# Exploring the wilson Python package¶

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Insert a new cell below by pressing 'b'.

In [6]:
import wilson


## Examples from the paper¶

In [8]:
mywilson = wilson.Wilson({'uG_33': 1e-6},
scale=1e3, eft='SMEFT', basis='Warsaw')


In a Jupyter notebook the Wilson instance is "pretty printed" in the form of a table showing the input EFT, basis and scale, as well as a table with the input Wilson coefficient values.

In [3]:
mywilson

Out[3]:

### Wilson coefficients

EFT Basis scale
SMEFT Warsaw 1000.0 GeV

#### Values

Re Im
uG_33 0.000001 0.0

Running down to the top mass scale, look at the induced values of $C_{uG}^{33}$ and $C_{uB}^{33}$

In [11]:
wc = mywilson.match_run(scale=160, eft='SMEFT', basis='Warsaw')
wc['uG_33'], wc['uB_33']

Out[11]:
((1.0228430603379855e-06-1.572304908340795e-19j),
(-1.9807848899202916e-08+3.044024654195142e-21j))

$R_{D^*}$ as computed by flavio induced by $(C_{lq}^{(3)})_{3333}$

In [14]:
import flavio

my_wilson = wilson.Wilson({'lq3_3333': 1e-6},
scale=1e3, eft='SMEFT', basis='Warsaw')

RDs_SM = flavio.sm_prediction('Rtaul(B->D*lnu)')
RDs_NP = flavio.np_prediction('Rtaul(B->D*lnu)', my_wilson)

RDs_NP / RDs_SM

Out[14]:
0.8847548062233653
In [ ]: