(********************************************************) (* Experiment B has been run at beam energy 0.09, with sensitivity factor k=100, threshold function beta^3, and has observed 0 events.*) kb=100 ebb=0.09 v=Sqrt[1-(m/ebb)^2] lambda= kb*v^3 lik=Exp[-lambda] norm=NIntegrate[lik, {m, 0, ebb}] fb=lik/norm avb = NIntegrate[m*fb, {m, 0, ebb}] Plot[fb, {m, 0.07, ebb}, PlotRange->{0, 600}, AxesLabel -> {m, f}] fbmax=fb/.m->ebb f2b=If[m<ebb, fb, fbmax] Plot[f2b, {m,0.07,0.15}, PlotRange->{0, 600}, AxesLabel -> {m, f}] (* The conclusions from A + B are, with and without the condition m<ebeam, respectively (remember that the latter is improper): *) fcom1ab=fa*fb/NIntegrate[fa*fb, {m, 0, eba}] avab = NIntegrate[m*fcom1ab, {m, 0, eba}] Plot[fcom1ab, {m, 0.07, eba}, PlotRange->{0, 600}, AxesLabel -> {m, f}] fcom2ab=f2a*f2b (* Experiment C has been run at beam energy 0.1, with sensitivity factor k=10, threshold function beta^3 and, has observed 0 events. *) kc=10 ebc=0.1 v=Sqrt[1-(m/ebc)^2] lambda= kc*v^3 lik=Exp[-lambda] norm=NIntegrate[lik, {m, 0, ebc}] fc=lik/norm Plot[fc, {m, 0.07, ebc}, PlotRange->{0, 100}, AxesLabel -> {m, f}] avc = NIntegrate[m*fc, {m, 0, ebc}] fcmax=fc/.m->(ebc-0.000001) f2c=If[m<ebc, fc, fcmax] Plot[f2c, {m,0.07,0.15}, PlotRange->{0, 100}, AxesLabel -> {m, f}] (********************************************************)
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The combination of the result is done in the usual way, multiplying
the likelihoods or the final p.d.f.'s, if these were obtained from a uniform distribution.
We only see the combination of the three experiments, shown
in Fig. . Finally, the indirect
determinations are also included.
(********************************************************) (* Conclusions from A + B + C , with and without the condition m<ebeam, respectively (remember that the latter is improper): *) fcom1abc=f2a*f2b*fc/NIntegrate[f2a*f2b*fc, {m, 0, ebc}] avabc=NIntegrate[m*fcom1abc, {m, 0, ebc}] Plot[fcom1abc, {m, 0.07, ebc}, PlotRange->{0, 150}, AxesLabel -> {m, f}] fcom2abc=f2a*f2b*f2c (* Now we add independent determinations of m, deriving from normal likelihoods, and assuming uniform prior *) g1=1/sigma1/(Sqrt[2*Pi])*Exp[-(m-mu1)^2/2/sigma1^2] g2=1/sigma2/(Sqrt[2*Pi])*Exp[-(m-mu2)^2/2/sigma2^2] mu1=0.09 sigma1=0.04 mu2=0.15 sigma2=0.04 (* The two overall (improper) priors may be a uniform, or 1/m, i.e. flat in ln(m), to express initial uncertainty on the order of magnitude of m *) p1=1 p2=1/m final1=fcom2abc*g1*g2*p1/NIntegrate[fcom2abc*g1*g2*p1, {m, 0, 10}] mean1=NIntegrate[m*final1, {m, 0, 10}] std1=Sqrt[NIntegrate[m^2*final1, {m, 0, 10}]-mean1^2] Plot[final1, {m, 0.0, 0.25}, PlotRange->{0, 20}, AxesLabel -> {m, f}] final2=fcom2abc*g1*g2*p2/NIntegrate[fcom2abc*g1*g2*p2, {m, 0, 10}] mean2=NIntegrate[m*final2, {m, 0, 10}] std2=Sqrt[NIntegrate[m^2*final2, {m, 0, 10}]-mean2^2] Plot[final2, {m, 0.0, 0.25}, PlotRange->{0, 20}, AxesLabel -> {m, f}] (********************************************************)Finally, the two extra pieces of information enable us to constrain the mass also on the upper side and to arrive at a proper distribution (see Fig.
From the final distribution we can evaluate, as usual, all the quantities that we find interesting to summarize the result with a couple of numbers. For a more realistic analysis of this problem see Ref. [26].
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