lfv.py

def Fme(x,xmin=0.996,xmax=1.005,xfit=1.001):
    """Fixing near to one values
     xmin: close to 1 from below
     xmax: close to 1 from above
     xfit:  optimized 1 limit
    """
    x=np.asarray(x)
    if x.shape:
        x[np.logical_and(x>xmin,x<xmax)]=xfit
    else:
        if x>xmin and x<xmax:
            x=xfit
        
    return (1.-6.*x+3.*x**2+2.*x**3-6*x**2*np.log(x))/(6.*(x-1.)**4)

def LFV(SM,dp,yuk):
    '''Oscar Notes with \pi^2 -> \pi
    '''
    import numpy as np
    const=False
    SM=pd.Series(SM)
    y=np.matrix(yuk)
    
    dp=pd.Series(dp)
    FMl=[]
    for i in range(1,4):
        dp['Mtrp%d' %i]=dp['Mtr0%d' %i] #degenerate fermions
        FMl.append(1./(dp.MHC**2)*Fme(dp['Mtr0%d' %i]**2/dp.MHC**2)\
                   -1./(dp['Mtr0%d' %i]**2)*( Fme(dp.MH0**2/dp['Mtrp%d' %i]**2)\
                                            + Fme(dp.MA0**2/dp['Mtrp%d' %i]**2)   )  )
    FM=np.matrix(np.diag(FMl))
    
    Brmueg  =(3.*SM.alpha_em/(4.*16.*np.pi*SM.G_F**2)*np.abs(y[:,0].T*FM*y[:,1].conjugate())**2)[0,0]
    Brtaumug=(3.*SM.alpha_em/(4.*16.*np.pi*SM.G_F**2)*np.abs(y[:,1].T*FM*y[:,2].conjugate())**2)[0,0]
    if (Brmueg<5.7e-13):
       if (Brtaumug<4.5e-8):
           const=True

    return Brmueg,Brtaumug,const