def test_taulnunu_exp(self):
# compare to the experimental values
# cf. eq. (95) of 1705.00929
self.assertAlmostEqual(flavio.sm_prediction('BR(tau->mununu)') / 0.1739,
1 / 1.0060, delta=0.0003)
self.assertAlmostEqual(flavio.sm_prediction('BR(tau->enunu)') / 0.1782,
1 / 1.0022, delta=0.0003)
def test_bksnunu(self):
# just check that the SM prediction of BR(B->K*nunu) is OK
self.assertAlmostEqual(
flavio.sm_prediction('BR(B0->K*nunu)')/9.48e-6,
1, delta=0.2)
# just check the other stuff doesn't raise errors
flavio.sm_prediction('dBR/dq2(B+->K*nunu)', 11)
flavio.sm_prediction('<dBR/dq2>(B+->K*nunu)', 11, 13)
def test_bxgamma(self):
# compare SM predictions to arXiv:1503.01789
self.assertAlmostEqual(1e4*flavio.sm_prediction('BR(B->Xsgamma)'),
3.36,
delta=0.2)
self.assertAlmostEqual(1e5*flavio.sm_prediction('BR(B->Xdgamma)'),
1.73,
delta=0.2)
def test_bxll_lratio(self):
# compare to arXiv:1503.04849
self.assertAlmostEqual(flavio.sm_prediction('<Rmue>(B->Xsll)', 1, 3.5),
0.96, delta=0.01)
self.assertAlmostEqual(flavio.sm_prediction('<Rmue>(B->Xsll)', 14.4, 25),
1.15, delta=0.01)
# for tau, just check this doesn't raise an error
flavio.sm_prediction('<Rtaumu>(B->Xsll)', 14.4, 25)
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_taulnunu_np3(self):
CLSM = -4 * par['GF'] / sqrt(2)
w = Wilson({'CVLL_numunueemu': CLSM},
80, 'WET', 'flavio')
BRSM = flavio.sm_prediction('BR(tau->enunu)')
BRNP = flavio.np_prediction('BR(tau->enunu)', w)
self.assertEqual(BRNP / BRSM, 0.25)
w = Wilson({'CVLL_numunueemu': -0.5 * CLSM},
80, 'WET', 'flavio')
BRSM = flavio.sm_prediction('BR(tau->enunu)')
BRNP = flavio.np_prediction('BR(tau->enunu)', w)
self.assertEqual(BRNP / BRSM, 4)
def test_bpll_lfv(self):
# test for errors
self.assertEqual(flavio.sm_prediction('BR(B0->Kemu)'), 0)
self.assertEqual(flavio.sm_prediction('BR(B+->Ktaumu)'), 0)
self.assertEqual(flavio.sm_prediction('BR(B+->pitaumu)'), 0)
self.assertEqual(flavio.sm_prediction('BR(B0->pitaumu)'), 0)
obs_1 = flavio.classes.Observable["BR(B0->Kemu)"]
obs_2 = flavio.classes.Observable["BR(B0->Kmue)"]
self.assertEqual(obs_1.prediction_central(flavio.default_parameters, wc_sm), 0)
# BR(B->Kemu) should be 4 times larger as Wilson coeff is 2x the mue one
self.assertAlmostEqual(
obs_1.prediction_central(flavio.default_parameters, wc_lfv)
/obs_2.prediction_central(flavio.default_parameters, wc_lfv),
4., places=10)
def testkpinunu(self):
self.assertAlmostEqual(
flavio.sm_prediction('BR(K+->pinunu)')/1e-10,
1, delta=0.2)
self.assertAlmostEqual(
flavio.sm_prediction('BR(KL->pinunu)')/3e-11,
1, delta=0.2)
obs = flavio.classes.Observable['BR(K+->pinunu)']
wc_sm = flavio.WilsonCoefficients()
# confirm that deltaPcu leads to an enhancement of the K+->pinunu BR
# by 6% as stated in the abstract of hep-ph/0503107
self.assertAlmostEqual(
obs.prediction_central(constraints, wc_sm)/obs.prediction_central(constraints_no_deltaPcu, wc_sm),
1.06,
delta=0.01)
def q2_plot_th_diff(obs_name, q2min, q2max, wc=None, q2steps=100, **kwargs):
r"""Plot the central theory prediction of a $q^2$-dependent observable
as a function of $q^2$.
Parameters:
- `q2min`, `q2max`: minimum and maximum $q^2$ values in GeV^2
- `wc` (optional): `WilsonCoefficient` instance to define beyond-the-SM
Wilson coefficients
- `q2steps` (optional): number of $q^2$ steps. Defaults to 100. Less is
faster but less precise.
Additional keyword arguments are passed to the matplotlib plot function,
e.g. 'c' for colour.
"""
obs = flavio.classes.Observable.get_instance(obs_name)
if obs.arguments != ['q2']:
raise ValueError(r"Only observables that depend on $q^2$ (and nothing else) are allowed")
q2_arr = np.arange(q2min, q2max, (q2max-q2min)/(q2steps-1))
if wc is None:
wc = flavio.WilsonCoefficients() # SM Wilson coefficients
obs_arr = [flavio.sm_prediction(obs_name, q2) for q2 in q2_arr]
else:
obs_arr = [flavio.np_prediction(obs_name, wc, q2) for q2 in q2_arr]
ax = plt.gca()
if 'c' not in kwargs and 'color' not in kwargs:
kwargs['c'] = 'k'
ax.plot(q2_arr, obs_arr, **kwargs)
def test_bpll_lfv(self):
# rough numerical test for branching ratio at high q^2 to old code
self.assertAlmostEqual(bpll_dbrdq2(15., wc_obj, par, 'B+', 'K+', 'mu', 'mu')/2.1824401629030333e-8, 1, delta=0.1)
# test for errors
self.assertEqual(flavio.sm_prediction('BR(B0->Kemu)'), 0)
self.assertEqual(flavio.sm_prediction('BR(B+->Ktaumu)'), 0)
self.assertEqual(flavio.sm_prediction('BR(B+->pitaumu)'), 0)
self.assertEqual(flavio.sm_prediction('BR(B0->pitaumu)'), 0)
obs_1 = flavio.classes.Observable.get_instance("BR(B0->Kemu)")
obs_2 = flavio.classes.Observable.get_instance("BR(B0->Kmue)")
self.assertEqual(obs_1.prediction_central(flavio.default_parameters, wc_sm), 0)
# BR(B->Kemu) should be 4 times larger as Wilson coeff is 2x the mue one
self.assertAlmostEqual(
obs_1.prediction_central(flavio.default_parameters, wc_lfv)
/obs_2.prediction_central(flavio.default_parameters, wc_lfv),
4., places=10)
def q2_plot_th_bin(obs_name, bin_list, wc=None, divide_binwidth=False, N=50, **kwargs):
r"""Plot the binned theory prediction with uncertainties of a
$q^2$-dependent observable as a function of $q^2$ (in the form of coloured
boxes)."""
obs = flavio.classes.Observable.get_instance(obs_name)
if obs.arguments != ['q2min', 'q2max']:
raise ValueError(r"Only observables that depend on q2min and q2max (and nothing else) are allowed")
if wc is None:
wc = flavio.WilsonCoefficients() # SM Wilson coefficients
obs_dict = {bin_: flavio.sm_prediction(obs_name, *bin_) for bin_ in bin_list}
obs_err_dict = {bin_: flavio.sm_uncertainty(obs_name, *bin_, N=N) for bin_ in bin_list}
else:
wc = flavio.WilsonCoefficients() # SM Wilson coefficients
obs_dict = {bin_:flavio.np_prediction(obs_name, wc, *bin_) for bin_ in bin_list}
ax = plt.gca()
for bin_, central_ in obs_dict.items():
q2min, q2max = bin_
err = obs_err_dict[bin_]
if divide_binwidth:
err = err/(q2max-q2min)
central = central_/(q2max-q2min)
else:
central = central_
if 'fc' not in kwargs and 'facecolor' not in kwargs:
kwargs['fc'] = flavio.plots.colors.pastel[3]
if 'linewidth' not in kwargs and 'lw' not in kwargs:
kwargs['lw'] = 0
ax.add_patch(patches.Rectangle((q2min, central-err), q2max-q2min, 2*err,**kwargs))
def q2_plot_th_diff(obs_name, q2min, q2max, wc=None, q2steps=100, **kwargs):
r"""Plot the central theory prediction of a $q^2$-dependent observable
as a function of $q^2$.
Parameters:
- `q2min`, `q2max`: minimum and maximum $q^2$ values in GeV^2
- `wc` (optional): `WilsonCoefficient` instance to define beyond-the-SM
Wilson coefficients
- `q2steps` (optional): number of $q^2$ steps. Defaults to 100. Less is
faster but less precise.
Additional keyword arguments are passed to the matplotlib plot function,
e.g. 'c' for colour.
"""
obs = flavio.classes.Observable[obs_name]
if obs.arguments != ['q2']:
raise ValueError(r"Only observables that depend on $q^2$ (and nothing else) are allowed")
q2_arr = np.arange(q2min, q2max, (q2max-q2min)/(q2steps-1))
if wc is None:
wc = flavio.physics.eft._wc_sm # SM Wilson coefficients
obs_arr = [flavio.sm_prediction(obs_name, q2) for q2 in q2_arr]
else:
obs_arr = [flavio.np_prediction(obs_name, wc, q2) for q2 in q2_arr]
ax = plt.gca()
if 'c' not in kwargs and 'color' not in kwargs:
kwargs['c'] = 'k'
ax.plot(q2_arr, obs_arr, **kwargs)
def test_bpll_lfv(self):
# test for errors
self.assertEqual(flavio.sm_prediction('BR(B0->Kemu)'), 0)
self.assertEqual(flavio.sm_prediction('BR(B+->Ktaumu)'), 0)
self.assertEqual(flavio.sm_prediction('BR(B+->pitaumu)'), 0)
self.assertEqual(flavio.sm_prediction('BR(B0->pitaumu)'), 0)
obs_1 = flavio.classes.Observable["BR(B0->Kemu)"]
obs_2 = flavio.classes.Observable["BR(B0->Kmue)"]
self.assertEqual(
obs_1.prediction_central(flavio.default_parameters, wc_sm), 0)
# BR(B->Kemu) should be 4 times larger as Wilson coeff is 2x the mue one
self.assertAlmostEqual(
obs_1.prediction_central(flavio.default_parameters, wc_lfv) /
obs_2.prediction_central(flavio.default_parameters, wc_lfv),
4.,
places=10)
def diff_plot_th(obs_name, x_min, x_max, wc=None, steps=100, **kwargs):
r"""Plot the central theory prediction of an observable dependending on
a continuous parameter, e.g. $q^2$.
Parameters:
- `x_min`, `x_max`: minimum and maximum values of the parameter
- `wc` (optional): `WilsonCoefficient` instance to define beyond-the-SM
Wilson coefficients
- `steps` (optional): number of steps in x. Defaults to 100. Less is
faster but less precise.
Additional keyword arguments are passed to the matplotlib plot function,
e.g. 'c' for colour.
"""
obs = flavio.classes.Observable[obs_name]
if not obs.arguments or len(obs.arguments) != 1:
raise ValueError(r"Only observables that depend on a single parameter are allowed")
x_arr = np.arange(x_min, x_max, (x_max-x_min)/(steps-1))
if wc is None:
wc = flavio.physics.eft._wc_sm # SM Wilson coefficients
obs_arr = [flavio.sm_prediction(obs_name, x) for x in x_arr]
else:
obs_arr = [flavio.np_prediction(obs_name, wc, x) for x in x_arr]
ax = plt.gca()
if 'c' not in kwargs and 'color' not in kwargs:
kwargs['c'] = 'k'
ax.plot(x_arr, obs_arr, **kwargs)
def diff_plot_th(obs_name, x_min, x_max, wc=None, steps=100, **kwargs):
r"""Plot the central theory prediction of an observable dependending on
a continuous parameter, e.g. $q^2$.
Parameters:
- `x_min`, `x_max`: minimum and maximum values of the parameter
- `wc` (optional): `WilsonCoefficient` instance to define beyond-the-SM
Wilson coefficients
- `steps` (optional): number of steps in x. Defaults to 100. Less is
faster but less precise.
Additional keyword arguments are passed to the matplotlib plot function,
e.g. 'c' for colour.
"""
obs = flavio.classes.Observable[obs_name]
if not obs.arguments or len(obs.arguments) != 1:
raise ValueError(
r"Only observables that depend on a single parameter are allowed")
x_arr = np.arange(x_min, x_max, (x_max - x_min) / (steps - 1))
if wc is None:
wc = flavio.physics.eft._wc_sm # SM Wilson coefficients
obs_arr = [flavio.sm_prediction(obs_name, x) for x in x_arr]
else:
obs_arr = [flavio.np_prediction(obs_name, wc, x) for x in x_arr]
ax = plt.gca()
if 'c' not in kwargs and 'color' not in kwargs:
kwargs['c'] = 'k'
ax.plot(x_arr, obs_arr, **kwargs)
def test_bdsenu(self):
Vcb = flavio.default_parameters.get_central('Vcb')
# assert that total BR is in the ballpark of the experimental number
self.assertAlmostEqual(flavio.sm_prediction('BR(B+->D*lnu)') /
5.41e-2 * 0.04**2 / Vcb**2,
1,
delta=0.1)
def test_sm(self):
self.assertAlmostEqual(flavio.sm_prediction('BR(W->enu)'), 10.83e-2, delta=0.02e-2)
self.assertAlmostEqual(flavio.sm_prediction('BR(W->munu)'), 10.83e-2, delta=0.02e-2)
self.assertAlmostEqual(flavio.sm_prediction('BR(W->taunu)'), 10.83e-2, delta=0.02e-2)
self.assertAlmostEqual(flavio.sm_prediction('Rmue(W->lnu)'), 1, delta=1e-16)
self.assertAlmostEqual(flavio.sm_prediction('Rtaue(W->lnu)'), 1, delta=1e-16)
self.assertAlmostEqual(flavio.sm_prediction('Rtaumu(W->lnu)'), 1, delta=1e-16)
self.assertAlmostEqual(flavio.sm_prediction('R(W->cX)'), 0.5, delta=1e-16)
self.assertAlmostEqual(flavio.sm_prediction('GammaW'), 2.091, delta=0.001)
def test_kpilnu(self):
# test for errors
q2 = 0.05
flavio.physics.kdecays.kpilnu.get_ff(q2, par, 'KL')
flavio.physics.kdecays.kpilnu.get_ff(q2, par, 'K+')
flavio.physics.kdecays.kpilnu.get_angularcoeff(q2, wc_obj, par, 'KL',
'pi+', 'e')
flavio.physics.kdecays.kpilnu.get_angularcoeff(q2, wc_obj, par, 'KL',
'pi+', 'mu')
flavio.physics.kdecays.kpilnu.get_angularcoeff(q2, wc_obj, par, 'K+',
'pi0', 'e')
flavio.physics.kdecays.kpilnu.get_angularcoeff(q2, wc_obj, par, 'K+',
'pi0', 'mu')
# unphysical q2
self.assertEqual(
flavio.physics.kdecays.kpilnu.dBRdq2(0, wc_obj, par, 'KL', 'pi+',
'e'), 0)
self.assertEqual(
flavio.physics.kdecays.kpilnu.dBRdq2(0.01, wc_obj, par, 'K+',
'pi0', 'mu'), 0)
self.assertEqual(
flavio.physics.kdecays.kpilnu.dBRdq2(2, wc_obj, par, 'K+', 'pi0',
'e'), 0)
self.assertEqual(
flavio.physics.kdecays.kpilnu.dBRdq2(1.5, wc_obj, par, 'KL', 'pi+',
'mu'), 0)
self.assertEqual(
flavio.physics.kdecays.kpilnu.dBRdq2(0.1, wc_obj, par, 'KL', 'pi+',
'tau'), 0)
# compare central predictions to PDG values
self.assertAlmostEqual(flavio.sm_prediction('BR(KL->pienu)') * 1e2 /
40.55,
1,
delta=0.04)
self.assertAlmostEqual(flavio.sm_prediction('BR(K+->pienu)') * 1e2 /
5.07,
1,
delta=0.04)
self.assertAlmostEqual(flavio.sm_prediction('BR(KL->pimunu)') * 1e2 /
27.04,
1,
delta=0.02)
self.assertAlmostEqual(flavio.sm_prediction('BR(K+->pimunu)') * 1e2 /
3.352,
1,
delta=0.03)
def test_taulnunu_np4(self):
CLSM = -4 * par['GF'] / sqrt(2)
w = Wilson({'CVLL_numunueemu': -0.5 * CLSM,
'CVLL_nutaunueetau': CLSM},
80, 'WET', 'flavio')
BR1 = flavio.sm_prediction('BR(tau->enunu)')
BR2 = flavio.np_prediction('BR(tau->enunu)', w)
self.assertEqual(BR2 / BR1, 9)
def test_afbz_sm(self):
for l in ['e', 'mu', 'tau']:
self.assertAlmostEqual(flavio.sm_prediction('A(Z->{}{})'.format(
l, l)),
0.1472,
delta=0.0002,
msg="Failed for {}".format(l))
self.assertAlmostEqual(flavio.sm_prediction('AFB(Z->{}{})'.format(
l, l)),
0.0163,
delta=0.0002,
msg="Failed for {}".format(l))
self.assertAlmostEqual(flavio.sm_prediction('A(Z->bb)'),
0.935,
delta=0.001)
self.assertAlmostEqual(flavio.sm_prediction('A(Z->cc)'),
0.668,
delta=0.001)
self.assertAlmostEqual(flavio.sm_prediction('A(Z->ss)'),
0.935,
delta=0.001)
self.assertAlmostEqual(flavio.sm_prediction('AFB(Z->bb)'),
0.1032,
delta=0.0002)
self.assertAlmostEqual(flavio.sm_prediction('AFB(Z->cc)'),
0.0738,
delta=0.0002)
def test_bs_timedep(self):
q2 = 3
wc_obj = flavio.WilsonCoefficients()
par = flavio.default_parameters.get_central_all()
B = 'Bs'
V = 'phi'
l = 'mu'
# a set of parameters with y_s=0!
par_y0 = par.copy()
par_y0['DeltaGamma/Gamma_Bs']=0
# compare without lifetime effect: must be equal!
self.assertEqual(
observables.bvll_obs( observables.dGdq2_ave, q2, wc_obj, par_y0, B, V, l),
observables_bs.bsvll_obs( observables_bs.dGdq2_ave_Bs, q2, wc_obj, par_y0, B, V, l))
self.assertEqual(
observables.bvll_obs( observables.FL, q2, wc_obj, par_y0, B, V, l),
observables_bs.bsvll_obs( observables_bs.FL_Bs, q2, wc_obj, par_y0, B, V, l))
for i in [3, 4, 7]: # S3,4,7
self.assertEqual(
observables.bvll_obs( lambda J, J_bar: observables.S_experiment(J, J_bar, i), q2, wc_obj, par_y0, B, V, l),
observables_bs.bsvll_obs( lambda y, J, J_bar, J_h: observables_bs.S_experiment_Bs(y, J, J_bar, J_h, i), q2, wc_obj, par_y0, B, V, l))
# check that the phase phi has the right convention
q_over_p = flavio.physics.mesonmixing.observables.q_over_p(wc_obj, par, B)
phi = cmath.phase(-q_over_p) # the phase of q/p
self.assertAlmostEqual(phi, 0.04, delta=0.01)
# compare WITH lifetime effect: angular observables must be similar
delta = 0.01
self.assertAlmostEqual(
observables.bvll_obs( observables.FL, q2, wc_obj, par, B, V, l)/
observables_bs.bsvll_obs( observables_bs.FL_Bs, q2, wc_obj, par, B, V, l),
1, delta=delta)
for i in [4, 7]: # S4,7
self.assertAlmostEqual(
observables.bvll_obs( lambda J, J_bar: observables.S_experiment(J, J_bar, i), q2, wc_obj, par, B, V, l)/
observables_bs.bsvll_obs( lambda y, J, J_bar, J_h: observables_bs.S_experiment_Bs(y, J, J_bar, J_h, i), q2, wc_obj, par, B, V, l),
1, delta=delta)
for i in [3]: # S3: look at differnece only
self.assertAlmostEqual(
observables.bvll_obs( lambda J, J_bar: observables.S_experiment(J, J_bar, i), q2, wc_obj, par, B, V, l) -
observables_bs.bsvll_obs( lambda y, J, J_bar, J_h: observables_bs.S_experiment_Bs(y, J, J_bar, J_h, i), q2, wc_obj, par, B, V, l),
0, delta=0.01)
# compare WITH lifetime effect: BR suppressed by ~6%!
self.assertAlmostEqual(
observables.bvll_obs( observables.dGdq2_ave, q2, wc_obj, par, B, V, l)/
observables_bs.bsvll_obs( observables_bs.dGdq2_ave_Bs, q2, wc_obj, par, B, V, l),
1.06, delta=0.02)
# and now just check a few observables to see if any errors are raised
flavio.sm_prediction("FL(Bs->phimumu)", q2=1)
flavio.sm_prediction("S3(Bs->phimumu)", q2=1)
flavio.sm_prediction("S4(Bs->phimumu)", q2=1)
flavio.sm_prediction("S7(Bs->phimumu)", q2=1)
def test_bs_timedep(self):
q2 = 3
wc_obj = flavio.WilsonCoefficients()
par = flavio.default_parameters.get_central_all()
B = 'Bs'
V = 'phi'
l = 'mu'
# a set of parameters with y_s=0!
par_y0 = par.copy()
par_y0['DeltaGamma/Gamma_Bs']=0
# compare without lifetime effect: must be equal!
self.assertEqual(
observables.bvll_obs( observables.dGdq2_ave, q2, wc_obj, par_y0, B, V, l),
observables_bs.bsvll_obs( observables_bs.dGdq2_ave_Bs, q2, wc_obj, par_y0, B, V, l))
self.assertEqual(
observables.bvll_obs( observables.FL, q2, wc_obj, par_y0, B, V, l),
observables_bs.bsvll_obs( observables_bs.FL_Bs, q2, wc_obj, par_y0, B, V, l))
for i in [3, 4, 7]: # S3,4,7
self.assertEqual(
observables.bvll_obs( lambda J, J_bar: observables.S_experiment(J, J_bar, i), q2, wc_obj, par_y0, B, V, l),
observables_bs.bsvll_obs( lambda y, J, J_bar, J_h: observables_bs.S_experiment_Bs(y, J, J_bar, J_h, i), q2, wc_obj, par_y0, B, V, l))
# check that the phase phi has the right convention
q_over_p = flavio.physics.mesonmixing.observables.q_over_p(wc_obj, par, B)
phi = cmath.phase(-q_over_p) # the phase of q/p
self.assertAlmostEqual(phi, 0.04, delta=0.01)
# compare WITH lifetime effect: angular observables must be similar
delta = 0.01
self.assertAlmostEqual(
observables.bvll_obs( observables.FL, q2, wc_obj, par, B, V, l)/
observables_bs.bsvll_obs( observables_bs.FL_Bs, q2, wc_obj, par, B, V, l),
1, delta=delta)
for i in [4, 7]: # S4,7
self.assertAlmostEqual(
observables.bvll_obs( lambda J, J_bar: observables.S_experiment(J, J_bar, i), q2, wc_obj, par, B, V, l)/
observables_bs.bsvll_obs( lambda y, J, J_bar, J_h: observables_bs.S_experiment_Bs(y, J, J_bar, J_h, i), q2, wc_obj, par, B, V, l),
1, delta=delta)
for i in [3]: # S3: look at differnece only
self.assertAlmostEqual(
observables.bvll_obs( lambda J, J_bar: observables.S_experiment(J, J_bar, i), q2, wc_obj, par, B, V, l) -
observables_bs.bsvll_obs( lambda y, J, J_bar, J_h: observables_bs.S_experiment_Bs(y, J, J_bar, J_h, i), q2, wc_obj, par, B, V, l),
0, delta=0.01)
# compare WITH lifetime effect: BR suppressed by ~6%!
self.assertAlmostEqual(
observables.bvll_obs( observables.dGdq2_ave, q2, wc_obj, par, B, V, l)/
observables_bs.bsvll_obs( observables_bs.dGdq2_ave_Bs, q2, wc_obj, par, B, V, l),
1.06, delta=0.02)
# and now just check a few observables to see if any errors are raised
flavio.sm_prediction("FL(Bs->phimumu)", q2=1)
flavio.sm_prediction("S3(Bs->phimumu)", q2=1)
flavio.sm_prediction("S4(Bs->phimumu)", q2=1)
flavio.sm_prediction("S7(Bs->phimumu)", q2=1)
def test_taulnunu_np4(self):
CLSM = -4 * par['GF'] / sqrt(2)
w = Wilson({
'CVLL_numunueemu': -0.5 * CLSM,
'CVLL_nutaunueetau': CLSM
}, 80, 'WET', 'flavio')
BR1 = flavio.sm_prediction('BR(tau->enunu)')
BR2 = flavio.np_prediction('BR(tau->enunu)', w)
self.assertEqual(BR2 / BR1, 9)
def test_blnu(self):
Vub = flavio.physics.ckm.get_ckm(par)[0,2]
# compare to literature value
self.assertAlmostEqual(
flavio.Observable["BR(B+->taunu)"].prediction_central(constraints, wc_obj),
1.1e-4 * (abs(Vub)/3.95e-3)**2 * (par['f_B+']/0.2)**2,
delta=2e-6)
# check that B->enu BR is smaller than B->munu
# (ratio given by mass ratio squared)
self.assertAlmostEqual(
(
flavio.Observable["BR(B+->enu)"].prediction_central(constraints, wc_obj)/
flavio.Observable["BR(B+->munu)"].prediction_central(constraints, wc_obj)
)/(par['m_e']**2/par['m_mu']**2),
1,
delta=0.001) # there are corrections of order mmu**2/mB**2
# check that Bc->taunu works without errors
flavio.sm_prediction('BR(Bc->taunu)')
def test_ae_SM(self):
self.assertAlmostEqual(flavio.sm_prediction('a_e') / ae_SM, 1)
pd = flavio.combine_measurements('a_e')
ae_exp = pd.central_value
ae_err_exp = pd.error_left
np.random.seed(17)
ae_err_sm = flavio.sm_uncertainty('a_e')
# check that there is a -2.3 sigma tension, see 1804.07409 p. 13
self.assertAlmostEqual((ae_exp - ae_SM) / sqrt(ae_err_sm**2 + ae_err_exp**2), -2.3, delta=0.5)
def test_lfratios(self):
self.assertAlmostEqual(
flavio.sm_prediction('<Rmue>(B->Dlnu)', 0.5, 5), 1, delta=0.01)
self.assertAlmostEqual(
flavio.sm_prediction('<Rmue>(B->pilnu)', 0.5, 5), 1, delta=0.01)
self.assertAlmostEqual(
flavio.sm_prediction('Rmue(B->Dlnu)'), 1, delta=0.01)
self.assertAlmostEqual(
flavio.sm_prediction('Rmue(B->pilnu)'), 1, delta=0.01)
# for the taus, just make sure no error is raised
flavio.sm_prediction('Rtaumu(B->pilnu)')
flavio.sm_prediction('<Rtaumu>(B->Dlnu)', 15, 16)
def test_np(self):
wc = flavio.WilsonCoefficients()
for l in ['e', 'mu', 'tau']:
wc.set_initial({'CSL_bc' + l + 'nu' + l: 1}, 4.8)
self.assertTrue(par['tau_Bc_SM'] / flavio.np_prediction('tau_Bc', wc) > 1.1)
self.assertAlmostEqual(par['tau_Bc_SM'] / flavio.np_prediction('tau_Bc', wc),
1 + flavio.np_prediction('BR(Bc->' + l + 'nu)', wc)
- flavio.sm_prediction('BR(Bc->' + l + 'nu)'),
delta=0.05,
msg="Failed for {}".format(l))
def test_fl(self):
# compare to 1409.4557 table 2
self.assertAlmostEqual(flavio.sm_prediction('<FL>(B0->K*nunu)', 0, 27),
0.47,
delta=2 * 0.03)
self.assertAlmostEqual(flavio.sm_prediction('<FL>(B0->K*nunu)', 0, 4),
0.79,
delta=2 * 0.03)
self.assertAlmostEqual(flavio.sm_prediction('<FL>(B0->K*nunu)', 16,
19.25),
0.32,
delta=2 * 0.03)
# ... and the differential ones
self.assertAlmostEqual(flavio.sm_prediction('FL(B0->K*nunu)', 2),
0.79,
delta=3 * 0.03)
self.assertAlmostEqual(flavio.sm_prediction('FL(B0->K*nunu)', 17.5),
0.32,
delta=3 * 0.03)
def test_ft(self):
# compare to exp values in table 4 of 1803.08732
wc_obj = flavio.WilsonCoefficients()
Ft = flavio.physics.betadecays.ft.Ft_superallowed(par, wc_obj, '10C')
self.assertAlmostEqual(Ft / s, 3078, delta=2 * 5)
Ft = flavio.physics.betadecays.ft.Ft_superallowed(par, wc_obj, '26mAl')
self.assertAlmostEqual(Ft / s, 3072.9, delta=3 * 1)
Ft = flavio.physics.betadecays.ft.Ft_superallowed(par, wc_obj, '46V')
self.assertAlmostEqual(Ft / s, 3074.1, delta=2 * 2)
Ft = flavio.sm_prediction('Ft(38Ca)')
self.assertAlmostEqual(Ft / s, 3076.4, delta=2 * 7.2)
def test_kpilnu(self):
# test for errors
q2=0.05
flavio.physics.kdecays.kpilnu.get_ff(q2, par, 'KL')
flavio.physics.kdecays.kpilnu.get_ff(q2, par, 'K+')
flavio.physics.kdecays.kpilnu.get_angularcoeff(q2, wc_obj, par, 'KL', 'pi+', 'e')
flavio.physics.kdecays.kpilnu.get_angularcoeff(q2, wc_obj, par, 'KL', 'pi+', 'mu')
flavio.physics.kdecays.kpilnu.get_angularcoeff(q2, wc_obj, par, 'K+', 'pi0', 'e')
flavio.physics.kdecays.kpilnu.get_angularcoeff(q2, wc_obj, par, 'K+', 'pi0', 'mu')
# unphysical q2
self.assertEqual(flavio.physics.kdecays.kpilnu.dBRdq2(0, wc_obj, par, 'KL', 'pi+', 'e'), 0)
self.assertEqual(flavio.physics.kdecays.kpilnu.dBRdq2(0.01, wc_obj, par, 'K+', 'pi0', 'mu'), 0)
self.assertEqual(flavio.physics.kdecays.kpilnu.dBRdq2(2, wc_obj, par, 'K+', 'pi0', 'e'), 0)
self.assertEqual(flavio.physics.kdecays.kpilnu.dBRdq2(1.5, wc_obj, par, 'KL', 'pi+', 'mu'), 0)
self.assertEqual(flavio.physics.kdecays.kpilnu.dBRdq2(0.1, wc_obj, par, 'KL', 'pi+', 'tau'), 0)
# compare central predictions to PDG values
self.assertAlmostEqual(flavio.sm_prediction('BR(KL->pienu)')*1e2/40.55, 1, delta=0.04)
self.assertAlmostEqual(flavio.sm_prediction('BR(K+->pienu)')*1e2/5.07, 1, delta=0.04)
self.assertAlmostEqual(flavio.sm_prediction('BR(KL->pimunu)')*1e2/27.04, 1, delta=0.02)
self.assertAlmostEqual(flavio.sm_prediction('BR(K+->pimunu)')*1e2/3.352, 1, delta=0.03)
def test_ae_SM(self):
self.assertAlmostEqual(flavio.sm_prediction('a_e') / ae_SM, 1)
pd = flavio.combine_measurements('a_e')
ae_exp = pd.central_value
ae_err_exp = pd.error_left
np.random.seed(17)
ae_err_sm = flavio.sm_uncertainty('a_e')
# check that there is a -2.3 sigma tension, see 1804.07409 p. 13
self.assertAlmostEqual(
(ae_exp - ae_SM) / sqrt(ae_err_sm**2 + ae_err_exp**2),
-2.3,
delta=0.5)
def chi2_budget(wc=None):
chi = []
for i in range(0, nobs):
if isinstance(obslist[i]['obs'], tuple):
if wc is None:
th = flavio.sm_prediction(obslist[i]['obs'][0],
q2min=obslist[i]['obs'][1],
q2max=obslist[i]['obs'][2])
else:
th = flavio.np_prediction(obslist[i]['obs'][0],
wc_obj=wc,
q2min=obslist[i]['obs'][1],
q2max=obslist[i]['obs'][2])
else:
if wc is None:
th = flavio.sm_prediction(obslist[i]['obs'])
else:
th = flavio.np_prediction(obslist[i]['obs'], wc_obj=wc)
chi.append((th - obslist[i]['central'])**2 / obslist[i]['error']**2)
return chi
def test_bxclnu(self):
# check that the NLO and NNLO functions reproduce the correct numbers
self.assertAlmostEqual(
flavio.physics.bdecays.bxlnu.pc1(r=(0.986/4.6)**2, mb=4.6),
-1.65019, delta=0.001)
self.assertAlmostEqual(
flavio.physics.bdecays.bxlnu.pc2(r=(0.986/4.6)**2, mb=4.6),
-1.91556 -0.4519 * 9 , delta=0.001)
# check that the total BR roughly agrees with the experimental value
self.assertAlmostEqual(
flavio.sm_prediction('BR(B->Xcenu)'),
0.1065,
delta = 0.0005)
self.assertAlmostEqual(
flavio.sm_prediction('BR(B->Xcmunu)'),
0.1065,
delta = 0.0005)
self.assertAlmostEqual(
flavio.sm_prediction('BR(B->Xclnu)'),
0.1065,
delta = 0.0005)
def test_lfv(self):
obs_1 = flavio.classes.Observable.get_instance("BR(B0->K*emu)")
obs_2 = flavio.classes.Observable.get_instance("BR(B0->K*mue)")
self.assertEqual(obs_1.prediction_central(flavio.default_parameters, wc_sm), 0)
# BR(B->K*emu) should be 4 times larger as Wilson coeff is 2x the mue one
self.assertAlmostEqual(
obs_1.prediction_central(flavio.default_parameters, wc_np)
/obs_2.prediction_central(flavio.default_parameters, wc_np),
4., places=10)
# test for errors
flavio.sm_prediction("BR(B+->K*mue)")
flavio.sm_prediction("BR(B0->rhotaue)")
flavio.sm_prediction("BR(B+->rhotaumu)")
flavio.sm_prediction("BR(Bs->phimutau)")
def test_bxll(self):
# check whether QED corrections have the right behaviour
wc_obj = flavio.WilsonCoefficients()
par = flavio.default_parameters.get_central_all()
br_1_noqedpc = _bxll_dbrdq2(1, wc_obj, par, 's', 'mu', include_qed=False, include_pc=False)
br_1_qed = _bxll_dbrdq2(1, wc_obj, par, 's', 'mu', include_qed=True, include_pc=False)
br_6_noqedpc = _bxll_dbrdq2(6, wc_obj, par, 's', 'mu', include_qed=False, include_pc=False)
br_6_qed = _bxll_dbrdq2(6, wc_obj, par, 's', 'mu', include_qed=True, include_pc=False)
br_15_noqedpc = _bxll_dbrdq2(15, wc_obj, par, 's', 'mu', include_qed=False, include_pc=False)
br_15_qed = _bxll_dbrdq2(15, wc_obj, par, 's', 'mu', include_qed=True, include_pc=False)
br_21_noqedpc = _bxll_dbrdq2(21, wc_obj, par, 's', 'mu', include_qed=False, include_pc=False)
br_21_qed = _bxll_dbrdq2(21, wc_obj, par, 's', 'mu', include_qed=True, include_pc=False)
self.assertAlmostEqual((br_1_qed+br_6_qed)/(br_1_noqedpc+br_6_noqedpc),
1.02, delta=0.01) # should lead to a 2% enhancement
self.assertAlmostEqual((br_15_qed+br_21_qed)/(br_15_noqedpc+br_21_noqedpc),
0.92, delta=0.03) # should lead to a 8% suppression
# compare SM predictions to arXiv:1503.04849
# to convert to the parameters used there
xi_t = flavio.physics.ckm.xi('t','bs')(par)
Vcb = flavio.physics.ckm.get_ckm(par)[1,2]
r = abs(xi_t)**2/Vcb**2/0.9621*0.574/par['C_BXlnu']*par['BR(B->Xcenu)_exp']/0.1051
self.assertAlmostEqual(1e6*flavio.sm_prediction('<BR>(B->Xsmumu)', 1, 3.5)/r,
0.888, delta=0.02)
self.assertAlmostEqual(1e6*flavio.sm_prediction('<BR>(B->Xsmumu)', 3.5, 6)/r,
0.731, delta=0.01)
self.assertAlmostEqual(1e7*flavio.sm_prediction('<BR>(B->Xsmumu)', 14.4, 25)/r,
2.53, delta=0.7) # larger difference due to Krüger-Sehgal
self.assertAlmostEqual(1e6*flavio.sm_prediction('<BR>(B->Xsee)', 1, 3.5)/r,
0.926, delta=0.04)
self.assertAlmostEqual(1e6*flavio.sm_prediction('<BR>(B->Xsee)', 3.5, 6)/r,
0.744, delta=0.015)
self.assertAlmostEqual(1e7*flavio.sm_prediction('<BR>(B->Xsee)', 14.4, 25)/r,
2.20, delta=0.6) # larger difference due to Krüger-Sehgal
def test_bxll(self):
# check whether QED corrections have the right behaviour
wc_obj = flavio.WilsonCoefficients()
par = flavio.default_parameters.get_central_all()
br_1_noqedpc = _bxll_dbrdq2(1, wc_obj, par, 's', 'mu', include_qed=False, include_pc=False)
br_1_qed = _bxll_dbrdq2(1, wc_obj, par, 's', 'mu', include_qed=True, include_pc=False)
br_6_noqedpc = _bxll_dbrdq2(6, wc_obj, par, 's', 'mu', include_qed=False, include_pc=False)
br_6_qed = _bxll_dbrdq2(6, wc_obj, par, 's', 'mu', include_qed=True, include_pc=False)
br_15_noqedpc = _bxll_dbrdq2(15, wc_obj, par, 's', 'mu', include_qed=False, include_pc=False)
br_15_qed = _bxll_dbrdq2(15, wc_obj, par, 's', 'mu', include_qed=True, include_pc=False)
br_21_noqedpc = _bxll_dbrdq2(21, wc_obj, par, 's', 'mu', include_qed=False, include_pc=False)
br_21_qed = _bxll_dbrdq2(21, wc_obj, par, 's', 'mu', include_qed=True, include_pc=False)
self.assertAlmostEqual((br_1_qed+br_6_qed)/(br_1_noqedpc+br_6_noqedpc),
1.02, delta=0.01) # should lead to a 2% enhancement
self.assertAlmostEqual((br_15_qed+br_21_qed)/(br_15_noqedpc+br_21_noqedpc),
0.92, delta=0.03) # should lead to a 8% suppression
# compare SM predictions to arXiv:1503.04849
# to convert to the parameters used there
xi_t = flavio.physics.ckm.xi('t','bs')(par)
Vcb = flavio.physics.ckm.get_ckm(par)[1,2]
r = abs(xi_t)**2/Vcb**2/0.9621*0.574/par['C_BXlnu']*par['BR(B->Xcenu)_exp']/0.1051
self.assertAlmostEqual(1e6*flavio.sm_prediction('<BR>(B->Xsmumu)', 1, 3.5)/r,
0.888, delta=0.02)
self.assertAlmostEqual(1e6*flavio.sm_prediction('<BR>(B->Xsmumu)', 3.5, 6)/r,
0.731, delta=0.01)
self.assertAlmostEqual(1e7*flavio.sm_prediction('<BR>(B->Xsmumu)', 14.4, 25)/r,
2.53, delta=0.6) # larger difference due to Krüger-Sehgal
self.assertAlmostEqual(1e6*flavio.sm_prediction('<BR>(B->Xsee)', 1, 3.5)/r,
0.926, delta=0.04)
self.assertAlmostEqual(1e6*flavio.sm_prediction('<BR>(B->Xsee)', 3.5, 6)/r,
0.744, delta=0.015)
self.assertAlmostEqual(1e7*flavio.sm_prediction('<BR>(B->Xsee)', 14.4, 25)/r,
2.20, delta=0.6) # larger difference due to Krüger-Sehgal
def test_bkll(self):
# rough numerical test for branching ratio at high q^2 to old code
self.assertAlmostEqual(bpll_dbrdq2(15., wc_obj, par, 'B+', 'K+', 'mu', 'mu')/2.1824401629030333e-8, 1, delta=0.1)
# test for errors
flavio.sm_prediction('dBR/dq2(B0->Kmumu)', q2=3)
flavio.sm_prediction('AFB(B0->Kmumu)', q2=15)
flavio.sm_prediction('FH(B+->Kmumu)', q2=21)
def test_lfv(self):
obs_1 = flavio.classes.Observable["BR(B0->K*emu)"]
obs_2 = flavio.classes.Observable["BR(B0->K*mue)"]
self.assertEqual(obs_1.prediction_central(flavio.default_parameters, wc_sm), 0)
# BR(B->K*emu) should be 4 times larger as Wilson coeff is 2x the mue one
self.assertAlmostEqual(
obs_1.prediction_central(flavio.default_parameters, wc_np)
/obs_2.prediction_central(flavio.default_parameters, wc_np),
4., places=10)
# test for errors
flavio.sm_prediction("BR(B+->K*mue)")
flavio.sm_prediction("BR(B0->rhotaue)")
flavio.sm_prediction("BR(B+->rhotaumu)")
flavio.sm_prediction("BR(Bs->phimutau)")
def test_tau3l_sm(self):
self.assertEqual(flavio.sm_prediction('BR(tau->muee)'), 0)
self.assertEqual(flavio.sm_prediction('BR(tau->mumumu)'), 0)
self.assertEqual(flavio.sm_prediction('BR(tau->emumu)'), 0)
self.assertEqual(flavio.sm_prediction('BR(tau->eee)'), 0)
self.assertEqual(flavio.sm_prediction('BR(tau->emue)'), 0)
self.assertEqual(flavio.sm_prediction('BR(tau->muemu)'), 0)
def test_bpll_lfv(self):
# rough numerical test for branching ratio at high q^2 to old code
self.assertAlmostEqual(
bpll_dbrdq2(15., wc_obj, par, 'B+', 'K+', 'mu', 'mu') /
2.1824401629030333e-8,
1,
delta=0.1)
# test for errors
self.assertEqual(flavio.sm_prediction('BR(B0->Kemu)'), 0)
self.assertEqual(flavio.sm_prediction('BR(B+->Ktaumu)'), 0)
self.assertEqual(flavio.sm_prediction('BR(B+->pitaumu)'), 0)
self.assertEqual(flavio.sm_prediction('BR(B0->pitaumu)'), 0)
obs_1 = flavio.classes.Observable["BR(B0->Kemu)"]
obs_2 = flavio.classes.Observable["BR(B0->Kmue)"]
self.assertEqual(
obs_1.prediction_central(flavio.default_parameters, wc_sm), 0)
# BR(B->Kemu) should be 4 times larger as Wilson coeff is 2x the mue one
self.assertAlmostEqual(
obs_1.prediction_central(flavio.default_parameters, wc_lfv) /
obs_2.prediction_central(flavio.default_parameters, wc_lfv),
4.,
places=10)
def q2_plot_th_bin(obs_name, bin_list, wc=None, divide_binwidth=False, N=50, **kwargs):
r"""Plot the binned theory prediction with uncertainties of a
$q^2$-dependent observable as a function of $q^2$ (in the form of coloured
boxes).
Parameters:
- `bin_list`: a list of tuples containing bin boundaries
- `wc` (optional): `WilsonCoefficient` instance to define beyond-the-SM
Wilson coefficients
- `divide_binwidth` (optional): this should be set to True when comparing
integrated branching ratios from experiments with different bin widths
or to theory predictions for a differential branching ratio. It will
divide all values and uncertainties by the bin width (i.e. dimensionless
integrated BRs will be converted to integrated differential BRs with
dimensions of GeV$^{-2}$). Defaults to False.
- `N` (optional): number of random draws to determine the uncertainty.
Defaults to 50. Larger is slower but more precise. The relative
error of the theory uncertainty scales as $1/\sqrt{2N}$.
Additional keyword arguments are passed to the matplotlib add_patch function,
e.g. 'fc' for face colour.
"""
obs = flavio.classes.Observable[obs_name]
if obs.arguments != ['q2min', 'q2max']:
raise ValueError(r"Only observables that depend on q2min and q2max (and nothing else) are allowed")
if wc is None:
wc = flavio.physics.eft._wc_sm # SM Wilson coefficients
obs_dict = {bin_: flavio.sm_prediction(obs_name, *bin_) for bin_ in bin_list}
obs_err_dict = {bin_: flavio.sm_uncertainty(obs_name, *bin_, N=N) for bin_ in bin_list}
else:
obs_dict = {bin_:flavio.np_prediction(obs_name, wc, *bin_) for bin_ in bin_list}
obs_err_dict = {bin_: flavio.np_uncertainty(obs_name, wc, *bin_, N=N) for bin_ in bin_list}
ax = plt.gca()
for _i, (bin_, central_) in enumerate(obs_dict.items()):
q2min, q2max = bin_
err = obs_err_dict[bin_]
if divide_binwidth:
err = err/(q2max-q2min)
central = central_/(q2max-q2min)
else:
central = central_
if 'fc' not in kwargs and 'facecolor' not in kwargs:
kwargs['fc'] = flavio.plots.colors.pastel[3]
if 'linewidth' not in kwargs and 'lw' not in kwargs:
kwargs['lw'] = 0
if _i > 0:
# the label should only be set for one (i.e. the first)
# of the boxes, otherwise it will appear multiply in the legend
kwargs.pop('label', None)
ax.add_patch(patches.Rectangle((q2min, central-err), q2max-q2min, 2*err,**kwargs))
def bin_plot_th(obs_name, bin_list, wc=None, divide_binwidth=False, N=50, threads=1, **kwargs):
r"""Plot the binned theory prediction with uncertainties of an observable
dependending on a continuous parameter, e.g. $q^2$ (in the form of coloured
boxes).
Parameters:
- `bin_list`: a list of tuples containing bin boundaries
- `wc` (optional): `WilsonCoefficient` instance to define beyond-the-SM
Wilson coefficients
- `divide_binwidth` (optional): this should be set to True when comparing
integrated branching ratios from experiments with different bin widths
or to theory predictions for a differential branching ratio. It will
divide all values and uncertainties by the bin width (i.e. dimensionless
integrated BRs will be converted to $q^2$-integrated differential BRs with
dimensions of GeV$^{-2}$). Defaults to False.
- `N` (optional): number of random draws to determine the uncertainty.
Defaults to 50. Larger is slower but more precise. The relative
error of the theory uncertainty scales as $1/\sqrt{2N}$.
Additional keyword arguments are passed to the matplotlib add_patch function,
e.g. 'fc' for face colour.
"""
obs = flavio.classes.Observable[obs_name]
if not obs.arguments or len(obs.arguments) != 2:
raise ValueError(r"Only observables that depend on the two bin boundaries (and nothing else) are allowed")
if wc is None:
wc = flavio.physics.eft._wc_sm # SM Wilson coefficients
obs_dict = {bin_: flavio.sm_prediction(obs_name, *bin_) for bin_ in bin_list}
obs_err_dict = {bin_: flavio.sm_uncertainty(obs_name, *bin_, N=N, threads=threads) for bin_ in bin_list}
else:
obs_dict = {bin_:flavio.np_prediction(obs_name, wc, *bin_) for bin_ in bin_list}
obs_err_dict = {bin_: flavio.np_uncertainty(obs_name, wc, *bin_, N=N, threads=threads) for bin_ in bin_list}
ax = plt.gca()
for _i, (bin_, central_) in enumerate(obs_dict.items()):
xmin, xmax = bin_
err = obs_err_dict[bin_]
if divide_binwidth:
err = err/(xmax-xmin)
central = central_/(xmax-xmin)
else:
central = central_
if 'fc' not in kwargs and 'facecolor' not in kwargs:
kwargs['fc'] = flavio.plots.colors.pastel[3]
if 'linewidth' not in kwargs and 'lw' not in kwargs:
kwargs['lw'] = 0
if _i > 0:
# the label should only be set for one (i.e. the first)
# of the boxes, otherwise it will appear multiply in the legend
kwargs.pop('label', None)
ax.add_patch(patches.Rectangle((xmin, central-err), xmax-xmin, 2*err,**kwargs))
def test_fl(self):
# compare to 1409.4557 table 2
self.assertAlmostEqual(
flavio.sm_prediction('<FL>(B0->K*nunu)', 0, 27),
0.47,
delta=2*0.03)
self.assertAlmostEqual(
flavio.sm_prediction('<FL>(B0->K*nunu)', 0, 4),
0.79,
delta=2*0.03)
self.assertAlmostEqual(
flavio.sm_prediction('<FL>(B0->K*nunu)', 16, 19.25),
0.32,
delta=2*0.03)
# ... and the differential ones
self.assertAlmostEqual(
flavio.sm_prediction('FL(B0->K*nunu)', 2),
0.79,
delta=3*0.03)
self.assertAlmostEqual(
flavio.sm_prediction('FL(B0->K*nunu)', 17.5),
0.32,
delta=3*0.03)
def test_functions(self):
o = Observable('test_obs')
o.arguments = ['x']
def f(wc_obj, par_dict, x):
return x
pr = Prediction('test_obs', f )
wc_obj = None
self.assertEqual(flavio.sm_prediction('test_obs', 7), 7)
self.assertEqual(flavio.np_prediction('test_obs', x=7, wc_obj=wc_obj), 7)
self.assertEqual(flavio.sm_uncertainty('test_obs', 7), 0)
self.assertEqual(flavio.np_uncertainty('test_obs', x=7, wc_obj=wc_obj), 0)
self.assertEqual(flavio.sm_uncertainty('test_obs', 7, threads=2), 0)
self.assertEqual(flavio.np_uncertainty('test_obs', x=7, wc_obj=wc_obj, threads=2), 0)
# delete dummy instance
Observable.del_instance('test_obs')
def test_acp(self):
# check that the SM central values for the individual B->Xsgamma
# and B->Xdgamma (ignoring long distance contributions) roughly
# agree with the values quoted in hep-ph/0312260
wc_sm_s = flavio.physics.bdecays.wilsoncoefficients.wctot_dict(wc_obj, 'bsee', scale=2, par=par_dict, nf_out=5)
wc_sm_d = flavio.physics.bdecays.wilsoncoefficients.wctot_dict(wc_obj, 'bdee', scale=2, par=par_dict, nf_out=5)
p_ave_s = flavio.physics.bdecays.bxgamma.PE0_BR_BXgamma(wc_sm_s, par_dict, 's', 1.6)
p_asy_s = flavio.physics.bdecays.bxgamma.PE0_ACP_BXgamma(wc_sm_s, par_dict, 's', 1.6)
acp_s = p_asy_s/p_ave_s
self.assertAlmostEqual(100*acp_s, 0.44, delta=0.5)
p_ave_d = flavio.physics.bdecays.bxgamma.PE0_BR_BXgamma(wc_sm_d, par_dict, 'd', 1.6)
p_asy_d = flavio.physics.bdecays.bxgamma.PE0_ACP_BXgamma(wc_sm_d, par_dict, 'd', 1.6)
acp_d = p_asy_d/p_ave_d
# check that the s+d CP asymmetry vanishes
self.assertAlmostEqual(flavio.sm_prediction('ACP(B->Xgamma)'), 0, delta=1e-9)
def q2_plot_th_diff(obs_name, q2min, q2max, wc=None, q2steps=100, **kwargs):
r"""Plot the central theory prediction of a $q^2$-dependent observable
as a function of $q^2$."""
obs = flavio.classes.Observable.get_instance(obs_name)
if obs.arguments != ['q2']:
raise ValueError(r"Only observables that depend on $q^2$ (and nothing else) are allowed")
q2_arr = np.arange(q2min, q2max, (q2max-q2min)/(q2steps-1))
if wc is None:
wc = flavio.WilsonCoefficients() # SM Wilson coefficients
obs_arr = [flavio.sm_prediction(obs_name, q2) for q2 in q2_arr]
else:
obs_arr = [flavio.np_prediction(obs_name, wc, q2) for q2 in q2_arr]
ax = plt.gca()
if 'c' not in kwargs and 'color' not in kwargs:
kwargs['c'] = 'k'
ax.plot(q2_arr, obs_arr, **kwargs)
def test_unphysical(self):
# check BR calculation yields zero outside kinematical limits
self.assertEqual(flavio.sm_prediction("dBR/dq2(B0->K*mumu)", q2=0.01), 0)
self.assertEqual(flavio.sm_prediction("dBR/dq2(B0->K*mumu)", q2=25), 0)
# and also *at* kinemetical limits
par = flavio.default_parameters.get_central_all()
q2min = 4*par['m_mu']**2
q2max = (par['m_B0']-par['m_K*0'])**2
self.assertAlmostEqual(flavio.sm_prediction("dBR/dq2(B0->K*mumu)", q2=q2min), 0, delta=1e-10)
self.assertAlmostEqual(flavio.sm_prediction("dBR/dq2(B0->K*mumu)", q2=q2max), 0, delta=1e-10)
# same for angular observables (make sure no division by 0)
self.assertEqual(flavio.sm_prediction("S5(B0->K*mumu)", q2=0.01), 0)
self.assertEqual(flavio.sm_prediction("S5(B0->K*mumu)", q2=25), 0)
def test_unphysical(self):
# check BR calculation yields zero outside kinematical limits
self.assertEqual(flavio.sm_prediction("dBR/dq2(B0->K*mumu)", q2=0.01), 0)
self.assertEqual(flavio.sm_prediction("dBR/dq2(B0->K*mumu)", q2=25), 0)
# and also *at* kinemetical limits
par = flavio.default_parameters.get_central_all()
q2min = 4*par['m_mu']**2
q2max = (par['m_B0']-par['m_K*0'])**2
self.assertAlmostEqual(flavio.sm_prediction("dBR/dq2(B0->K*mumu)", q2=q2min), 0, delta=1e-10)
self.assertAlmostEqual(flavio.sm_prediction("dBR/dq2(B0->K*mumu)", q2=q2max), 0, delta=1e-10)
# same for angular observables (make sure no division by 0)
self.assertEqual(flavio.sm_prediction("S5(B0->K*mumu)", q2=0.01), 0)
self.assertEqual(flavio.sm_prediction("S5(B0->K*mumu)", q2=25), 0)
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(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_diff_ee_ww_NP(self):
coeffs = ['phiWB', 'phiD', 'phil3_11', 'phil3_22', 'll_1221', 'phil1_11', 'phie_11']
for coeff in coeffs:
for E in [182.66, 189.09, 198.38, 205.92]:
_E = Es.flat[np.abs(Es - E).argmin()]
dsigma = []
dsigma_sm = []
for i in range(10):
args = (E,
np.round(i * 0.2 - 1, 1),
np.round((i + 1) * 0.2 - 1, 1))
w = wilson.Wilson({coeff: 0.1 / 246.22**2}, 91.1876, 'SMEFT', 'Warsaw')
dsigma.append(np_prediction('<dR/dtheta>(ee->WW)', w, *args))
dsigma_sm.append(sm_prediction('<dR/dtheta>(ee->WW)', *args))
r_tot = np_prediction('R(ee->WW)', w, _E)
sigma_tot_sm = sum(dsigma_sm)
sigma_tot = sum(dsigma)
self.assertAlmostEqual(sigma_tot / sigma_tot_sm,
r_tot,
delta=0.25,
msg="Failed for E={}, C_{}".format(E, coeff))
def test_corrn(self):
# compare to exp values in table 5 of 1803.08732
self.assertAlmostEqual(flavio.sm_prediction('a_n'),
-0.1034,
delta=2 * 0.0037)
self.assertAlmostEqual(flavio.sm_prediction('atilde_n', me_E=0.695),
-0.1090,
delta=0.01)
self.assertAlmostEqual(flavio.sm_prediction('Atilde_n', me_E=0.569),
-0.11869,
delta=0.01)
self.assertAlmostEqual(flavio.sm_prediction('Btilde_n', me_E=0.591),
0.9805,
delta=3 * 0.003)
self.assertAlmostEqual(flavio.sm_prediction('lambdaAB_n', me_E=0.581),
-1.2686,
delta=0.04)
self.assertEqual(flavio.sm_prediction('D_n'), 0)
self.assertEqual(flavio.sm_prediction('R_n'), 0)
def test_acp(self):
# check that the SM central values for the individual B->Xsgamma
# and B->Xdgamma (ignoring long distance contributions) roughly
# agree with the values quoted in hep-ph/0312260
wc_sm_s = flavio.physics.bdecays.wilsoncoefficients.wctot_dict(
wc_obj, 'bsee', scale=2, par=par_dict, nf_out=5)
wc_sm_d = flavio.physics.bdecays.wilsoncoefficients.wctot_dict(
wc_obj, 'bdee', scale=2, par=par_dict, nf_out=5)
p_ave_s = flavio.physics.bdecays.bxgamma.PE0_BR_BXgamma(
wc_sm_s, par_dict, 's', 1.6)
p_asy_s = flavio.physics.bdecays.bxgamma.PE0_ACP_BXgamma(
wc_sm_s, par_dict, 's', 1.6)
acp_s = p_asy_s / p_ave_s
self.assertAlmostEqual(100 * acp_s, 0.44, delta=0.5)
p_ave_d = flavio.physics.bdecays.bxgamma.PE0_BR_BXgamma(
wc_sm_d, par_dict, 'd', 1.6)
p_asy_d = flavio.physics.bdecays.bxgamma.PE0_ACP_BXgamma(
wc_sm_d, par_dict, 'd', 1.6)
acp_d = p_asy_d / p_ave_d
# check that the s+d CP asymmetry vanishes
self.assertAlmostEqual(flavio.sm_prediction('ACP(B->Xgamma)'),
0,
delta=1e-9)
def test_bxll_afb(self):
# check calling outside of kinematical regions yields 0
self.assertAlmostEqual(flavio.sm_prediction('AFB(B->Xsmumu)', 0), 0)
self.assertAlmostEqual(flavio.sm_prediction('AFB(B->Xsll)', 30), 0)
# just check differential AFB doesn't raise errors
flavio.sm_prediction('AFB(B->Xsee)', 1)
flavio.sm_prediction('AFB(B->Xsmumu)', 6)
flavio.sm_prediction('AFB(B->Xsll)', 14.4)
# check whether QED corrections have the right behaviour
# (table 2 of arXiv:1503.04849)
wc_obj = flavio.WilsonCoefficients()
par = flavio.default_parameters.get_central_all()
afb_num_low1_noqed = bxll_afb_num_int(1, 3.5, wc_obj, par, 's', 'e', include_qed=False)
afb_num_low1_qed = bxll_afb_num_int(1, 3.5, wc_obj, par, 's', 'e', include_qed=True)
afb_num_low2_noqed = bxll_afb_num_int(3.5, 6, wc_obj, par, 's', 'e', include_qed=False)
afb_num_low2_qed = bxll_afb_num_int(3.5, 6, wc_obj, par, 's', 'e', include_qed=True)
afb_den_low1_noqed = bxll_afb_den_int(1, 3.5, wc_obj, par, 's', 'e', include_qed=False)
afb_den_low1_qed = bxll_afb_den_int(1, 3.5, wc_obj, par, 's', 'e', include_qed=True)
afb_den_low2_noqed = bxll_afb_den_int(3.5, 6, wc_obj, par, 's', 'e', include_qed=False)
afb_den_low2_qed = bxll_afb_den_int(3.5, 6, wc_obj, par, 's', 'e', include_qed=True)
self.assertAlmostEqual((afb_num_low1_qed-afb_num_low1_noqed)/afb_num_low1_qed,
-0.107, delta=0.050) # should lead to a -10.7% suppression
self.assertAlmostEqual((afb_num_low2_qed-afb_num_low2_noqed)/afb_num_low2_qed,
+0.162, delta=0.025) # should lead to a 16.2% enhancement
self.assertAlmostEqual((afb_den_low1_qed-afb_den_low1_noqed)/afb_den_low1_qed,
0.068, delta=0.005) # should lead to a 6.8% enhancement
self.assertAlmostEqual((afb_den_low2_qed-afb_den_low2_noqed)/afb_den_low2_qed,
0.031, delta=0.010) # should lead to a 3.1% enhancement
# compare SM predictions to arXiv:1503.04849
self.assertAlmostEqual(
flavio.sm_prediction('<AFB>(B->Xsee)', 1, 3.5)/(3/4.*(-1.03e-7)/((2.91e-7)+(6.35e-7))),
1, delta=0.15)
self.assertAlmostEqual(
flavio.sm_prediction('<AFB>(B->Xsee)', 3.5, 6)/(3/4.*(0.73e-7)/((2.43e-7)+(4.97e-7))),
1, delta=0.1)
self.assertAlmostEqual(
flavio.sm_prediction('<AFB>(B->Xsmumu)', 1, 3.5)/(3/4.*(-1.10e-7)/((2.09e-7)+(6.79e-7))),
1, delta=0.1)
self.assertAlmostEqual(
flavio.sm_prediction('<AFB>(B->Xsmumu)', 3.5, 6)/(3/4.*(0.67e-7)/((1.94e-7)+(5.34e-7))),
1, delta=0.15)
