#include <Juca.h>

Inheritance diagram for Juca:

Collaboration diagram for Juca:

Public Member Functions
	Juca ()

	~Juca ()

void	add (const char s, ex pred, observable ob)

parameters	generateparameters () const

lst	getlist (const parameters &p) const

Public Member Functions inherited from Model
	Model ()

virtual	~Model ()

virtual parameters	generateparameters (int max=0) const =0

virtual int	veto (const parameters &p, int max=0) const

double	likelihood (const parameters &p, bool check=1, int max=0) const
	calculates the probability of getting all the experimental measures if the model describes the reality More...

double	loglike (const parameters &p, bool check=1, int max=0) const

Public Attributes
const possymbol	Mu

const possymbol	Md

const possymbol	Mc

const possymbol	Ms

const possymbol	Mt

const possymbol	Mb

const possymbol	lambda

const possymbol	A

const realsymbol	rho

const realsymbol	eta

lst	replacements

Public Attributes inherited from Model
TRandom3 *	r

Detailed Description

Definition at line 15 of file Juca.h.

Constructor & Destructor Documentation

Juca::Juca ( )

inline

Definition at line 18 of file Juca.h.

       :Mu("Mu"), Md("Md"), Mc("Mc"), Ms("Ms"), Mt("Mt"), Mb("Mb"), lambda("lambda"), A("A"),rho("rho"),eta("eta"){
     
         add("",lambda,new gauss2obs(0.22535,0.00065));
         add("",A,new gauss2obs(0.811, 0.017));
         add("",rho,new gauss2obs(0.131, 0.02));
         add("",eta,new gauss2obs(0.345, 0.014));
         add("",Mu,new gauss2obs(1.27e-3, 0.46e-3));
         add("",Md,new gauss2obs(2.9e-3, 1.22e-3));
         add("",Ms,new gauss2obs(55e-3, 16e-3));
         add("",Mc,new gauss2obs(0.619, 0.084));
         add("",Mb,new gauss2obs(2.89, 0.09));
         add("",Mt,new gauss2obs(171.7, 3.0));
 }

Juca::~Juca ( )

inline

Definition at line 32 of file Juca.h.

32 {}

Member Function Documentation

void Juca::add	(	const char *	s,
		ex	pred,
		observable *	ob
	)

inline

Definition at line 36 of file Juca.h.

                                                   {
         ex p=collect_common_factors(expand(pred.subs(replacements)));
         push_back(prediction(ob,p));
         }

parameters Juca::generateparameters ( ) const

inline

Definition at line 41 of file Juca.h.

                                      {
         parameters p;
         
         //buu
         p.push_back(freeparameter(-5,0,r));
         //auu
         p.push_back(freeparameter(-5,0,r));
         //add
         p.push_back(freeparameter(-5,0,r));
         //bdd
         p.push_back(freeparameter(-5,0,r));
         //eu
         p.push_back(freeparameter(-2,2,r));
         //gu
         p.push_back(freeparameter(-2,2,r));
         //ed
         p.push_back(freeparameter(-2,2,r));
         //gd
         p.push_back(freeparameter(-2,2,r));
         //nu
         p.push_back(freeparameter(-2,3,r));
         //nd
         p.push_back(freeparameter(-2,3,r));
         //cuu
         //p.push_back(freeparameter(-5,0,r));
         //cdd
         p.push_back(freeparameter(-5,0,r));
         //hu
         //p.push_back(freeparameter(-2,2,r));
         //hd
         p.push_back(freeparameter(-2,2,r));
         
         return p;
 }

lst Juca::getlist ( const parameters & p ) const

inlinevirtual

Implements Model.

Definition at line 77 of file Juca.h.

Referenced by main().

                                        {
         
         
         double buu = pow(10.0,p[0].value), auu = pow(10.0,p[1].value);
         double add = pow(10.0,p[2].value), bdd = pow(10.0,p[3].value);
         double eu =p[4].value, gu = p[5].value;
         double ed = p[6].value, gd = p[7].value;
         //double cuu = pow(10.0,p[10].value); 
         double cdd = pow(10.0,p[10].value);
         //double hu =p[12].value;
         double hd= p[11].value;
         
 complex<double> bu = buu*exp(complex<double>(0,gu*M_PI_2)), au = auu*exp(complex<double>(0,eu*M_PI_2));
 complex<double> ad = add*exp(complex<double>(0,ed*M_PI_2)), bd = bdd*exp(complex<double>(0,gd*M_PI_2)); 
 //complex<double> cu = cuu*exp(complex<double>(0,hu*M_PI_2));
 complex<double> cu = bu;
 complex<double> cd = cdd*exp(complex<double>(0,hd*M_PI_2));
 //complex<double> cd = bd;
 
 //Matrix3cd X = Matrix3cd::Random(3,3);
 Matrix3cd mu,md;
 mu<<1,1.0,1.0+au+bu,1.0,1.0,1.0+bu,1.0+bu+au,1.0+bu,1.0+cu;
 md<<1,1.0,1.0+ad+bd,1.0,1.0,1.0+bd,1.0+bd+ad,1.0+bd,1.0+cd;
         mu=mu*pow(10.0,p[8].value);
         md=md*pow(10.0,p[9].value);
 
 Matrix3cd Hu = mu*mu.adjoint(), Hd = md*md.adjoint();
 SelfAdjointEigenSolver<Matrix3cd> usolver(Hu), dsolver(Hd);
 Vector3d Du=usolver.eigenvalues();//.asDiagonal();
 Vector3d Dd=dsolver.eigenvalues();//.asDiagonal();
 Matrix3cd VLd=dsolver.eigenvectors().adjoint();
 Matrix3cd VLu=usolver.eigenvectors().adjoint();
 Matrix3cd Vckm=VLu*VLd.adjoint();
 double lambda0=sqrt(norm(Vckm(0,1))/(norm(Vckm(0,0))+norm(Vckm(0,1))));
 double A0=abs(Vckm(1,2))/abs(Vckm(0,1))/lambda0;
 complex<double> rhoeta=-Vckm(0,0)*conj(Vckm(0,2))/Vckm(1,0)/conj(Vckm(1,2));
 double rho0=real(rhoeta), eta0=imag(rhoeta);
         
         return lst(lambda==lambda0,A==A0,rho==rho0,eta==eta0,Mu==sqrt(abs(Du[0])),Md==sqrt(abs(Dd[0])),Mc==sqrt(abs(Du[1])),Ms==sqrt(abs(Dd[1])),Mt==sqrt(abs(Du[2])),Mb==sqrt(abs(Dd[2])));
 }

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