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Update observables_bs.py
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Add Ang Obs for Bs2Phiee
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@gkhreich98
gkhreich98 authored Sep 5, 2024
1 parent 14982f8 commit b256a11
Showing 1 changed file with 93 additions and 2 deletions.
95 changes: 93 additions & 2 deletions flavio/physics/bdecays/bvll/observables_bs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
see arXiv:1502.05509."""

import flavio
from . import observables
import observables
from flavio.classes import Observable, Prediction
import cmath
import warnings
Expand Down Expand Up @@ -33,6 +33,31 @@ def bsvll_obs(function, q2, wc_obj, par, B, V, lep):
J_h = flavio.physics.bdecays.angular.angularcoeffs_h_v(phi, h, h_tilde, q2, mB, mV, mb, 0, ml, ml)
return function(y, J, J_bar, J_h)


def A_experiment_Bs(y, J, J_bar, J_h, i):
return A_experiment_num(y, J, J_bar, J_h, i)/SA_den_Bs(y, J, J_bar, J_h)

# numerator
def A_experiment_num_Bs(y, J, J_bar, J_h, i):
if i in [4, '6s', '6c', 7, 9]:
return -A_theory_num_Bs(y, J, J_bar, J_h, i)
return A_theory_num_Bs(y, J, J_bar, J_h, i)


def A_theory_Bs(y, J, J_bar, J_h, i):
r"""Angular CP asymmetry $A_i$ in the theory convention."""
return A_theory_num_Bs(y, J, J_bar, J_h, i)/SA_den_Bs(y, J, J_bar, J_h, i)

# numerator
def A_theory_num_Bs(y, J, J_bar, J_h, i):
return 1/(1-y**2) * (J[i] - J_bar[i]) - y/(1-y**2) * J_h[i]

# denominator of P_i observables
def P_den_Bs(y, J, J_bar, J_h):
return S_theory_num_Bs(y, J, J_bar, J_h,'2s')



def S_theory_num_Bs(y, J, J_bar, J_h, i):
# (42) of 1502.05509
flavio.citations.register("Descotes-Genon:2015hea")
Expand Down Expand Up @@ -135,18 +160,65 @@ def fct(wc_obj, par):
return fct

# Observable and Prediction instances

# https://arxiv.org/pdf/2210.11995
_tex = {'e': 'e', 'mu': '\mu', 'tau': r'\tau'}
_observables = {
'FL': {'func_num': FL_num_Bs, 'tex': r'\overline{F_L}', 'desc': 'Time-averaged longitudinal polarization fraction'},
'S3': {'func_num': lambda y, J, J_bar, J_h: S_experiment_num_Bs(y, J, J_bar, J_h, 3), 'tex': r'\overline{S_3}', 'desc': 'Time-averaged, CP-averaged angular observable'},
'S4': {'func_num': lambda y, J, J_bar, J_h: S_experiment_num_Bs(y, J, J_bar, J_h, 4), 'tex': r'\overline{S_4}', 'desc': 'Time-averaged, CP-averaged angular observable'},
'S5': {'func_num': lambda y, J, J_bar, J_h: S_experiment_num_Bs(y, J, J_bar, J_h, 5), 'tex': r'\overline{S_5}', 'desc': 'Time-averaged, CP-averaged angular observable'},
'S5': {'func_num': lambda y, J, J_bar, J_h: S_experiment_num_Bs(y, J, J_bar, J_h, 5), 'tex': r'\overline{S_5}', 'desc': 'Time-averaged, CP-averaged angular observable'},
'S7': {'func_num': lambda y, J, J_bar, J_h: S_experiment_num_Bs(y, J, J_bar, J_h, 7), 'tex': r'\overline{S_7}', 'desc': 'Time-averaged, CP-averaged angular observable'},
'S8': {'func_num': lambda y, J, J_bar, J_h: S_experiment_num_Bs(y, J, J_bar, J_h, 8), 'tex': r'\overline{S_8}', 'desc': 'Time-averaged, CP-averaged angular observable'},
'S9': {'func_num': lambda y, J, J_bar, J_h: S_experiment_num_Bs(y, J, J_bar, J_h, 9), 'tex': r'\overline{S_9}', 'desc': 'Time-averaged, CP-averaged angular observable'},
'A3': {'func_num': lambda y, J, J_bar, J_h: A_experiment_num_Bs(y, J, J_bar, J_h, 3), 'tex': r'\overline{A_3}', 'desc': 'Angular CP asymmetry'},
'A4': {'func_num': lambda y, J, J_bar, J_h: A_experiment_num_Bs(y, J, J_bar, J_h, 4), 'tex': r'\overline{A_4}', 'desc': 'Angular CP asymmetry'},
'A5': {'func_num': lambda y, J, J_bar, J_h: A_experiment_num_Bs(y, J, J_bar, J_h, 5), 'tex': r'\overline{A_5}', 'desc': 'Angular CP asymmetry'},
'A6s': {'func_num': lambda y, J, J_bar, J_h: A_experiment_num_Bs(y, J, J_bar, J_h, '6s'), 'tex': r'\overline{A_6^s}', 'desc': 'Angular CP asymmetry'},
'A7': {'func_num': lambda y, J, J_bar, J_h: A_experiment_num_Bs(y, J, J_bar, J_h, 7), 'tex': r'\overline{A_7}', 'desc': 'Angular CP asymmetry'},
'A8': {'func_num': lambda y, J, J_bar, J_h: A_experiment_num_Bs(y, J, J_bar, J_h, 8), 'tex': r'\overline{A_8}', 'desc': 'Angular CP asymmetry'},
'A9': {'func_num': lambda y, J, J_bar, J_h: A_experiment_num_Bs(y, J, J_bar, J_h, 9), 'tex': r'\overline{A_9}', 'desc': 'Angular CP asymmetry'},

}
_observables_p = {
'P1': {'func_num': lambda y, J, J_bar, J_h: S_experiment_num_Bs(y, J, J_bar, J_h, 3)/2., 'tex': r'P_1', 'desc': "CP-averaged \"optimized\" angular observable"},
'ATIm': {'func_num': lambda y, J, J_bar, J_h: A_experiment_num_Bs(y, J, J_bar, J_h, 9)/2., 'tex': r'A_T^\text{Im}', 'desc': "Transverse CP asymmetry"},
'ATRe': {'func_num': lambda y, J, J_bar, J_h: A_experiment_num_Bs(y, J, J_bar, J_h, 9)/4., 'tex': r'A_T^\text{2}', 'desc': "\"optimized\" angular CP asymmetry"},



}
_hadr = {
'Bs->phi': {'tex': r"B_s\to \phi ", 'B': 'Bs', 'V': 'phi', },
'Bs->K*0': {'tex': r"B_s\to K^* ", 'B': 'Bs', 'V': 'K*0', },
}

def make_metadata_binned_Bs(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


def make_metadata_differential_Bs(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


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

Expand All @@ -171,6 +243,25 @@ def fct(wc_obj, par):
_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))

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

# binned angular observables
_obs_name = "<" + obs + ">("+M+l+l+")"
_obs = Observable(name=_obs_name, arguments=['q2min', 'q2max'])
_obs.set_description('Binned ' + _observables_p[obs]['desc'] + r" in $" + _hadr[M]['tex'] +_tex[l]+r"^+"+_tex[l]+"^-$")
_obs.tex = r"$\langle " + _observables_p[obs]['tex'] + r"\rangle(" + _hadr[M]['tex'] +_tex[l]+r"^+"+_tex[l]+"^-)$"
_obs.add_taxonomy(_process_taxonomy)
Prediction(_obs_name, bsvll_obs_int_ratio_func(_observables_p[obs]['func_num'], P_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_p[obs]['desc'][0].capitalize() + _observables_p[obs]['desc'][1:] + r" in $" + _hadr[M]['tex'] +_tex[l]+r"^+"+_tex[l]+"^-$")
_obs.tex = r"$" + _observables_p[obs]['tex'] + r"(" + _hadr[M]['tex'] +_tex[l]+r"^+"+_tex[l]+"^-)$"
_obs.add_taxonomy(_process_taxonomy)
Prediction(_obs_name, bsvll_obs_ratio_func(_observables_p[obs]['func_num'], P_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'])
Expand Down

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