Esempio n. 1
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def make_obs_br(M, l):
    """Make observable instances for branching ratios"""
    _process_tex = _hadr[M]['tex'] + _tex[l] + r"^+" + _tex[l] + r"^-"
    _process_taxonomy = r'Process :: $b$ hadron decays :: FCNC decays :: $B\to V\ell^+\ell^-$ :: $' + _process_tex + r"$"
    B = _hadr[M]['B']
    V = _hadr[M]['V']

    # binned branching ratio
    _obs_name = "<dBR/dq2>(" + M + l + l + ")"
    _obs = Observable(name=_obs_name, arguments=['q2min', 'q2max'])
    _obs.set_description(r"Binned differential branching ratio of $" +
                         _process_tex + r"$")
    _obs.tex = r"$\langle \frac{d\text{BR}}{dq^2} \rangle(" + _process_tex + r")$"
    _obs.add_taxonomy(_process_taxonomy)
    func = lambda wc_obj, par, q2min, q2max: BVll_dBRdq2_int(
        q2min, q2max, B, V, l, wc_obj, par)()
    Prediction(_obs_name, func)

    # differential branching ratio
    _obs_name = "dBR/dq2(" + M + l + l + ")"
    _obs = Observable(name=_obs_name, arguments=['q2'])
    _obs.set_description(r"Differential branching ratio of $" + _process_tex +
                         r"$")
    _obs.tex = r"$\frac{d\text{BR}}{dq^2}(" + _process_tex + r")$"
    _obs.add_taxonomy(_process_taxonomy)
    func = lambda wc_obj, par, q2: BVll_dBRdq2(q2, B, V, l, wc_obj, par)()
    Prediction(_obs_name, func)
Esempio n. 2
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def _define_obs_B_Mll(M, ll):
    _process_tex = _hadr_lfv[M]['tex']+' '+_tex_lfv[''.join(ll)]
    _process_taxonomy = r'Process :: $b$ hadron decays :: FCNC decays :: $B\to P\ell^+\ell^-$ :: $' + _process_tex + r"$"
    _obs_name = "BR("+M+''.join(ll)+")"
    _obs = Observable(_obs_name)
    _obs.set_description(r"Total branching ratio of $"+_process_tex+r"$")
    _obs.tex = r"$\text{BR}(" + _process_tex+r")$"
    _obs.add_taxonomy(_process_taxonomy)
    return _obs_name
Esempio n. 3
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def make_metadata_binned(M, l, obs, obsdict):
    _process_tex = _hadr[M]['tex'] +_tex[l]+r"^+"+_tex[l]+r"^-"
    _process_taxonomy = r'Process :: $b$ hadron decays :: FCNC decays :: $B\to V\ell^+\ell^-$ :: $' + _process_tex + r"$"
    B = _hadr[M]['B']
    V = _hadr[M]['V']
    _obs_name = "<" + obs + ">("+M+l+l+")"
    _obs = Observable(name=_obs_name, arguments=['q2min', 'q2max'])
    _obs.set_description('Binned ' + obsdict['desc'] + r" in $" + _process_tex + r"$")
    _obs.tex = r"$\langle " + obsdict['tex'] + r"\rangle(" + _process_tex + r")$"
    _obs.add_taxonomy(_process_taxonomy)
    return _obs
Esempio n. 4
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def make_metadata_differential(M, l, obs, obsdict):
    _process_tex = _hadr[M]['tex'] +_tex[l]+r"^+"+_tex[l]+r"^-"
    _process_taxonomy = r'Process :: $b$ hadron decays :: FCNC decays :: $B\to V\ell^+\ell^-$ :: $' + _process_tex + r"$"
    B = _hadr[M]['B']
    V = _hadr[M]['V']
    _obs_name = obs + "("+M+l+l+")"
    _obs = Observable(name=_obs_name, arguments=['q2'])
    _obs.set_description(obsdict['desc'][0].capitalize() + obsdict['desc'][1:] + r" in $" + _process_tex + r"$")
    _obs.tex = r"$" + obsdict['tex'] + r"(" + _process_tex + r")$"
    _obs.add_taxonomy(_process_taxonomy)
    return _obs
Esempio n. 5
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def make_metadata_binned(M, l, obs, obsdict):
    _process_tex = _hadr[M]['tex'] +_tex[l]+r"^+"+_tex[l]+r"^-"
    _process_taxonomy = r'Process :: $b$ hadron decays :: FCNC decays :: $B\to V\ell^+\ell^-$ :: $' + _process_tex + r"$"
    B = _hadr[M]['B']
    V = _hadr[M]['V']
    _obs_name = "<" + obs + ">("+M+l+l+")"
    _obs = Observable(name=_obs_name, arguments=['q2min', 'q2max'])
    _obs.set_description('Binned ' + obsdict['desc'] + r" in $" + _process_tex + r"$")
    _obs.tex = r"$\langle " + obsdict['tex'] + r"\rangle(" + _process_tex + r")$"
    _obs.add_taxonomy(_process_taxonomy)
    return _obs
Esempio n. 6
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def make_metadata_differential(M, l, obs, obsdict):
    _process_tex = _hadr[M]['tex'] +_tex[l]+r"^+"+_tex[l]+r"^-"
    _process_taxonomy = r'Process :: $b$ hadron decays :: FCNC decays :: $B\to V\ell^+\ell^-$ :: $' + _process_tex + r"$"
    B = _hadr[M]['B']
    V = _hadr[M]['V']
    _obs_name = obs + "("+M+l+l+")"
    _obs = Observable(name=_obs_name, arguments=['q2'])
    _obs.set_description(obsdict['desc'][0].capitalize() + obsdict['desc'][1:] + r" in $" + _process_tex + r"$")
    _obs.tex = r"$" + obsdict['tex'] + r"(" + _process_tex + r")$"
    _obs.add_taxonomy(_process_taxonomy)
    return _obs
Esempio n. 7
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def make_obs_neutron_corr(coeff, me_E=False):
    _process_tex = r"n\to p^+ e^-\bar\nu_e"
    _process_taxonomy = r'Process :: Nucleon decays :: Beta decays :: Neutron decay :: $' + _process_tex + r"$"
    _obs_name = coeff + "_n"
    if me_E:
            _obs = Observable(_obs_name, arguments=['me_E'])
    else:
        _obs = Observable(_obs_name)
    _obs.set_description(r"Correlation coefficient $" + tex + r"$ in neutron beta decay")
    _obs.tex = r"$" + tex + r"$"
    _obs.add_taxonomy(_process_taxonomy)
    if me_E:
        func = lambda wc_obj, par, me_E: Neutron_corr(wc_obj, par, me_E, coeff)()
    else:
        func = lambda wc_obj, par: Neutron_corr(wc_obj, par, None, coeff)()
    Prediction(_obs_name, func)
Esempio n. 8
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def make_obs_lfur(M, l):
    """Make observable instances for lepton flavour ratios"""
    # binned ratio of BRs
    _obs_name = "<R" + l[0] + l[1] + ">(" + M + "ll)"
    _obs = Observable(name=_obs_name, arguments=['q2min', 'q2max'])
    _obs.set_description(r"Ratio of partial branching ratios of $" +
                         _hadr[M]['tex'] + _tex[l[0]] + r"^+ " + _tex[l[0]] +
                         r"^-$" + " and " + r"$" + _hadr[M]['tex'] +
                         _tex[l[1]] + r"^+ " + _tex[l[1]] + "^-$")
    _obs.tex = r"$\langle R_{" + _tex[l[0]] + ' ' + _tex[
        l[1]] + r"} \rangle(" + _hadr[M]['tex'] + r"\ell^+\ell^-)$"
    for li in l:
        # add taxonomy for both processes (e.g. B->Vee and B->Vmumu)
        _obs.add_taxonomy(
            r'Process :: $b$ hadron decays :: FCNC decays :: $B\to V\ell^+\ell^-$ :: $'
            + _hadr[M]['tex'] + _tex[li] + r"^+" + _tex[li] + r"^-$")
    Prediction(
        _obs_name,
        bvll_obs_int_ratio_leptonflavour(dGdq2_ave, _hadr[M]['B'],
                                         _hadr[M]['V'], *l))

    # differential ratio of BRs
    _obs_name = "R" + l[0] + l[1] + "(" + M + "ll)"
    _obs = Observable(name=_obs_name, arguments=['q2'])
    _obs.set_description(r"Ratio of differential branching ratios of $" +
                         _hadr[M]['tex'] + _tex[l[0]] + r"^+ " + _tex[l[0]] +
                         r"^-$" + " and " + r"$" + _hadr[M]['tex'] +
                         _tex[l[1]] + r"^+ " + _tex[l[1]] + "^-$")
    _obs.tex = r"$R_{" + _tex[l[0]] + ' ' + _tex[
        l[1]] + r"} (" + _hadr[M]['tex'] + r"\ell^+\ell^-)$"
    for li in l:
        # add taxonomy for both processes (e.g. B->Vee and B->Vmumu)
        _obs.add_taxonomy(
            r'Process :: $b$ hadron decays :: FCNC decays :: $B\to V\ell^+\ell^-$ :: $'
            + _hadr[M]['tex'] + _tex[li] + r"^+" + _tex[li] + r"^-$")
    func = lambda wc_obj, par, q2: BVll_dBRdq2(q2, B, V, l, wc_obj, par)()
    Prediction(
        _obs_name,
        bvll_obs_ratio_leptonflavour(dGdq2_ave, _hadr[M]['B'], _hadr[M]['V'],
                                     *l))
Esempio n. 9
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def make_obs_lfur(M, l):
    """Make observable instances for lepton flavour ratios"""
    # binned ratio of BRs
    _obs_name = "<R"+l[0]+l[1]+">("+M+"ll)"
    _obs = Observable(name=_obs_name, arguments=['q2min', 'q2max'])
    _obs.set_description(r"Ratio of partial branching ratios of $" + _hadr[M]['tex'] +_tex[l[0]]+r"^+ "+_tex[l[0]]+r"^-$" + " and " + r"$" + _hadr[M]['tex'] +_tex[l[1]]+r"^+ "+_tex[l[1]]+"^-$")
    _obs.tex = r"$\langle R_{" + _tex[l[0]] + ' ' + _tex[l[1]] + r"} \rangle(" + _hadr[M]['tex'] + r"\ell^+\ell^-)$"
    for li in l:
        # add taxonomy for both processes (e.g. B->Vee and B->Vmumu)
        _obs.add_taxonomy(r'Process :: $b$ hadron decays :: FCNC decays :: $B\to V\ell^+\ell^-$ :: $' + _hadr[M]['tex'] +_tex[li]+r"^+"+_tex[li]+r"^-$")
    Prediction(_obs_name, bvll_obs_int_ratio_leptonflavour(dGdq2_ave, _hadr[M]['B'], _hadr[M]['V'], *l))

    # differential ratio of BRs
    _obs_name = "R"+l[0]+l[1]+"("+M+"ll)"
    _obs = Observable(name=_obs_name, arguments=['q2'])
    _obs.set_description(r"Ratio of differential branching ratios of $" + _hadr[M]['tex'] +_tex[l[0]]+r"^+ "+_tex[l[0]]+r"^-$" + " and " + r"$" + _hadr[M]['tex'] +_tex[l[1]]+r"^+ "+_tex[l[1]]+"^-$")
    _obs.tex = r"$R_{" + _tex[l[0]] + ' ' + _tex[l[1]] + r"} (" + _hadr[M]['tex'] + r"\ell^+\ell^-)$"
    for li in l:
        # add taxonomy for both processes (e.g. B->Vee and B->Vmumu)
        _obs.add_taxonomy(r'Process :: $b$ hadron decays :: FCNC decays :: $B\to V\ell^+\ell^-$ :: $' + _hadr[M]['tex'] +_tex[li]+r"^+"+_tex[li]+r"^-$")
    func = lambda wc_obj, par, q2: BVll_dBRdq2(q2, B, V, l, wc_obj, par)()
    Prediction(_obs_name, bvll_obs_ratio_leptonflavour(dGdq2_ave, _hadr[M]['B'], _hadr[M]['V'], *l))
Esempio n. 10
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def make_obs_br(M, l):
    """Make observable instances for branching ratios"""
    _process_tex = _hadr[M]['tex'] +_tex[l]+r"^+"+_tex[l]+r"^-"
    _process_taxonomy = r'Process :: $b$ hadron decays :: FCNC decays :: $B\to V\ell^+\ell^-$ :: $' + _process_tex + r"$"
    B = _hadr[M]['B']
    V = _hadr[M]['V']

    # binned branching ratio
    _obs_name = "<dBR/dq2>("+M+l+l+")"
    _obs = Observable(name=_obs_name, arguments=['q2min', 'q2max'])
    _obs.set_description(r"Binned differential branching ratio of $" + _process_tex + r"$")
    _obs.tex = r"$\langle \frac{d\text{BR}}{dq^2} \rangle(" + _process_tex + r")$"
    _obs.add_taxonomy(_process_taxonomy)
    func = lambda wc_obj, par, q2min, q2max: BVll_dBRdq2_int(q2min, q2max, B, V, l, wc_obj, par)()
    Prediction(_obs_name, func)

    # differential branching ratio
    _obs_name = "dBR/dq2("+M+l+l+")"
    _obs = Observable(name=_obs_name, arguments=['q2'])
    _obs.set_description(r"Differential branching ratio of $" + _process_tex + r"$")
    _obs.tex = r"$\frac{d\text{BR}}{dq^2}(" + _process_tex + r")$"
    _obs.add_taxonomy(_process_taxonomy)
    func = lambda wc_obj, par, q2: BVll_dBRdq2(q2, B, V, l, wc_obj, par)()
    Prediction(_obs_name, func)
Esempio n. 11
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    wc_s = flavio.physics.bdecays.wilsoncoefficients.wctot_dict(wc_obj, "bsee", scale, par, nf_out=5)
    br_d = abs(xi_t_d) ** 2 * PE0_BR_BXgamma(wc_d, par, "d", E0)
    br_s = abs(xi_t_s) ** 2 * PE0_BR_BXgamma(wc_s, par, "s", E0)
    as_d = abs(xi_t_d) ** 2 * PE0_ACP_BXgamma(wc_d, par, "d", E0)
    as_s = abs(xi_t_s) ** 2 * PE0_ACP_BXgamma(wc_s, par, "s", E0)
    # return (as_s)/(br_s + br_d)
    return (as_s + as_d) / (br_s + br_d)


_process_taxonomy = r"Process :: $b$ hadron decays :: FCNC decays :: $B\to X\gamma$ :: "

_obs_name = "BR(B->Xsgamma)"
_obs = Observable(_obs_name)
_obs.set_description(r"CP-averaged branching ratio of $B\to X_s\gamma$ for $E_\gamma>1.6$ GeV")
_obs.tex = r"$\text{BR}(B\to X_s\gamma)$"
_obs.add_taxonomy(_process_taxonomy + r"$B\to X_s\gamma$")
Prediction(_obs_name, lambda wc_obj, par: BRBXgamma(wc_obj, par, "s", 1.6))

_obs_name = "BR(B->Xdgamma)"
_obs = Observable(_obs_name)
_obs.set_description(r"CP-averaged branching ratio of $B\to X_d\gamma$ for $E_\gamma>1.6$ GeV")
_obs.tex = r"$\text{BR}(B\to X_d\gamma)$"
_obs.add_taxonomy(_process_taxonomy + r"$B\to X_d\gamma$")
Prediction(_obs_name, lambda wc_obj, par: BRBXgamma(wc_obj, par, "d", 1.6))

_obs_name = "ACP(B->Xgamma)"
_obs = Observable(_obs_name)
_obs.set_description(r"Direct CP asymmetry in $B\to X_{s+d}\gamma$ for $E_\gamma>1.6$ GeV")
_obs.tex = r"$A_\text{CP}(B\to X_{s+d}\gamma)$"
_obs.add_taxonomy(_process_taxonomy + r"$B\to X_{s+d}\gamma$")
Prediction(_obs_name, lambda wc_obj, par: ACPBXgamma(wc_obj, par, 1.6))
Esempio n. 12
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    br_d = abs(xi_t_d)**2 * PE0_BR_BXgamma(wc_d, par, 'd', E0)
    br_s = abs(xi_t_s)**2 * PE0_BR_BXgamma(wc_s, par, 's', E0)
    as_d = abs(xi_t_d)**2 * PE0_ACP_BXgamma(wc_d, par, 'd', E0)
    as_s = abs(xi_t_s)**2 * PE0_ACP_BXgamma(wc_s, par, 's', E0)
    # return (as_s)/(br_s + br_d)
    return (as_s + as_d) / (br_s + br_d)


_process_taxonomy = r'Process :: $b$ hadron decays :: FCNC decays :: $B\to X\gamma$ :: '

_obs_name = "BR(B->Xsgamma)"
_obs = Observable(_obs_name)
_obs.set_description(
    r"CP-averaged branching ratio of $B\to X_s\gamma$ for $E_\gamma>1.6$ GeV")
_obs.tex = r"$\text{BR}(B\to X_s\gamma)$"
_obs.add_taxonomy(_process_taxonomy + r"$B\to X_s\gamma$")
Prediction(_obs_name, lambda wc_obj, par: BRBXgamma(wc_obj, par, 's', 1.6))

_obs_name = "BR(B->Xdgamma)"
_obs = Observable(_obs_name)
_obs.set_description(
    r"CP-averaged branching ratio of $B\to X_d\gamma$ for $E_\gamma>1.6$ GeV")
_obs.tex = r"$\text{BR}(B\to X_d\gamma)$"
_obs.add_taxonomy(_process_taxonomy + r"$B\to X_d\gamma$")
Prediction(_obs_name, lambda wc_obj, par: BRBXgamma(wc_obj, par, 'd', 1.6))

_obs_name = "ACP(B->Xgamma)"
_obs = Observable(_obs_name)
_obs.set_description(
    r"Direct CP asymmetry in $B\to X_{s+d}\gamma$ for $E_\gamma>1.6$ GeV")
_obs.tex = r"$A_\text{CP}(B\to X_{s+d}\gamma)$"
Esempio n. 13
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    return (-par['omega+'] / (sqrt(2) * par['eps_K'])
            * (ImA0 / ReA0 * (1 - par['Omegahat_eff'])
               - 1 / a * ImA2 / ReA2).real)


def epsprime_NP(wc_obj, par):
    r"""Compute the NP contribution to $\epsilon'/\epsilon$."""
    # Neglecting isospin breaking corrections!
    A = Kpipi_amplitudes_NP(wc_obj, par)
    ImA0 = A[0].imag
    ImA2 = A[2].imag
    ReA0 = par['ReA0(K->pipi)']
    ReA2 = par['ReA2(K->pipi)']
    a = par['epsp a']  # eq. (16)
    # dividing by a to remove the isospin brk corr in omega+, cf. (16) in 1507.06345
    return (-par['omega+'] / a / (sqrt(2) * par['eps_K'])
            * (ImA0 / ReA0 - ImA2 / ReA2).real)

def epsprime(wc_obj, par):
    r"""Compute $\epsilon'/\epsilon$, parametrizing direct CPV in
    $K\to\pi\pi$."""
    return epsprime_SM(par) + epsprime_NP(wc_obj, par)


# Observable and Prediction instances
o = Observable('epsp/eps')
o.tex = r"$\epsilon^\prime/\epsilon$"
Prediction('epsp/eps', epsprime)
o.set_description(r"Direct CP violation parameter")
o.add_taxonomy(r'Process :: $s$ hadron decays :: Non-leptonic decays :: $K\to \pi\pi$')
Esempio n. 14
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'Bs->phi': {'tex': r"B_s\to \phi ", 'B': 'Bs', 'V': 'phi', },
}
for l in ['e', 'mu', 'tau']:
    for M in _hadr.keys():

        _process_tex = _hadr[M]['tex'] +_tex[l]+r"^+"+_tex[l]+r"^-"
        _process_taxonomy = r'Process :: $b$ hadron decays :: FCNC decays :: $B\to V\ell^+\ell^-$ :: $' + _process_tex + r"$"

        for obs in sorted(_observables.keys()):

            # binned angular observables
            _obs_name = "<" + obs + ">("+M+l+l+")"
            _obs = Observable(name=_obs_name, arguments=['q2min', 'q2max'])
            _obs.set_description('Binned ' + _observables[obs]['desc'] + r" in $" + _hadr[M]['tex'] +_tex[l]+r"^+"+_tex[l]+"^-$")
            _obs.tex = r"$\langle " + _observables[obs]['tex'] + r"\rangle(" + _hadr[M]['tex'] +_tex[l]+r"^+"+_tex[l]+"^-)$"
            _obs.add_taxonomy(_process_taxonomy)
            Prediction(_obs_name, bsvll_obs_int_ratio_func(_observables[obs]['func_num'], SA_den_Bs, _hadr[M]['B'], _hadr[M]['V'], l))

            # differential angular observables
            _obs_name = obs + "("+M+l+l+")"
            _obs = Observable(name=_obs_name, arguments=['q2'])
            _obs.set_description(_observables[obs]['desc'][0].capitalize() + _observables[obs]['desc'][1:] + r" in $" + _hadr[M]['tex'] +_tex[l]+r"^+"+_tex[l]+"^-$")
            _obs.tex = r"$" + _observables[obs]['tex'] + r"(" + _hadr[M]['tex'] +_tex[l]+r"^+"+_tex[l]+"^-)$"
            _obs.add_taxonomy(_process_taxonomy)
            Prediction(_obs_name, bsvll_obs_ratio_func(_observables[obs]['func_num'], SA_den_Bs, _hadr[M]['B'], _hadr[M]['V'], l))

        # binned branching ratio
        _obs_name = "<dBR/dq2>("+M+l+l+")"
        _obs = Observable(name=_obs_name, arguments=['q2min', 'q2max'])
        _obs.set_description(r"Binned time-integrated differential branching ratio of $" + _hadr[M]['tex'] +_tex[l]+r"^+"+_tex[l]+"^-$")
        _obs.tex = r"$\langle \frac{d\overline{\text{BR}}}{dq^2} \rangle(" + _hadr[M]['tex'] +_tex[l]+r"^+"+_tex[l]+"^-)$"
Esempio n. 15
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def BR_tot_leptonflavour_function(lnum, lden):
    return lambda wc_obj, par: BR_tot_leptonflavour(wc_obj, par, lnum, lden)


_process_taxonomy = r'Process :: $b$ hadron decays :: Semi-leptonic tree-level decays :: $B\to X\ell\nu$ :: $'

_lep = {'e': 'e', 'mu': r'\mu', 'tau': r'\tau', 'l': r'\ell'}

for l in _lep:
    _obs_name = "BR(B->Xc" + l + "nu)"
    _process_tex = r"B\to X_c" + _lep[l] + r"^+\nu_" + _lep[l]
    _obs = Observable(_obs_name)
    _obs.set_description(r"Total branching ratio of $" + _process_tex + r"$")
    _obs.tex = r"$\text{BR}(" + _process_tex + r")$"
    _obs.add_taxonomy(_process_taxonomy + _process_tex + r"$")
    Prediction(_obs_name, BR_tot_function(l))

# Lepton flavour ratios
for l in [('mu', 'e'), ('tau', 'mu'), ('tau', 'l')]:
    _obs_name = "R" + l[0] + l[1] + "(B->Xclnu)"
    _obs = Observable(name=_obs_name)
    _process_1 = r"B\to X_c" + _lep[l[0]] + r"^+\nu_" + _lep[l[0]]
    _process_2 = r"B\to X_c" + _lep[l[1]] + r"^+\nu_" + _lep[l[1]]
    _obs.set_description(r"Ratio of total branching ratios of $" + _process_1 +
                         r"$" + " and " + r"$" + _process_2 + r"$")
    _obs.tex = r"$R_{" + _lep[l[0]] + ' ' + _lep[
        l[1]] + r"}(B\to X_c\ell^+\nu)$"
    # add taxonomy for both processes (e.g. B->Xcenu and B->Xcmunu)
    _obs.add_taxonomy(_process_taxonomy + _process_1 + r"$")
    _obs.add_taxonomy(_process_taxonomy + _process_2 + r"$")
Esempio n. 16
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             (1 - par['Omegahat_eff']) - 1 / a * ImA2 / ReA2).real)


def epsprime_NP(wc_obj, par):
    r"""Compute the NP contribution to $\epsilon'/\epsilon$."""
    # Neglecting isospin breaking corrections!
    A = Kpipi_amplitudes_NP(wc_obj, par)
    ImA0 = A[0].imag
    ImA2 = A[2].imag
    ReA0 = par['ReA0(K->pipi)']
    ReA2 = par['ReA2(K->pipi)']
    a = par['epsp a']  # eq. (16)
    # dividing by a to remove the isospin brk corr in omega+, cf. (16) in 1507.06345
    return (-par['omega+'] / a / (sqrt(2) * par['eps_K']) *
            (ImA0 / ReA0 - ImA2 / ReA2).real)


def epsprime(wc_obj, par):
    r"""Compute $\epsilon'/\epsilon$, parametrizing direct CPV in
    $K\to\pi\pi$."""
    return epsprime_SM(par) + epsprime_NP(wc_obj, par)


# Observable and Prediction instances
o = Observable('epsp/eps')
o.tex = r"$\epsilon^\prime/\epsilon$"
Prediction('epsp/eps', epsprime)
o.set_description(r"Direct CP violation parameter")
o.add_taxonomy(
    r'Process :: $s$ hadron decays :: Non-leptonic decays :: $K\to \pi\pi$')
Esempio n. 17
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# Observable and Prediction instances

_tex = {'e': 'e', 'mu': '\mu', 'tau': r'\tau'}
for l in ['e', 'mu', 'tau']:
    _process_taxonomy = r'Process :: $b$ hadron decays :: FCNC decays :: $B\to\ell^+\ell^-$ :: $'

    # For the Bs decay, we take the time-integrated branching ratio
    _obs_name = "BR(Bs->" + l + l + ")"
    _obs = Observable(_obs_name)
    _process_tex = r"B_s\to " + _tex[l] + r"^+" + _tex[l] + r"^-"
    _obs.set_description(r"Time-integrated branching ratio of $" +
                         _process_tex + r"$.")
    _obs.tex = r"$\overline{\text{BR}}(" + _process_tex + r")$"
    _obs.add_taxonomy(_process_taxonomy + _process_tex + r"$")
    Prediction(_obs_name, bqll_obs_function(br_timeint, 'Bs', l, l))

    # Add the effective lifetimes for Bs
    _obs_name = 'tau_' + l + l
    _obs = Observable(_obs_name)
    _obs.set_description(r"Effective lifetime for $" + _process_tex + r"$.")
    _obs.tex = r"$\tau_{B_s \to " + _tex[l] + _tex[l] + "}$"
    _obs.add_taxonomy(_process_taxonomy + _process_tex + r"$")
    if l == 'e':
        Prediction(_obs_name,
                   lambda wc_obj, par: tau_ll_func(wc_obj, par, 'Bs', 'e'))
    if l == 'mu':
        Prediction(_obs_name,
                   lambda wc_obj, par: tau_ll_func(wc_obj, par, 'Bs', 'mu'))
    if l == 'tau':
Esempio n. 18
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def CR_mueAl(wc_obj, par):
    r"""Conversion rate for $\phantom k^{27}_{13} \mathrm{Al}$"""
    return CR_mue(wc_obj, par, 'Al')


def CR_mueTi(wc_obj, par):
    r"""Conversion rate for $\phantom k^{48}_{22} \mathrm{Ti}$"""
    return CR_mue(wc_obj, par, 'Ti')


CRAu = Observable('CR(mu->e, Au)')
Prediction('CR(mu->e, Au)', CR_mueAu)
CRAu.tex = r"$CR(\mu - e)$ in $\phantom k^{197}_{79} \mathrm{Au}$"
CRAu.description = r"Coherent conversion rate of $\mu^-$ to $e^-$ in $\phantom k^{197}_{79} \mathrm{Au}$"
CRAu.add_taxonomy(
    r'Process :: muon decays :: LFV decays :: $\mu N \to e N$ :: ' + CRAu.tex)

CRAl = Observable('CR(mu->e, Al)')
Prediction('CR(mu->e, Al)', CR_mueAl)
CRAl.tex = r"$CR(\mu - e)$ in $\phantom k^{27}_{13} \mathrm{Al}$"
CRAl.description = r"Coherent conversion rate of $\mu^-$ to $e^-$ in $\phantom k^{27}_{13} \mathrm{Al}$"
CRAl.add_taxonomy(
    r'Process :: muon decays :: LFV decays :: $\mu N \to e N$ :: ' + CRAl.tex)

CRTi = Observable('CR(mu->e, Ti)')
Prediction('CR(mu->e, Ti)', CR_mueTi)
CRTi.tex = r"$CR(\mu - e)$ in $\phantom k^{48}_{22} \mathrm{Ti}$"
CRTi.description = r"Coherent conversion rate of $\mu^-$ to $e^-$ in $\phantom k^{48}_{22} \mathrm{Ti}$"
CRTi.add_taxonomy(
    r'Process :: muon decays :: LFV decays :: $\mu N \to e N$ :: ' + CRTi.tex)
Esempio n. 19
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'FL': {'func_num': FL_num, 'tex': r'F_L', 'desc': 'longitudinal polarization fraction'},
'AFBl': {'func_num': AFBl_num, 'tex': r'A_\text{FB}^\ell', 'desc': 'leptonic forward-backward asymmetry'},
'AFBh': {'func_num': AFBh_num, 'tex': r'A_\text{FB}^h', 'desc': 'hadronic forward-backward asymmetry'},
'AFBlh': {'func_num': AFBlh_num, 'tex': r'A_\text{FB}^{\ell h}', 'desc': 'lepton-hadron forward-backward asymmetry'},
}
for l in ['e', 'mu', ]: # tau requires lepton mass dependence!

    _process_tex = r"\Lambda_b\to\Lambda " +_tex[l]+r"^+"+_tex[l]+r"^-"
    _process_taxonomy = r'Process :: $b$ hadron decays :: FCNC decays :: $\Lambda_b\to \Lambda\ell^+\ell^-$ :: $' + _process_tex + r"$"

    # binned branching ratio
    _obs_name = "<dBR/dq2>(Lambdab->Lambda"+l+l+")"
    _obs = Observable(name=_obs_name, arguments=['q2min', 'q2max'])
    _obs.set_description(r"Binned differential branching ratio of $" + _process_tex + r"$")
    _obs.tex = r"$\langle \frac{d\text{BR}}{dq^2} \rangle(" + _process_tex + r")$"
    _obs.add_taxonomy(_process_taxonomy)
    Prediction(_obs_name, dbrdq2_int_func(l))

    # differential branching ratio
    _obs_name = "dBR/dq2(Lambdab->Lambda"+l+l+")"
    _obs = Observable(name=_obs_name, arguments=['q2'])
    _obs.set_description(r"Differential branching ratio of $" + _process_tex + r"$")
    _obs.tex = r"$\frac{d\text{BR}}{dq^2}(" + _process_tex + r")$"
    _obs.add_taxonomy(_process_taxonomy)
    Prediction(_obs_name, dbrdq2_func(l))

    for obs in _observables:
        # binned angular observables
        _obs_name = "<" + obs + ">(Lambdab->Lambda"+l+l+")"
        _obs = Observable(name=_obs_name, arguments=['q2min', 'q2max'])
        _obs.set_description("Binned " + _observables[obs]['desc'] + r" in $" + _process_tex + r"$")
Esempio n. 20
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# Observable instances

_tex = {'e': 'e', 'mu': '\mu', 'tau': r'\tau', 'l': r'\ell'}
for l in ['e', 'mu', 'tau', 'l']:
    for q in ['s', 'd']:

        _process_tex =  r"B\to X_" + q +_tex[l]+r"^+"+_tex[l]+r"^-"
        _process_taxonomy = r'Process :: $b$ hadron decays :: FCNC decays :: $B\to X\ell^+\ell^-$ :: $' + _process_tex + r"$"

        # binned branching ratio
        _obs_name = "<BR>(B->X"+q+l+l+")"
        _obs = Observable(name=_obs_name, arguments=['q2min', 'q2max'])
        _obs.set_description(r"Binned branching ratio of $" + _process_tex + r"$")
        _obs.tex = r"$\langle \text{BR} \rangle(" + _process_tex + r")$"
        _obs.add_taxonomy(_process_taxonomy)
        Prediction(_obs_name, bxll_br_int_func(q, l))

        # differential branching ratio
        _obs_name = "dBR/dq2(B->X"+q+l+l+")"
        _obs = Observable(name=_obs_name, arguments=['q2'])
        _obs.set_description(r"Differential branching ratio of $" + _process_tex + r"$")
        _obs.tex = r"$\frac{d\text{BR}}{dq^2}(" + _process_tex + r")$"
        _obs.add_taxonomy(_process_taxonomy)
        Prediction(_obs_name, bxll_dbrdq2_func(q, l))

        if l != 'tau': # AFB not yet implemented for tau! (ml=0)

            # binned AFB
            _obs_name = "<AFB>(B->X"+q+l+l+")"
            _obs = Observable(name=_obs_name, arguments=['q2min', 'q2max'])
Esempio n. 21
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for l in ['e', 'mu', 'tau', 'l']:
    for br in ['dBR/dq2', 'BR', '<BR>',
               'dBR_L/dq2', 'BR_L', '<BR_L>',
               'dBR_T/dq2', 'BR_T', '<BR_T>',
               '<BR>/<cl>', '<BR>/<cV>', '<BR>/<phi>',
               'dBR/dcl', 'dBR/dcV', 'dBR/dphi',
               '<FL>', 'FL', 'FLtot']:
        for M in _hadr.keys():
            _process_tex = _hadr[M]['tex']+_tex[l]+r"^+\nu_"+_tex[l]
            _obs_name = br + "("+M+l+"nu)"
            _obs = Observable(_obs_name)
            _obs.set_description(_desc[br] + r" branching ratio of $" + _process_tex + "$")
            _obs.tex = r'$' + _tex_br[br] + r"(" +_process_tex + ")$"
            _obs.arguments = _args[br]
            _obs.add_taxonomy(_process_taxonomy + _process_tex +  r'$')
            if br in _A:
                # for dBR/dq2, need to distinguish between total, L, and T
                Prediction(_obs_name, _func[br](_hadr[M]['B'], _hadr[M]['V'], l, A=_A[br]))
            else:
                # for other observables not
                Prediction(_obs_name, _func[br](_hadr[M]['B'], _hadr[M]['V'], l))


# Lepton flavour ratios
for l in [('mu','e'), ('tau','mu'), ('tau', 'l')]:
    for M in _hadr_l.keys():

        # binned ratio of BRs
        _obs_name = "<R"+l[0]+l[1]+">("+M+"lnu)"
        _obs = Observable(name=_obs_name, arguments=['q2min', 'q2max'])
Esempio n. 22
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    xi = etaCP * qp * A / A_bar
    return -2*xi.imag / ( 1 + abs(xi)**2 )

def S_BJpsiK(wc_obj, par):
    return S(wc_obj, par, 'B0', amplitude_BJpsiK, etaCP=-1)

def S_Bspsiphi(wc_obj, par):
    return S(wc_obj, par, 'Bs', amplitude_Bspsiphi, etaCP=+1)


# Observable and Prediction instances

o = Observable('DeltaM_s')
o.set_description(r"Mass difference in the $B_s$-$\bar B_s$ system")
o.tex = r"$\Delta M_s$"
o.add_taxonomy(r'Process :: Meson-antimeson mixing ::  $B_s$-$\bar B_s$ mixing')
Prediction('DeltaM_s', lambda wc_obj, par: DeltaM(wc_obj, par, 'Bs'))

o = Observable('DeltaM_d')
o.set_description(r"Mass difference in the $B^0$-$\bar B^0$ system")
o.tex = r"$\Delta M_d$"
o.add_taxonomy(r'Process :: Meson-antimeson mixing ::  $B^0$-$\bar B^0$ mixing')
Prediction('DeltaM_d', lambda wc_obj, par: DeltaM(wc_obj, par, 'B0'))

o = Observable('a_fs_s')
o.set_description(r"CP asymmetry in flavour-specific $B_s$ decays")
o.tex = r"$a_\text{fs}^s$"
o.add_taxonomy(r'Process :: Meson-antimeson mixing ::  $B_s$-$\bar B_s$ mixing')
Prediction('a_fs_s', lambda wc_obj, par: a_fs(wc_obj, par, 'Bs'))

o = Observable('a_fs_d')
Esempio n. 23
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# Observable instances

_tex = {'e': 'e', 'mu': '\mu', 'tau': r'\tau', 'l': r'\ell'}
for l in ['e', 'mu', 'tau', 'l']:
    for q in ['s', 'd']:

        _process_tex = r"B\to X_" + q + _tex[l] + r"^+" + _tex[l] + r"^-"
        _process_taxonomy = r'Process :: $b$ hadron decays :: FCNC decays :: $B\to X\ell^+\ell^-$ :: $' + _process_tex + r"$"

        # binned branching ratio
        _obs_name = "<BR>(B->X" + q + l + l + ")"
        _obs = Observable(name=_obs_name, arguments=['q2min', 'q2max'])
        _obs.set_description(r"Binned branching ratio of $" + _process_tex +
                             r"$")
        _obs.tex = r"$\langle \text{BR} \rangle(" + _process_tex + r")$"
        _obs.add_taxonomy(_process_taxonomy)
        Prediction(_obs_name, bxll_br_int_func(q, l))

        # differential branching ratio
        _obs_name = "dBR/dq2(B->X" + q + l + l + ")"
        _obs = Observable(name=_obs_name, arguments=['q2'])
        _obs.set_description(r"Differential branching ratio of $" +
                             _process_tex + r"$")
        _obs.tex = r"$\frac{d\text{BR}}{dq^2}(" + _process_tex + r")$"
        _obs.add_taxonomy(_process_taxonomy)
        Prediction(_obs_name, bxll_dbrdq2_func(q, l))

        if l != 'tau':  # AFB not yet implemented for tau! (ml=0)

            # binned AFB
            _obs_name = "<AFB>(B->X" + q + l + l + ")"
Esempio n. 24
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    xi = etaCP * qp * A / A_bar
    return -2*xi.imag / ( 1 + abs(xi)**2 )

def S_BJpsiK(wc_obj, par):
    return S(wc_obj, par, 'B0', amplitude_BJpsiK, etaCP=-1)

def S_Bspsiphi(wc_obj, par):
    return S(wc_obj, par, 'Bs', amplitude_Bspsiphi, etaCP=+1)


# Observable and Prediction instances

o = Observable('DeltaM_s')
o.set_description(r"Mass difference in the $B_s$-$\bar B_s$ system")
o.tex = r"$\Delta M_s$"
o.add_taxonomy(r'Process :: Meson-antimeson mixing ::  $B_s$-$\bar B_s$ mixing')
Prediction('DeltaM_s', lambda wc_obj, par: DeltaM_positive(wc_obj, par, 'Bs'))

o = Observable('DeltaM_d')
o.set_description(r"Mass difference in the $B^0$-$\bar B^0$ system")
o.tex = r"$\Delta M_d$"
o.add_taxonomy(r'Process :: Meson-antimeson mixing ::  $B^0$-$\bar B^0$ mixing')
Prediction('DeltaM_d', lambda wc_obj, par: DeltaM_positive(wc_obj, par, 'B0'))

o = Observable('DeltaM_d/DeltaM_s')
o.set_description(r"Ratio of Mass differences in the $B^0$-$\bar B^0$ and $B_s$-$\bar B_s$ systems")
o.tex = r"$\Delta M_d/\Delta M_s$"
o.add_taxonomy(r'Process :: Meson-antimeson mixing ::  $B^0$-$\bar B^0$ mixing')
o.add_taxonomy(r'Process :: Meson-antimeson mixing ::  $B_s$-$\bar B_s$ mixing')
Prediction('DeltaM_d/DeltaM_s', lambda wc_obj, par: DeltaM_positive(wc_obj, par, 'B0')
                                                    / DeltaM_positive(wc_obj, par, 'Bs'))
Esempio n. 25
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    return num/den

def BR_tot_leptonflavour_function(lnum, lden):
    return lambda wc_obj, par: BR_tot_leptonflavour(wc_obj, par, lnum, lden)

_process_taxonomy = r'Process :: $b$ hadron decays :: Semi-leptonic tree-level decays :: $B\to X\ell\nu$ :: $'

_lep = {'e': 'e', 'mu': r'\mu', 'tau': r'\tau', 'l': r'\ell'}

for l in _lep:
        _obs_name = "BR(B->Xc"+l+"nu)"
        _process_tex = r"B\to X_c"+_lep[l]+r"^+\nu_"+_lep[l]
        _obs = Observable(_obs_name)
        _obs.set_description(r"Total branching ratio of $" + _process_tex + r"$")
        _obs.tex = r"$\text{BR}(" + _process_tex + r")$"
        _obs.add_taxonomy(_process_taxonomy + _process_tex + r"$")
        Prediction(_obs_name, BR_tot_function(l))

# Lepton flavour ratios
for l in [('mu','e'), ('tau','mu'), ('tau', 'l')]:
    _obs_name = "R"+l[0]+l[1]+"(B->Xclnu)"
    _obs = Observable(name=_obs_name)
    _process_1 = r"B\to X_c"+_lep[l[0]]+r"^+\nu_"+_lep[l[0]]
    _process_2 = r"B\to X_c"+_lep[l[1]]+r"^+\nu_"+_lep[l[1]]
    _obs.set_description(r"Ratio of total branching ratios of $" + _process_1 + r"$" + " and " + r"$" + _process_2 +r"$")
    _obs.tex = r"$R_{" + _lep[l[0]] + ' ' + _lep[l[1]] + r"}(B\to X_c\ell^+\nu)$"
        # add taxonomy for both processes (e.g. B->Xcenu and B->Xcmunu)
    _obs.add_taxonomy(_process_taxonomy + _process_1 + r"$")
    _obs.add_taxonomy(_process_taxonomy + _process_2 + r"$")
    Prediction(_obs_name, BR_tot_leptonflavour_function(l[0], l[1]))
Esempio n. 26
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def BR_tot_function(B, V, lep):
    return lambda wc_obj, par: BR_tot(wc_obj, par, B, V, lep)


# Observable and Prediction instances

_tex = {'e': 'e', 'mu': '\mu', 'tau': r'\tau', 'l': r'\ell'}
_func = {'dBR/dq2': dBRdq2_function, 'BR': BR_tot_function, '<BR>': BR_binned_function}
_desc = {'dBR/dq2': 'Differential', 'BR': 'Total', '<BR>': 'Binned'}
_tex_br = {'dBR/dq2': r'\frac{d\text{BR}}{dq^2}', 'BR': r'\text{BR}', '<BR>': r'\langle\text{BR}\rangle'}
_args = {'dBR/dq2': ['q2'], 'BR': None, '<BR>': ['q2min', 'q2max']}
_hadr = {
'B0->D*': {'tex': r"B^0\to D^{\ast -}", 'B': 'B0', 'V': 'D*+', },
'B+->D*': {'tex': r"B^+\to D^{\ast 0}", 'B': 'B+', 'V': 'D*0', },
'B0->rho': {'tex': r"B^0\to \rho^-", 'B': 'B0', 'V': 'rho+', },
'B+->rho': {'tex': r"B^+\to \rho^0", 'B': 'B+', 'V': 'rho0', },
'B+->omega': {'tex': r"B^+\to \omega ", 'B': 'B+', 'V': 'omega', },
'Bs->K*': {'tex': r"B_s\to K^{* -} ", 'B': 'Bs', 'V': 'K*+', },
}
for l in ['e', 'mu', 'tau', 'l']:
    for br in ['dBR/dq2', 'BR', '<BR>']:
        for M in _hadr.keys():
            _process_tex = _hadr[M]['tex']+_tex[l]+r"^+\nu_"+_tex[l]
            _obs_name = br + "("+M+l+"nu)"
            _obs = Observable(_obs_name)
            _obs.set_description(_desc[br] + r" branching ratio of $" + _process_tex + "$")
            _obs.tex = r'$' + _tex_br[br] + r"(" +_process_tex + ")$"
            _obs.arguments = _args[br]
            _obs.add_taxonomy(r'Process :: $b$ hadron decays :: Semi-leptonic tree-level decays :: $B\to V\ell\nu$ :: $' + _process_tex +  r'$')
            Prediction(_obs_name, _func[br](_hadr[M]['B'], _hadr[M]['V'], l))
Esempio n. 27
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    },
}

_process_taxonomy = r'Process :: $b$ hadron decays :: Semi-leptonic tree-level decays :: $B\to V\ell\nu$ :: $'

for l in ['e', 'mu', 'tau', 'l']:
    for br in ['dBR/dq2', 'BR', '<BR>']:
        for M in _hadr.keys():
            _process_tex = _hadr[M]['tex'] + _tex[l] + r"^+\nu_" + _tex[l]
            _obs_name = br + "(" + M + l + "nu)"
            _obs = Observable(_obs_name)
            _obs.set_description(_desc[br] + r" branching ratio of $" +
                                 _process_tex + "$")
            _obs.tex = r'$' + _tex_br[br] + r"(" + _process_tex + ")$"
            _obs.arguments = _args[br]
            _obs.add_taxonomy(_process_taxonomy + _process_tex + r'$')
            Prediction(_obs_name, _func[br](_hadr[M]['B'], _hadr[M]['V'], l))

# Lepton flavour ratios
for l in [('mu', 'e'), ('tau', 'mu'), ('tau', 'l')]:
    for M in _hadr_l.keys():

        # binned ratio of BRs
        _obs_name = "<R" + l[0] + l[1] + ">(" + M + "lnu)"
        _obs = Observable(name=_obs_name, arguments=['q2min', 'q2max'])
        _obs.set_description(r"Ratio of partial branching ratios of $" +
                             _hadr_l[M]['tex'] + _tex[l[0]] + r"^+ \nu_" +
                             _tex[l[0]] + r"$" + " and " + r"$" +
                             _hadr_l[M]['tex'] + _tex[l[1]] + r"^+ \nu_" +
                             _tex[l[1]] + r"$")
        _obs.tex = r"$\langle R_{" + _tex[l[0]] + ' ' + _tex[
Esempio n. 28
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}

for l in ['e', 'mu', 'tau']:
    for M in _hadr.keys():

        _process_tex = _hadr[M]['tex'] + _tex[l] + r"^+" + _tex[l] + r"^-"
        _process_taxonomy = r'Process :: $b$ hadron decays :: FCNC decays :: $B\to P\ell^+\ell^-$ :: $' + _process_tex + r"$"

        for obs in sorted(_observables.keys()):
            _obs_name = "<" + obs + ">(" + M + l + l + ")"
            _obs = Observable(name=_obs_name, arguments=['q2min', 'q2max'])
            _obs.set_description('Binned ' + _observables[obs]['desc'] +
                                 r" in $" + _process_tex + r"$")
            _obs.tex = r"$\langle " + _observables[obs][
                'tex'] + r"\rangle(" + _process_tex + r")$"
            _obs.add_taxonomy(_process_taxonomy)
            Prediction(
                _obs_name,
                bpll_obs_int_ratio_func(_observables[obs]['func_num'],
                                        dGdq2_cpaverage, _hadr[M]['B'],
                                        _hadr[M]['P'], l))

            _obs_name = obs + "(" + M + l + l + ")"
            _obs = Observable(name=_obs_name, arguments=['q2'])
            _obs.set_description(_observables[obs]['desc'][0].capitalize() +
                                 _observables[obs]['desc'][1:] + r" in $" +
                                 _process_tex + r"$")
            _obs.tex = r"$" + _observables[obs][
                'tex'] + r"(" + _process_tex + r")$"
            _obs.add_taxonomy(_process_taxonomy)
            Prediction(
Esempio n. 29
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    'dBR/dq2': r'\frac{d\text{BR}}{dq^2}',
    'BR': r'\text{BR}',
    '<BR>': r'\langle\text{BR}\rangle'
}
_args = {'dBR/dq2': ['q2'], 'BR': None, '<BR>': ['q2min', 'q2max']}
_hadr = {
    'KL->pi': {
        'tex': r"K_L\to \pi^+",
        'K': 'KL',
        'P': 'pi+',
    },
    'K+->pi': {
        'tex': r"K^+\to \pi^0",
        'K': 'K+',
        'P': 'pi0',
    },
}

for l in ['e', 'mu', 'l']:
    for M in _hadr.keys():
        _process_tex = _hadr[M]['tex'] + _tex[l] + r"^+\nu_" + _tex[l]
        _process_taxonomy = r'Process :: $s$ hadron decays :: Semi-leptonic tree-level decays :: $K\to P\ell\nu$ :: $' + _process_tex + r"$"

        _obs_name = "BR(" + M + l + "nu)"
        _obs = Observable(_obs_name)
        _obs.set_description(r"Total branching ratio of $" + _process_tex +
                             r"$")
        _obs.tex = r"$\text{BR}(" + _process_tex + r")$"
        _obs.add_taxonomy(_process_taxonomy)
        Prediction(_obs_name, BR_tot_function(_hadr[M]['K'], _hadr[M]['P'], l))
Esempio n. 30
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        wc = wctot_dict(wc_obj, label, scale, par)
        return ADeltaGamma(par, wc, B, lep)
    return ADG_func
# Observable and Prediction instances

_tex = {'e': 'e', 'mu': '\mu', 'tau': r'\tau'}
for l in ['e', 'mu', 'tau']:
    _process_taxonomy = r'Process :: $b$ hadron decays :: FCNC decays :: $B\to\ell^+\ell^-$ :: $'

    # For the B^0 decay, we take the time-integrated branching ratio
    _obs_name = "BR(Bs->"+l+l+")"
    _obs = Observable(_obs_name)
    _process_tex = r"B_s\to "+_tex[l]+r"^+"+_tex[l]+r"^-"
    _obs.set_description(r"Time-integrated branching ratio of $" + _process_tex + r"$.")
    _obs.tex = r"$\overline{\text{BR}}(" + _process_tex + r")$"
    _obs.add_taxonomy(_process_taxonomy + _process_tex + r"$")
    Prediction(_obs_name, bqll_obs_function(br_timeint, 'Bs', l, l))


    # Add the effective lifetimes for Bs
    _obs_name = 'tau_'+l+l
    _obs = Observable(_obs_name)
    _obs.set_description(r"Effective lifetime for $"+ _process_tex + r"$.")
    _obs.tex = r"$\tau_{B_s \to " +_tex[l] +_tex[l] + "}$"
    _obs.add_taxonomy(_process_taxonomy + _process_tex + r"$")
    if l=='e':
        Prediction(_obs_name, lambda wc_obj, par: tau_ll_func(wc_obj, par, 'Bs', 'e'))
    if l=='mu':
        Prediction(_obs_name, lambda wc_obj, par: tau_ll_func(wc_obj, par, 'Bs', 'mu'))
    if l=='tau':
        Prediction(_obs_name, lambda wc_obj, par: tau_ll_func(wc_obj, par, 'Bs', 'tau'))