Module flavio.physics.bdecays.test_bll

Classes

class TestBll (methodName='runTest')

Expand source code

class TestBll(unittest.TestCase):
    def test_bsll(self):
        # just some trivial tests to see if calling the functions raises an error
        self.assertGreater(br_lifetime_corr(0.08, -1), 0)
        self.assertEqual(len(amplitudes(par, wc, 'Bs', 'mu', 'mu')), 2)
        # ADeltaGamma should be +1.0 in the SM
        self.assertEqual(ADeltaGamma(par, wc, 'Bs', 'mu'), 1.0)
        self.assertEqual(flavio.sm_prediction('ADeltaGamma(Bs->mumu)'), 1.0)
        # BR should be around 3.5e-9
        self.assertAlmostEqual(br_inst(par, wc, 'Bs', 'mu', 'mu')*1e9, 3.5, places=0)
        # correction factor should enhance the BR by roughly 7%
        self.assertAlmostEqual(br_timeint(par, wc, 'Bs', 'mu', 'mu')/br_inst(par, wc, 'Bs', 'mu', 'mu'), 1.07, places=2)
        # ratio of Bs->mumu and Bs->ee BRs should be roughly given by ratio of squared masses
        self.assertAlmostEqual(
            br_timeint(par, wc_e, 'Bs', 'e', 'e')/br_timeint(par, wc, 'Bs', 'mu', 'mu')/par['m_e']**2*par['m_mu']**2,
            1., places=2)
        # comparison to 1311.0903
        self.assertAlmostEqual(abs(ckm.xi('t','bs')(par))/par['Vcb'], 0.980, places=3)
        self.assertAlmostEqual(br_timeint(par, wc, 'Bs', 'mu', 'mu')/3.65e-9, 1, places=1)
        self.assertAlmostEqual(br_timeint(par, wc_e, 'Bs', 'e', 'e')/8.54e-14, 1, places=1)
        self.assertAlmostEqual(br_timeint(par, wc_tau, 'Bs', 'tau', 'tau')/7.73e-7, 1, places=1)

    def test_bsll_classes(self):
        par_default = default_parameters.get_central_all()
        self.assertAlmostEqual(br_timeint(par_default, wc_tau, 'Bs', 'tau', 'tau')/Observable['BR(Bs->tautau)'].prediction_central(default_parameters, wc_obj), 1, places=4)
        self.assertAlmostEqual(br_timeint(par_default, wc_e, 'Bs', 'e', 'e')/Observable['BR(Bs->ee)'].prediction_central(default_parameters, wc_obj), 1, places=4)
        self.assertAlmostEqual(br_timeint(par_default, wc, 'Bs', 'mu', 'mu')/Observable['BR(Bs->mumu)'].prediction_central(default_parameters, wc_obj), 1, places=4)

    def test_bsll_lfv(self):
        # test for errors
        self.assertEqual(flavio.sm_prediction('BR(B0->emu)'), 0)
        self.assertEqual(flavio.sm_prediction('BR(Bs->taumu)'), 0)
        self.assertEqual(flavio.sm_prediction('BR(B0->emu,mue)'), 0)
        self.assertEqual(flavio.sm_prediction('BR(Bs->mutau,taumu)'), 0)
        wc = flavio.WilsonCoefficients()
        wc.set_initial({'C10_bdemu': 1, 'C10_bdmue': 2}, scale=4.8)
        self.assertAlmostEqual(flavio.np_prediction('BR(B0->mue)', wc)
                        /flavio.np_prediction('BR(B0->emu)', wc), 4)
        self.assertAlmostEqual(flavio.np_prediction('BR(B0->emu,mue)', wc)
                        /flavio.np_prediction('BR(B0->emu)', wc), 5)

    def test_EffectiveLifetimes(self):
        # In this test we trivially check that the prefactors in (22) and (28) of arXiv:1204.1737 are the same

        ys     = .5*par['DeltaGamma/Gamma_Bs']
        tau_Bs = par['tau_Bs']

        wc_dict = {'e': wc_e, 'mu': wc, 'tau': wc_tau}

        for l in ['e', 'mu', 'tau']:
            ADG    = ADeltaGamma(par, wc_dict[l], 'Bs', l)
            tau    = tau_ll(wc_dict[l], par, 'Bs', l)

            prefactor1 = br_lifetime_corr(ys, ADG)        # eq. (22) of arXiv:1204.1737
            prefactor2 = 2.  - (1.-ys**2) * tau / tau_Bs  # eq. (28) of arXiv:1204.1737

            self.assertAlmostEqual(prefactor1, prefactor2, places=8)

    def test_BR_Bs_to_mumu(self):
        # cross check formula with 2nd implementation

        # use formula (1.2) and (1.3) of 1407.2771
        def amplitudes_Amsterdam_Bs_mumu(par, wc):
            # masses
            scale = flavio.config['renormalization scale']['bll']
            mmu = par['m_mu']
            mB  = par['m_Bs']
            mb  = running.get_mb(par, scale, nf_out=5)
            ms  = running.get_ms(par, scale, nf_out=5)
            # Wilson coefficients
            C10SM = -4.188642825319258 #SM value for C10 -4.134#
            C10m  = (C10SM+wc['C10_bsmumu']) - wc['C10p_bsmumu']
            CPm   = wc['CP_bsmumu']          - wc['CPp_bsmumu']
            CSm   = wc['CS_bsmumu']          - wc['CSp_bsmumu']

            P = C10m/C10SM + mB**2/(2.*mmu) * (mb / (mb + ms)) * CPm/C10SM
            S = math.sqrt(1. - 4*mmu**2/mB**2) * mB**2/(2.*mmu) * (mb / (mb + ms)) * CSm/C10SM
            return P, S

        def BR_inst_Amsterdam_Bs_mumu(par, wc):
            # eq.(1.2) from 1407.2771
            scale = flavio.config['renormalization scale']['bll']
            GF       = par['GF']
            alphaem  = running.get_alpha(par, scale)['alpha_e']
            mW       = par['m_W']
            mB       = par['m_Bs']
            mmu      = par['m_mu']
            tauB     = par['tau_Bs']
            fB       = par['f_Bs']
            xi_ts_tb = ckm.xi('t','bs')(par)
            s2w      = par['s2w']

            # Wilson coefficients
            C10SM = -4.188642825319258 #SM value for C10
            P,S      = amplitudes_Amsterdam_Bs_mumu(par, wc)

            return (GF**2 * alphaem**2 * mB)/(16. * math.pi**3) * math.sqrt(1.-4.*mmu**2/mB**2) *  C10SM**2 * abs(xi_ts_tb)**2 * tauB * fB**2 * mmu**2 * (abs(P)**2 + abs(S)**2)

        def ADG_Amsterdam(par, wc):
            P, S = amplitudes_Amsterdam_Bs_mumu(par, wc)
            return ((P**2).real - (S**2).real)/(abs(P)**2 + abs(S)**2)

        def corr_factor(par, wc):
            ADG  = ADG_Amsterdam(par, wc)
            y    = par['DeltaGamma/Gamma_Bs']/2.
            corr = (1 - y**2)/(1 + ADG*y)

            return corr

        def BR_Amsterdam_Bs_mumu(par, wc):
            BR_inst = BR_inst_Amsterdam_Bs_mumu(par, wc)
            corr    = corr_factor(par, wc)

            return BR_inst / corr

        # define function that calculates the BR in both implementations
        def BR(c10, c10p, cS, cSp, cP, cPp):
            list_wc = {'C10_bsmumu' :  c10,
                 'C10p_bsmumu': c10p,
                 'CS_bsmumu'  : cS,
                 'CSp_bsmumu' : cSp,
                 'CP_bsmumu'  : cP,
                 'CPp_bsmumu' : cPp,
                 'C9_bsmumu'  : 0.,
                 'C9p_bsmumu' : 0.}
            wc = flavio.WilsonCoefficients()
            wc.set_initial(list_wc, scale=160 )

            BR_flavio    = flavio.np_prediction('BR(Bs->mumu)', wc)
            BR_Amsterdam = BR_Amsterdam_Bs_mumu(par, list_wc)

            return {'flavio': BR_flavio, 'Amsterdam': BR_Amsterdam}

        # test SM value
        br_SM = BR(0,0,0,0,0,0)
        self.assertAlmostEqual(br_SM['flavio'] / br_SM['Amsterdam'], 1., delta=0.02)

        # test some values for WC's
        br = BR(-.4,0,0,0,0,0)
        self.assertAlmostEqual(br['flavio'] / br['Amsterdam'], 1., delta=0.02)

        br = BR(-.5*1j,0,0,0,0,0)
        self.assertAlmostEqual(br['flavio'] / br['Amsterdam'], 1., delta=0.02)

        br = BR(0,0,0.01,0,0,0)
        self.assertAlmostEqual(br['flavio'] / br['Amsterdam'], 1., delta=0.02)

        br = BR(0,0,0,-0.01*1j,0,0)
        self.assertAlmostEqual(br['flavio'] / br['Amsterdam'], 1., delta=2)

        br = BR(0,0,0,0,0.01,0)
        self.assertAlmostEqual(br['flavio'] / br['Amsterdam'], 1., delta=0.05)

        br = BR(0,0,0,0,0,-0.02*1j)
        self.assertAlmostEqual(br['flavio'] / br['Amsterdam'], 1., delta=0.04)

        br = BR(-.5*1j,0,0.01,0,0,0)
        self.assertAlmostEqual(br['flavio'] / br['Amsterdam'], 1., delta=0.02)

        br = BR(-.5*1j,0,0,0,-0.03,0)
        self.assertAlmostEqual(br['flavio'] / br['Amsterdam'], 1., delta=0.04)

A class whose instances are single test cases.

By default, the test code itself should be placed in a method named 'runTest'.

If the fixture may be used for many test cases, create as many test methods as are needed. When instantiating such a TestCase subclass, specify in the constructor arguments the name of the test method that the instance is to execute.

Test authors should subclass TestCase for their own tests. Construction and deconstruction of the test's environment ('fixture') can be implemented by overriding the 'setUp' and 'tearDown' methods respectively.

If it is necessary to override the init method, the base class init method must always be called. It is important that subclasses should not change the signature of their init method, since instances of the classes are instantiated automatically by parts of the framework in order to be run.

When subclassing TestCase, you can set these attributes: * failureException: determines which exception will be raised when the instance's assertion methods fail; test methods raising this exception will be deemed to have 'failed' rather than 'errored'. * longMessage: determines whether long messages (including repr of objects used in assert methods) will be printed on failure in addition to any explicit message passed. * maxDiff: sets the maximum length of a diff in failure messages by assert methods using difflib. It is looked up as an instance attribute so can be configured by individual tests if required.

Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.

Ancestors

• unittest.case.TestCase

Methods

def test_BR_Bs_to_mumu(self)

Expand source code

def test_BR_Bs_to_mumu(self):
    # cross check formula with 2nd implementation

    # use formula (1.2) and (1.3) of 1407.2771
    def amplitudes_Amsterdam_Bs_mumu(par, wc):
        # masses
        scale = flavio.config['renormalization scale']['bll']
        mmu = par['m_mu']
        mB  = par['m_Bs']
        mb  = running.get_mb(par, scale, nf_out=5)
        ms  = running.get_ms(par, scale, nf_out=5)
        # Wilson coefficients
        C10SM = -4.188642825319258 #SM value for C10 -4.134#
        C10m  = (C10SM+wc['C10_bsmumu']) - wc['C10p_bsmumu']
        CPm   = wc['CP_bsmumu']          - wc['CPp_bsmumu']
        CSm   = wc['CS_bsmumu']          - wc['CSp_bsmumu']

        P = C10m/C10SM + mB**2/(2.*mmu) * (mb / (mb + ms)) * CPm/C10SM
        S = math.sqrt(1. - 4*mmu**2/mB**2) * mB**2/(2.*mmu) * (mb / (mb + ms)) * CSm/C10SM
        return P, S

    def BR_inst_Amsterdam_Bs_mumu(par, wc):
        # eq.(1.2) from 1407.2771
        scale = flavio.config['renormalization scale']['bll']
        GF       = par['GF']
        alphaem  = running.get_alpha(par, scale)['alpha_e']
        mW       = par['m_W']
        mB       = par['m_Bs']
        mmu      = par['m_mu']
        tauB     = par['tau_Bs']
        fB       = par['f_Bs']
        xi_ts_tb = ckm.xi('t','bs')(par)
        s2w      = par['s2w']

        # Wilson coefficients
        C10SM = -4.188642825319258 #SM value for C10
        P,S      = amplitudes_Amsterdam_Bs_mumu(par, wc)

        return (GF**2 * alphaem**2 * mB)/(16. * math.pi**3) * math.sqrt(1.-4.*mmu**2/mB**2) *  C10SM**2 * abs(xi_ts_tb)**2 * tauB * fB**2 * mmu**2 * (abs(P)**2 + abs(S)**2)

    def ADG_Amsterdam(par, wc):
        P, S = amplitudes_Amsterdam_Bs_mumu(par, wc)
        return ((P**2).real - (S**2).real)/(abs(P)**2 + abs(S)**2)

    def corr_factor(par, wc):
        ADG  = ADG_Amsterdam(par, wc)
        y    = par['DeltaGamma/Gamma_Bs']/2.
        corr = (1 - y**2)/(1 + ADG*y)

        return corr

    def BR_Amsterdam_Bs_mumu(par, wc):
        BR_inst = BR_inst_Amsterdam_Bs_mumu(par, wc)
        corr    = corr_factor(par, wc)

        return BR_inst / corr

    # define function that calculates the BR in both implementations
    def BR(c10, c10p, cS, cSp, cP, cPp):
        list_wc = {'C10_bsmumu' :  c10,
             'C10p_bsmumu': c10p,
             'CS_bsmumu'  : cS,
             'CSp_bsmumu' : cSp,
             'CP_bsmumu'  : cP,
             'CPp_bsmumu' : cPp,
             'C9_bsmumu'  : 0.,
             'C9p_bsmumu' : 0.}
        wc = flavio.WilsonCoefficients()
        wc.set_initial(list_wc, scale=160 )

        BR_flavio    = flavio.np_prediction('BR(Bs->mumu)', wc)
        BR_Amsterdam = BR_Amsterdam_Bs_mumu(par, list_wc)

        return {'flavio': BR_flavio, 'Amsterdam': BR_Amsterdam}

    # test SM value
    br_SM = BR(0,0,0,0,0,0)
    self.assertAlmostEqual(br_SM['flavio'] / br_SM['Amsterdam'], 1., delta=0.02)

    # test some values for WC's
    br = BR(-.4,0,0,0,0,0)
    self.assertAlmostEqual(br['flavio'] / br['Amsterdam'], 1., delta=0.02)

    br = BR(-.5*1j,0,0,0,0,0)
    self.assertAlmostEqual(br['flavio'] / br['Amsterdam'], 1., delta=0.02)

    br = BR(0,0,0.01,0,0,0)
    self.assertAlmostEqual(br['flavio'] / br['Amsterdam'], 1., delta=0.02)

    br = BR(0,0,0,-0.01*1j,0,0)
    self.assertAlmostEqual(br['flavio'] / br['Amsterdam'], 1., delta=2)

    br = BR(0,0,0,0,0.01,0)
    self.assertAlmostEqual(br['flavio'] / br['Amsterdam'], 1., delta=0.05)

    br = BR(0,0,0,0,0,-0.02*1j)
    self.assertAlmostEqual(br['flavio'] / br['Amsterdam'], 1., delta=0.04)

    br = BR(-.5*1j,0,0.01,0,0,0)
    self.assertAlmostEqual(br['flavio'] / br['Amsterdam'], 1., delta=0.02)

    br = BR(-.5*1j,0,0,0,-0.03,0)
    self.assertAlmostEqual(br['flavio'] / br['Amsterdam'], 1., delta=0.04)

def test_EffectiveLifetimes(self)

Expand source code

def test_EffectiveLifetimes(self):
    # In this test we trivially check that the prefactors in (22) and (28) of arXiv:1204.1737 are the same

    ys     = .5*par['DeltaGamma/Gamma_Bs']
    tau_Bs = par['tau_Bs']

    wc_dict = {'e': wc_e, 'mu': wc, 'tau': wc_tau}

    for l in ['e', 'mu', 'tau']:
        ADG    = ADeltaGamma(par, wc_dict[l], 'Bs', l)
        tau    = tau_ll(wc_dict[l], par, 'Bs', l)

        prefactor1 = br_lifetime_corr(ys, ADG)        # eq. (22) of arXiv:1204.1737
        prefactor2 = 2.  - (1.-ys**2) * tau / tau_Bs  # eq. (28) of arXiv:1204.1737

        self.assertAlmostEqual(prefactor1, prefactor2, places=8)

def test_bsll(self)

Expand source code

def test_bsll(self):
    # just some trivial tests to see if calling the functions raises an error
    self.assertGreater(br_lifetime_corr(0.08, -1), 0)
    self.assertEqual(len(amplitudes(par, wc, 'Bs', 'mu', 'mu')), 2)
    # ADeltaGamma should be +1.0 in the SM
    self.assertEqual(ADeltaGamma(par, wc, 'Bs', 'mu'), 1.0)
    self.assertEqual(flavio.sm_prediction('ADeltaGamma(Bs->mumu)'), 1.0)
    # BR should be around 3.5e-9
    self.assertAlmostEqual(br_inst(par, wc, 'Bs', 'mu', 'mu')*1e9, 3.5, places=0)
    # correction factor should enhance the BR by roughly 7%
    self.assertAlmostEqual(br_timeint(par, wc, 'Bs', 'mu', 'mu')/br_inst(par, wc, 'Bs', 'mu', 'mu'), 1.07, places=2)
    # ratio of Bs->mumu and Bs->ee BRs should be roughly given by ratio of squared masses
    self.assertAlmostEqual(
        br_timeint(par, wc_e, 'Bs', 'e', 'e')/br_timeint(par, wc, 'Bs', 'mu', 'mu')/par['m_e']**2*par['m_mu']**2,
        1., places=2)
    # comparison to 1311.0903
    self.assertAlmostEqual(abs(ckm.xi('t','bs')(par))/par['Vcb'], 0.980, places=3)
    self.assertAlmostEqual(br_timeint(par, wc, 'Bs', 'mu', 'mu')/3.65e-9, 1, places=1)
    self.assertAlmostEqual(br_timeint(par, wc_e, 'Bs', 'e', 'e')/8.54e-14, 1, places=1)
    self.assertAlmostEqual(br_timeint(par, wc_tau, 'Bs', 'tau', 'tau')/7.73e-7, 1, places=1)

def test_bsll_classes(self)

Expand source code

def test_bsll_classes(self):
    par_default = default_parameters.get_central_all()
    self.assertAlmostEqual(br_timeint(par_default, wc_tau, 'Bs', 'tau', 'tau')/Observable['BR(Bs->tautau)'].prediction_central(default_parameters, wc_obj), 1, places=4)
    self.assertAlmostEqual(br_timeint(par_default, wc_e, 'Bs', 'e', 'e')/Observable['BR(Bs->ee)'].prediction_central(default_parameters, wc_obj), 1, places=4)
    self.assertAlmostEqual(br_timeint(par_default, wc, 'Bs', 'mu', 'mu')/Observable['BR(Bs->mumu)'].prediction_central(default_parameters, wc_obj), 1, places=4)

def test_bsll_lfv(self)

Expand source code

def test_bsll_lfv(self):
    # test for errors
    self.assertEqual(flavio.sm_prediction('BR(B0->emu)'), 0)
    self.assertEqual(flavio.sm_prediction('BR(Bs->taumu)'), 0)
    self.assertEqual(flavio.sm_prediction('BR(B0->emu,mue)'), 0)
    self.assertEqual(flavio.sm_prediction('BR(Bs->mutau,taumu)'), 0)
    wc = flavio.WilsonCoefficients()
    wc.set_initial({'C10_bdemu': 1, 'C10_bdmue': 2}, scale=4.8)
    self.assertAlmostEqual(flavio.np_prediction('BR(B0->mue)', wc)
                    /flavio.np_prediction('BR(B0->emu)', wc), 4)
    self.assertAlmostEqual(flavio.np_prediction('BR(B0->emu,mue)', wc)
                    /flavio.np_prediction('BR(B0->emu)', wc), 5)