How to use the smelli.ckm function in smelli

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github smelli / smelli / smelli / View on Github external
def test_sm(self):
        scheme = ckm.CKMSchemeRmuBtaunuBxlnuDeltaM()
        Vus, Vcb, Vub, delta = scheme.ckm_np(None)
        self.assertAlmostEqual(Vus, 0.225, delta=0.001)
        self.assertAlmostEqual(Vcb, 0.041, delta=0.0015)
        self.assertAlmostEqual(Vub, 0.004, delta=0.0005)
        self.assertAlmostEqual(delta, 1.15, delta=0.2)
github smelli / smelli / smelli / View on Github external
def test_ckm_np(self):
        scheme = ckm.CKMSchemeRmuBtaunuBxlnuDeltaM()
        w = Wilson({'lq3_1123': 0.04e-5,
                    'lq3_3313': 0.008e-5,
                    'lq3_2212': 0.5e-7,
                    'qq1_2323': 1e-12,
                    }, 91.1876, 'SMEFT', 'Warsaw')
        Vus, Vcb, Vub, delta = scheme.ckm_np(w, iterate=10)
        w.set_option('parameters', {'Vus': Vus, 'Vcb': Vcb, 'Vub': Vub, 'gamma': delta})
        _Vus, _Vcb, _Vub, _delta = scheme._ckm_np(w, Vus=Vus, Vcb=Vcb, Vub=Vub, delta=delta)
        self.assertAlmostEqual(_Vus, Vus, places=2)
        self.assertAlmostEqual(_Vcb, Vcb, places=2)
        self.assertAlmostEqual(_Vub, Vub, places=2)
        self.assertAlmostEqual(_delta, delta, places=2)


A Python package providing a global likelihood function in the space of dimension-6 Wilson coefficients of the Standard Model Effective Field Theory (SMEFT)

Latest version published 5 months ago

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