282.474293, -286.893133, -291.323950, -295.766601, -300.220949, -304.686857, -309.164194, -313.652830, -318.152640, -322.663499,
-327.185288, -331.717887, -336.261182, -340.815059, -345.379407, -349.954118, -354.539086, -359.134205, -363.739376, -368.354496,
-372.979469, -377.614198, -382.258589, -386.912549, -391.575988, -396.248817, -400.930948, -405.622296, -410.322777, -415.032307,
-419.750806, -424.478193, -429.214392, -433.959324, -438.712914, -443.475088, -448.245773, -453.024896, -457.812388, -462.608179,
-467.412200, -472.224384, -477.044665, -481.872979, -486.709261, -491.553448, -496.405478, -501.265291, -506.132825, -511.008023,
-515.890825, -520.781174, -525.679014, -530.584288, -535.496943, -540.416924, -545.344178, -550.278652, -555.220294, -560.169054,
-565.124881, -570.087726, -575.057539, -580.034273, -585.017879, -590.008312, -595.005524, -600.009471, -605.020106, -610.037386,
-615.061266, -620.091704, -625.128657, -630.172082, -635.221938, -640.278184, -645.340779, -650.409683, -655.484857, -660.566261,
-665.653857, -670.747608, -675.847474, -680.953420, -686.065407, -691.183401, -696.307365, -701.437264, -706.573062, -711.714726,
-716.862220, -722.015512, -727.174567, -732.339353, -737.509837, -742.685987, -747.867770, -753.055156, -758.248113, -763.446610,
-768.650617, -773.860103, -779.075039, -784.295395, -789.521141, -794.752250, -799.988692, -805.230439, -810.477463, -815.729736,
-820.987232, -826.249922, -831.517780, -836.790780, -842.068894, -847.352098, -852.640365, -857.933670, -863.231987, -868.535292,
-873.843560, -879.156766, -884.474886, -889.797896, -895.125772, -900.458491, -905.796029, -911.138363, -916.485471, -921.837329,
-927.193915, -932.555207, -937.921183, -943.291821, -948.667100, -954.046997, -959.431492, -964.820564, -970.214191, -975.612354,
-981.015031, -986.422203, -991.833849, -997.249950, -1002.670485, -1008.095435, -1013.524780, -1018.958502, -1024.396582, -1029.838999,
-1035.285737, -1040.736775, -1046.192096, -1051.651682, -1057.115514, -1062.583574, -1068.055844, -1073.532308, -1079.012947, -1084.497744,
-1089.986681, -1095.479743, -1100.976911, -1106.478169, -1111.983501, -1117.492889, -1123.006318, -1128.523771, -1134.045232, -1139.570685,
-1145.100114, -1150.633503, -1156.170838, -1161.712101, -1167.257279, -1172.806355, -1178.359314, -1183.916142, -1189.476824, -1195.041344,
-1200.609689, -1206.181843, -1211.757792, -1217.337522, -1222.921018, -1228.508267, -1234.099254, -1239.693965, -1245.292387, -1250.894506,
-1256.500308, -1262.109780, -1267.722908, -1273.339679, -1278.960080, -1284.584097, -1290.211718, -1295.842930, -1301.477720, -1307.116075,
-1312.757982, -1318.403428, -1324.052403, -1329.704892, -1335.360884, -1341.020366, -1346.683326, -1352.349753, -1358.019634, -1363.692957,
-1369.369711, -1375.049884, -1380.733463, -1386.420439, -1392.110798, -1397.804530, -1403.501624, -1409.202067, -1414.905850, -1420.612960,
-1426.323387, -1432.037120, -1437.754148, -1443.474460, -1449.198045, -1454.924892, -1460.654992, -1466.388333, -1472.124906, -1477.864699,
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logP = -Mu + n*log(Mu);
if (n >= 2) logP += logTable[TMath::Min(n,1000)-2];
for (Int_t i = 1001; i <= n; i++) logP -= log((Double_t) i);
return exp(logP);
}//
Double_t CLA(Double_t ilum, Double_t slum, Double_t eff, Double_t seff, Double_t bck, Double_t sbck, Int_t bckint)
{
plot = kFALSE;
Double_t CL95A = 0, precision = 1.e-4;
Int_t i;
for (i = bck; i >= 0; i--)
{
//
Double_t s95 = CL95(ilum, slum, eff, seff, bck, sbck, i, kFALSE, bckint);
Double_t s95w =s95*Poisson(bck,i);
CL95A += s95w;
if( VERBOSE && i % 10 == 0 ) cout << "n = " << i << "; 95% C.L. = " << s95 << " pb; weighted 95% C.L. = " << s95w << " pb; running <s95> = " << CL95A << " pb" << endl;
//
if (s95w < CL95A*precision) break;
}
cout << "Lower bound on n has been found at " << i+1 << endl;
//
for (i = bck+1; ; i++)
{
Double_t s95 = CL95(ilum, slum, eff, seff, bck, sbck, i, kFALSE, bckint);
Double_t s95w =s95*Poisson(bck,i);
CL95A += s95w;
if( VERBOSE && i % 10 == 0 ) cout << "n = " << i << "; 95% C.L. = " << s95 << " pb; weighted 95% C.L. = " << s95w << " pb; running <s95> = " << CL95A << " pb" << endl;
//
if (s95w < CL95A*precision) break;
}
cout << "Upper bound on n has been found at " << i << endl;
//
cout << "Average upper 95% C.L. limit = " << CL95A << " pb" << endl;
return CL95A;
}
void UL_1fb(Double_t s, Double_t b, Int_t mass) {
float nTotalEvents;
if( mass==200 ) nTotalEvents = 109110.;
else if( mass==250 ) nTotalEvents = 109981.;
else if( mass==300 ) nTotalEvents = 109101.;
else if( mass==350 ) nTotalEvents = 106452.;
else if( mass==400 ) nTotalEvents = 109924.;
else if( mass==450 ) nTotalEvents = 109906.;
else if( mass==500 ) nTotalEvents = 102857.;
else {
std::cout << "mass " << mass << " currently not supported." << std::endl;
exit(11818);
}
float crossSection;
if( mass==200 ) crossSection = (5.4218+0.64243+0.015972+0.056746+0.10830)*0.2537*0.067316*0.7*2.;
else if( mass==250 ) crossSection = (3.4120+0.43490+0.0073067+0.023553+0.044954)*0.2951*0.067316*0.7*2.;
else if( mass==300 ) crossSection = (2.5001+0.30317+0.0038098+0.010992+0.021079)*0.3053*0.067316*0.7*2.;
else if( mass==350 ) crossSection = (2.4983+0.21635+0.0021911+0.0055876+0.010794)*0.3023*0.067316*0.7*2.;
else if( mass==400 ) crossSection = (2.0608+0.15739+0.0054837+0.00030308+0.0059045)*0.2664*0.067316*0.7*2.;
else if( mass==450 ) crossSection = (1.3445+0.11630+0.00087374+0.0017291+0.00033991)*0.2582*0.067316*0.7*2.;
else if( mass==500 ) crossSection = (0.83396+0.087068+0.00058401+0.0010272+0.0020377)*0.2602*0.067316*0.7*2.;
else {
std::cout << "mass " << mass << " currently not supported." << std::endl;
exit(11818);
}
crossSection *= 1000.; //in fb
float eventWeight = crossSection / nTotalEvents;
float nEvents_passed = s/eventWeight;
float effS = nEvents_passed / nTotalEvents;
float ul = 1000.*CLA( 1000., 0., effS, 0., b, 0. );
std::cout << std::endl << std::endl;
std::cout << " ------> UL (1 fb-1, no errors) : " << ul<< " fb" << std::endl;
std::cout << std::endl << std::endl;
std::cout << "effS: " << effS << std::endl;
std::cout << "nEvents_passed: " << nEvents_passed << std::endl;
}