- ‣ User library
- ‣ di-lepton
- ‣ Belle Legacy
- ‣ Dilepton production at Belle II
from pymule import *
import re
import tarfile
import fnmatch
from tabulate import tabulate, SEPARATING_LINE
setup(folder='belle-legacy/out.tar.bz2', cachefolder='/tmp/mcmule/')
Populating the interactive namespace from numpy and matplotlib
tf = tarfile.open('belle-legacy/workers.tar.bz2')
def load1(fn):
dat = re.findall(
br"internal avgi, sd: *([\.\dE-]+) +([\.\dE-]+)",
tf.extractfile(fn).read()
)
ans = []
for y,e in dat:
ans.append([float(y), float(e)])
return np.array(ans)
def mkplot(pat):
ans = []
for fn in tf:
if not fnmatch.fnmatch(fn.name, pat):
continue
dat = load1(fn)
dat /= dat[-1,0]
errorbar(np.arange(len(dat)), dat[:,0] - 1, dat[:,1])
Some of the ee2mmF jobs didn't work
fig, axs = plt.subplots(nrows=2, sharex=True, gridspec_kw={'hspace': 0})
sca(axs[0])
title(r"${\tt ee2mmF}$")
mkplot('workers/worker_*ee2mmF_*')
axvline(20, color='black', linewidth=0.4, zorder=1)
sca(axs[1])
mkplot('workers/broken/worker_*ee2mmF_*')
axvline(20, color='black', linewidth=0.4, zorder=1)
xlabel("iteration")
Text(0.5, 0, 'iteration')
fig, axs = plt.subplots(nrows=2, sharex=True, gridspec_kw={'hspace': 0})
sca(axs[0])
title(r"${\tt ee2mmR}$")
mkplot('workers/worker_*ee2mmR_*')
ylim(-.01,.01)
axvline(20, color='black', linewidth=0.4, zorder=1)
sca(axs[1])
title(r"${\tt ee2mmA}$")
mkplot('workers/worker_*ee2mmA_*')
xlabel("iteration")
axvline(20, color='black', linewidth=0.4, zorder=1)
<matplotlib.lines.Line2D at 0x7fa125c6f9d0>
fig, axs = plt.subplots(nrows=3, sharex=True, gridspec_kw={'hspace': 0})
sca(axs[0])
mkplot('workers/worker_*eeZmmFX_*')
title(r"${\tt eeZmmFX}$")
sca(axs[1])
mkplot('workers/worker_*eeZmmRX_*')
ylim(-.1,.1)
title(r"${\tt eeZmmRX}$")
sca(axs[2])
mkplot('workers/worker_*eeZmmAX_*')
title(r"${\tt eeZmmAX}$")
xlabel("iteration")
Text(0.5, 0, 'iteration')
NLO QED works
setup(obs='30')
mergefkswithplot([[sigma('ee2mmF')], [sigma('ee2mmR')]]);
NLO EW works
mergefkswithplot([[sigma('eeZmmFX')], [sigma('eeZmmRX')]]);
NNLO QED
mergefkswithplot([
[sigma('ee2mmFFEEEE')],
[sigma('ee2mmRFEEEE')],
[sigma('ee2mmRREEEE')]
])
(<Figure size 432x288 with 2 Axes>,
{'time': 13255004.138114002,
'chi2a': [52.17921315510833,
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'value': array([-2.45456617e-07, 4.43077516e-11]),
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[ inf, 0.00000000e+00, 0.00000000e+00]]),
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[ 1.02500000e+02, -6.34737349e-10, 3.48979396e-10],
[ 1.03500000e+02, -1.16908911e-09, 3.53489057e-10],
[ 1.04500000e+02, -1.18085165e-10, 3.08547025e-10],
[ 1.05500000e+02, -9.73006433e-10, 3.50322647e-10],
[ 1.06500000e+02, -6.03462630e-10, 3.60124258e-10],
[ 1.07500000e+02, -7.32215605e-10, 3.24562323e-10],
[ 1.08500000e+02, -4.30586570e-10, 3.64458775e-10],
[ 1.09500000e+02, -6.93429493e-10, 3.50840504e-10],
[ 1.10500000e+02, 2.38362095e-10, 3.43486869e-10],
[ 1.11500000e+02, -7.05029653e-10, 3.33521124e-10],
[ 1.12500000e+02, -7.66647763e-10, 3.29492748e-10],
[ 1.13500000e+02, -2.98753039e-11, 3.52444915e-10],
[ 1.14500000e+02, -6.14416955e-10, 3.41402281e-10],
[ 1.15500000e+02, -9.63811038e-10, 3.11900338e-10],
[ 1.16500000e+02, 1.85387472e-10, 3.19653682e-10],
[ 1.17500000e+02, -7.61605122e-11, 3.32745996e-10],
[ 1.18500000e+02, -5.64093208e-10, 3.29865290e-10],
[ 1.19500000e+02, -4.63503041e-10, 3.51359541e-10],
[ 1.20500000e+02, -8.21621380e-10, 3.24684192e-10],
[ 1.21500000e+02, -4.85907479e-11, 3.14822094e-10],
[ 1.22500000e+02, -9.12304052e-10, 3.48568032e-10],
[ 1.23500000e+02, -3.44542180e-10, 3.58478670e-10],
[ 1.24500000e+02, 8.65752248e-11, 3.58563173e-10],
[ 1.25500000e+02, -7.47984255e-10, 3.38982274e-10],
[ 1.26500000e+02, -5.59731930e-10, 3.13403992e-10],
[ 1.27500000e+02, -4.43829043e-10, 3.28222336e-10],
[ 1.28500000e+02, -9.33801287e-10, 3.53748250e-10],
[ 1.29500000e+02, -1.85733565e-10, 3.16571967e-10],
[ 1.30500000e+02, -9.27746640e-10, 2.92001277e-10],
[ 1.31500000e+02, -7.76373851e-10, 3.12614830e-10],
[ 1.32500000e+02, -4.76621806e-10, 2.93273195e-10],
[ 1.33500000e+02, -7.13006899e-10, 3.21833532e-10],
[ 1.34500000e+02, -7.00689259e-10, 3.03072209e-10],
[ 1.35500000e+02, -7.54081068e-10, 2.79051420e-10],
[ 1.36500000e+02, -9.53295441e-10, 2.88452795e-10],
[ 1.37500000e+02, -1.49949333e-09, 3.14126109e-10],
[ 1.38500000e+02, -4.86340709e-10, 2.78199256e-10],
[ 1.39500000e+02, -9.70240685e-10, 2.77229907e-10],
[ 1.40500000e+02, -1.20057799e-09, 2.50499834e-10],
[ 1.41500000e+02, -1.48271680e-09, 2.41003487e-10],
[ 1.42500000e+02, -1.38429223e-09, 2.68982724e-10],
[ 1.43500000e+02, -9.95710518e-10, 3.30015137e-10],
[ 1.44500000e+02, -1.70833051e-09, 2.67890260e-10],
[ 1.45500000e+02, -1.02354498e-09, 2.92001375e-10],
[ 1.46500000e+02, -2.28658643e-09, 2.87703970e-10],
[ 1.47500000e+02, -2.06667323e-09, 2.59731732e-10],
[ 1.48500000e+02, -1.58714488e-09, 2.51017525e-10],
[ 1.49500000e+02, -2.13677167e-09, 2.28560706e-10],
[ 1.50500000e+02, -1.63177946e-09, 2.04332413e-10],
[ 1.51500000e+02, -2.61655940e-09, 2.04256188e-10],
[ 1.52500000e+02, -1.94702801e-09, 1.95313088e-10],
[ 1.53500000e+02, -2.29505164e-09, 1.90750989e-10],
[ 1.54500000e+02, -2.94276318e-09, 1.97411964e-10],
[ 1.55500000e+02, -2.47721349e-09, 2.08183018e-10],
[ 1.56500000e+02, -2.66982132e-09, 2.12858527e-10],
[ 1.57500000e+02, -2.75577644e-09, 2.18694162e-10],
[ 1.58500000e+02, -2.89625231e-09, 1.73244611e-10],
[ 1.59500000e+02, -2.90834119e-09, 1.75096876e-10],
[ 1.60500000e+02, -3.13341739e-09, 1.82364004e-10],
[ 1.61500000e+02, -2.98754864e-09, 1.63509704e-10],
[ 1.62500000e+02, -3.21742044e-09, 1.42794803e-10],
[ 1.63500000e+02, -3.44902734e-09, 1.31640150e-10],
[ 1.64500000e+02, -3.15408274e-09, 1.31839255e-10],
[ 1.65500000e+02, -3.19267345e-09, 1.23751572e-10],
[ 1.66500000e+02, -3.31689337e-09, 1.11648872e-10],
[ 1.67500000e+02, -3.45922197e-09, 1.04239437e-10],
[ 1.68500000e+02, -3.06842146e-09, 1.00333436e-10],
[ 1.69500000e+02, -3.27229799e-09, 9.62354247e-11],
[ 1.70500000e+02, -2.91697268e-09, 8.30106895e-11],
[ 1.71500000e+02, -2.71885587e-09, 7.64554969e-11],
[ 1.72500000e+02, -2.66800591e-09, 6.70401107e-11],
[ 1.73500000e+02, -2.37701059e-09, 5.80134843e-11],
[ 1.74500000e+02, -1.99837398e-09, 5.20662844e-11],
[ 1.75500000e+02, -1.90164439e-09, 4.39416358e-11],
[ 1.76500000e+02, -1.41962950e-09, 3.38298832e-11],
[ 1.77500000e+02, -1.02466714e-09, 2.71611121e-11],
[ 1.78500000e+02, -6.33829843e-10, 1.88314027e-11],
[ 1.79500000e+02, -1.95386981e-10, 8.78807788e-12],
[ 1.80500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.81500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.82500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.83500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.84500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.85500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.86500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.87500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.88500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.89500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.90500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.91500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.92500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.93500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.94500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.95500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.96500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.97500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.98500000e+02, 0.00000000e+00, 0.00000000e+00],
[ 1.99500000e+02, 0.00000000e+00, 0.00000000e+00],
[ inf, 0.00000000e+00, 0.00000000e+00]])})
setup(obs='31')
def check_distr(dic):
r = [
abs(dic['value'][0] / integratehistogram(dic[i]) - 1)
for i in [
'th3CMS', 'th4CMS', 'mttNC'
]
]
if sum(r) > 1e-2:
print(r)
return dic
def load_data_qed(obs):
setup(obs='3' + obs)
setup(sanitycheck=lambda d: d['iteration'] > 1)
lo = check_distr(scaleset(mergefks(
sigma('ee2mm0')
), 1e6*conv*alpha**2))
nlo = check_distr(scaleset(mergefks(
sigma('ee2mmF'), sigma('ee2mmR'),
anyxi=sigma('ee2mmA')
), 1e6*conv*alpha**3))
nnloNoVP = mergefks(
sigma('ee2mmFFEEEE'), sigma('ee2mmRFEEEE'), sigma('ee2mmRREEEE')
)
nnloVP = mergefks(
sigma('ee2mmAFEE'), sigma('ee2mmAREE'),
anyxiAA=sigma('ee2mmAA'), anyxiNF=sigma('ee2mmNFEE')
)
nnlo = check_distr(scaleset(addsets([nnloVP, nnloNoVP]), 1e6*conv*alpha**4))
return lo, nlo, nnlo
def load_data_ew(obs):
setup(obs='3' + obs)
loZ = check_distr(scaleset(mergefks(sigma('eeZmm0')), 1e6*conv*alpha**2))
nloZ = check_distr(scaleset(mergefks(
sigma('eeZmmFX'), sigma('eeZmmRX'),
anyxi=sigma('eeZmmAX')
), 1e6*conv*alpha**3))
return loZ, nloZ
def load_data_ew_0X(obs):
setup(obs='3' + obs)
loZX = check_distr(scaleset(mergefks(sigma('eeZmm0X')), 1e6*conv*alpha**2))
return loZX
Let us load data for all polarisation configurations. The numbering is
select case(pola)
case(0)
pol1 = 0.
pol2 = 0.
case(1) ! -+ in the paper
pol1 = boost_back(q1, +(/ 0., 0., .7, 0./))
pol2 = boost_back(q2, +(/ 0., 0., .7, 0./))
case(2) ! ++ in the paper
pol1 = boost_back(q1, +(/ 0., 0., .7, 0./))
pol2 = boost_back(q2, -(/ 0., 0., .7, 0./))
case(3) ! -- in the paper
pol1 = boost_back(q1, -(/ 0., 0., .7, 0./))
pol2 = boost_back(q2, +(/ 0., 0., .7, 0./))
case(4) ! +- in the paper
pol1 = boost_back(q1, -(/ 0., 0., .7, 0./))
pol2 = boost_back(q2, -(/ 0., 0., .7, 0./))
end select
lo0, nlo0, nnlo0 = load_data_qed('0')
lo1, nlo1, nnlo1 = load_data_qed('1')
lo2, nlo2, nnlo2 = load_data_qed('2')
loZ0X = load_data_ew_0X('0')
loZ0, nloZ0 = load_data_ew('0')
loZ1X = load_data_ew_0X('1')
loZ1, nloZ1 = load_data_ew('1')
loZ2X = load_data_ew_0X('2')
loZ2, nloZ2 = load_data_ew('2')
loZ3X = load_data_ew_0X('3')
loZ3, nloZ3 = load_data_ew('3')
loZ4X = load_data_ew_0X('4')
loZ4, nloZ4 = load_data_ew('4')
NLO0 = addsets([lo0, nlo0])
QED0 = addsets([lo0, nlo0, nnlo0])
Z0 = addsets([loZ0, nloZ0])
ALL0 = addsets([QED0, Z0])
NLO1 = addsets([lo1, nlo1])
QED1 = addsets([lo1, nlo1, nnlo1])
Z1 = addsets([loZ1, nloZ1])
ALL1 = addsets([QED1, Z1])
NLO2 = addsets([lo2, nlo2])
QED2 = addsets([lo2, nlo2, nnlo2])
Z2 = addsets([loZ2, nloZ2])
ALL2 = addsets([QED2, Z2])
Z3 = addsets([loZ3, nloZ3])
ALL3 = addsets([QED2, Z3])
Z4 = addsets([loZ4, nloZ4])
ALL4 = addsets([QED1, Z4])
We define the asymmetry as \begin{align} A_{\rm FB} = \frac{ \int_{+1}^{ 0}\D(\cos\theta_{\tau^-}^*)\tfrac{\D\sigma}{\D\cos\theta_{\tau^-}^*} -\int_{ 0}^{-1}\D(\cos\theta_{\tau^-}^*)\tfrac{\D\sigma}{\D\cos\theta_{\tau^-}^*} }{ \int_{+1}^{ 0}\D(\cos\theta_{\tau^-}^*)\tfrac{\D\sigma}{\D\cos\theta_{\tau^-}^*} +\int_{ 0}^{-1}\D(\cos\theta_{\tau^-}^*)\tfrac{\D\sigma}{\D\cos\theta_{\tau^-}^*} } \end{align}
def asym(dic, ang='th3CMS'):
spec = mergebins(dic[ang],90)
diff = plusnumbers(-spec[1,1:],spec[2,1:])
tot = plusnumbers(spec[1,1:],+spec[2,1:])
return dividenumbers(diff,tot)
print(tabulate([
[
"0", "\sigma/pb",
printnumber(lo0['value']),
printnumber(nlo0['value']),
printnumber(nnlo0['value']),
printnumber(Z0['value']),
""
], [
"0", "dK / %",
"",
printnumber(100*dividenumbers(nlo0['value'], lo0['value'])),
printnumber(100*dividenumbers(nnlo0['value'], NLO0['value'])),
printnumber(100*dividenumbers(Z0['value'], QED0['value'])),
""
], [
"0", "AFB",
printnumber(asym(lo0)),
printnumber(asym(NLO0)),
"n/a",
printnumber(asym(ALL0)),
""
],
SEPARATING_LINE,
[
"-+", "\sigma/pb",
printnumber(lo1['value']),
printnumber(nlo1['value']),
printnumber(nnlo1['value']),
printnumber(Z1['value']),
printnumber(Z4['value'])
], [
"-+", "dK / %",
"",
printnumber(100*dividenumbers(nlo1['value'], lo1['value'])),
printnumber(100*dividenumbers(nnlo1['value'], NLO1['value'])),
printnumber(100*dividenumbers(Z1['value'], QED1['value'])),
printnumber(100*dividenumbers(Z4['value'], QED1['value']))
], [
"-+", "AFB",
printnumber(asym(lo1)),
printnumber(asym(NLO1)),
"n/a",
printnumber(asym(ALL1)),
printnumber(asym(ALL4))
],
SEPARATING_LINE,
[
"++", "\sigma/pb",
printnumber(lo2['value']),
printnumber(nlo2['value']),
printnumber(nnlo2['value']),
printnumber(Z2['value']),
printnumber(Z3['value'])
], [
"++", "dK / %",
"",
printnumber(100*dividenumbers(nlo2['value'], lo2['value'])),
printnumber(100*dividenumbers(nnlo2['value'], NLO2['value'])),
printnumber(100*dividenumbers(Z2['value'], QED2['value'])),
printnumber(100*dividenumbers(Z3['value'], QED2['value']))
], [
"++", "AFB",
printnumber(asym(lo2)),
printnumber(asym(NLO2)),
"n/a",
printnumber(asym(ALL2)),
printnumber(asym(ALL3))
]
], headers=[
"P", "",
"\sigma^(0)_QED", "\sigma^(1)_QED", "\sigma^(2)_QED",
"\sigma_EW",
"\sigma_EW"
]))
def forwardpart(dic,ang='th3CMS'):
spec = mergebins(dic[ang],90)
return 90*spec[2,1:]
def backwardpart(dic,ang='th3CMS'):
spec = mergebins(dic[ang],90)
return 90*spec[1,1:]
print(tabulate([
[
"0", "\sigma^f/pb",
printnumber(forwardpart(loZ0)),
printnumber(forwardpart(nloZ0)),
printnumber(forwardpart(loZ0X))
],[
"0", "\sigma^b/pb",
printnumber(backwardpart(loZ0)),
printnumber(backwardpart(nloZ0)),
printnumber(backwardpart(loZ0X))
],[
"0", "\sigma/pb",
printnumber(loZ0['value']),
printnumber(nloZ0['value']),
printnumber(loZ0X['value'])
],
SEPARATING_LINE,
[
"-+", "\sigma^f/pb",
printnumber(forwardpart(loZ1)),
printnumber(forwardpart(nloZ1)),
printnumber(forwardpart(loZ1X))
],[
"-+", "\sigma^b/pb",
printnumber(backwardpart(loZ1)),
printnumber(backwardpart(nloZ1)),
printnumber(backwardpart(loZ1X))
],[
"-+", "\sigma/pb",
printnumber(loZ1['value']),
printnumber(nloZ1['value']),
printnumber(loZ1X['value'])
],
SEPARATING_LINE,
[
"+-", "\sigma^f/pb",
printnumber(forwardpart(loZ4)),
printnumber(forwardpart(nloZ4)),
printnumber(forwardpart(loZ4X))
],[
"+-", "\sigma^b/pb",
printnumber(backwardpart(loZ4)),
printnumber(backwardpart(nloZ4)),
printnumber(backwardpart(loZ4X))
],[
"+-", "\sigma/pb",
printnumber(loZ4['value']),
printnumber(nloZ4['value']),
printnumber(loZ4X['value'])
],
SEPARATING_LINE,
[
"++", "\sigma^f/pb",
printnumber(forwardpart(loZ2)),
printnumber(forwardpart(nloZ2)),
printnumber(forwardpart(loZ2X))
],[
"++", "\sigma^b/pb",
printnumber(backwardpart(loZ2)),
printnumber(backwardpart(nloZ2)),
printnumber(forwardpart(loZ2X))
],[
"++", "\sigma/pb",
printnumber(loZ2['value']),
printnumber(nloZ2['value']),
printnumber(loZ2X['value'])
],
], headers=[
"P", "",
"\sigma^(0)_EW", "\sigma^(1)_EW","\sigma^(0)_EW (1/MZ^2)"
]))
P \sigma^(0)_QED \sigma^(1)_QED \sigma^(2)_QED \sigma_EW \sigma_EW --- --------- ---------------- ---------------- ---------------- ------------ ------------ 0 \sigma/pb 771.640442(2) 139.2856(2) 4.1583(1) 0.154896(2) 0 dK / % 18.05058(3) 0.45649(1) 0.0169270(2) 0 AFB 0.000000(4) 0.01158(1) n/a 0.00583(1) --- --------- ---------------- ---------------- ---------------- ------------ ------------ -+ \sigma/pb 1149.744256(3) 205.6484(3) 5.783(1) 0.105334(4) 0.486073(4) -+ dK / % 17.88645(3) 0.4267(1) 0.0077385(3) 0.0357098(3) -+ AFB 0.000000(4) 0.01160(1) n/a 0.00574(2) 0.00591(2) --- --------- ---------------- ---------------- ---------------- ------------ ------------ ++ \sigma/pb 393.536644(1) 72.9224(1) 2.538(1) 0.014082(2) 0.014086(3) ++ dK / % 18.53001(3) 0.5441(3) 0.0030027(5) 0.0030033(7) ++ AFB 0.000000(4) 0.01153(2) n/a 0.00585(2) 0.00585(2) P \sigma^(0)_EW \sigma^(1)_EW \sigma^(0)_EW (1/MZ^2) --- ----------- --------------- --------------- ------------------------ 0 \sigma^f/pb -2.54179(2) 0.01505(4) -2.51552(2) 0 \sigma^b/pb 2.48931(2) 0.19236(6) 2.44825(2) 0 \sigma/pb -0.05247782(7) 0.207374(2) -0.06727043(7) --- ----------- --------------- --------------- ------------------------ -+ \sigma^f/pb -4.62024(4) 0.7186(1) -4.57355(4) -+ \sigma^b/pb 3.54342(3) 0.4635(1) 3.48405(3) -+ \sigma/pb -1.0768190(1) 1.182153(4) -1.0895035(1) --- ----------- --------------- --------------- ------------------------ +- \sigma^f/pb -2.95430(2) -0.63717(8) -2.92271(2) +- \sigma^b/pb 3.87475(3) 0.2029(1) 3.81175(3) +- \sigma/pb 0.92043506(9) -0.434362(4) 0.88903764(9) --- ----------- --------------- --------------- ------------------------ ++ \sigma^f/pb -1.29631(1) -0.01080(5) -1.28291(1) ++ \sigma^b/pb 1.26955(1) 0.05162(5) -1.28291(1) ++ \sigma/pb -0.02676369(4) 0.040846(2) -0.03430792(3)
def mklegend(cols, style, labs, ctx, **kwargs):
return ctx.legend(
[matplotlib.lines.Line2D([0], [0], color=c, linestyle=s) for c,s in zip(cols, style)],
labs,
**kwargs
)
def mklab(lab, *args, **kwargs):
if isinstance(lab, str):
lab = [lab]
gca().add_artist(
mklegend(
['black']*len(lab), ['-']*len(lab),
lab,
gca(),
handlelength=0, *args, **kwargs
)
)
def mymerge(dat, n, m=1):
return np.concatenate((
mergebins(dat[:70], m),
mergebins(dat[70:151], n)
))
def myplot(style, dat, *args, cut=True, **kwargs):
if style == plot:
# plot(dat[18:52,0], dat[18:52,1], *args, **kwargs)
# plot(dat[53:151,0], dat[53:151,1], *args, **kwargs)
plot(dat[18:151,0], dat[18:151,1], *args, **kwargs)
elif style == errorband:
# errorband(dat[18:52], *args, **kwargs)
# errorband(dat[53:151], *args, **kwargs)
errorband(dat[18:151], *args, **kwargs)
fig, axs = plt.subplots(
2, sharex=True, gridspec_kw={'hspace': 0}
)
sca(axs[0])
errorband(lo0['th3'][18:151], col='C2')
myplot(plot, nlo0['th3'], 'C0')
myplot(plot, nlo0['th4'], 'C0--')
sca(axs[1])
myplot(errorband, mymerge(nnlo0['th3'], 3), col='C3')
myplot(plot, Z0['th3'], 'C1')
myplot(plot, Z0['th4'], 'C1--')
axhline(0, color='black', linewidth=0.4, zorder=1)
ylim(-0.08,0.08)
fig.text(0.04, 0.5,
r'$\D\sigma/\D \theta_{\tau^\pm}\ /\ {\rm pb}$',
va='center', rotation='vertical'
)
xlabel(r"$\theta_{\tau^\pm}\,/\,{\rm deg}$")
mulify(fig)
mklegend(
['C2', 'C0', 'C3', 'C1', 'black', 'black'],
['-', '-', '-', '-', '-', '--'],
[
r'$\sigma^{(0)}_{\rm QED}$', r'$\sigma^{(1)}_{\rm QED}$', r'$\sigma^{(2)}_{\rm QED}$',
r'$\sigma_{\rm EW}$',
r'$\tau^-$', r'$\tau^+$'
],
fig,
ncol=6,
loc=9,
)
fig.savefig('plots/th.pdf')
lo0['th3CMS'][0,0] = 0.
nlo0['th3CMS'][0,0] = 0.
nlo0['th4CMS'][0,0] = 0.
fig, axs = plt.subplots(
2, sharex=True, gridspec_kw={'hspace': 0}
)
sca(axs[0])
errorband(lo0['th3CMS'][0:182], col='C2')
plot(nlo0['th3CMS'][0:182,0], nlo0['th3CMS'][0:182,1], 'C0')
plot(nlo0['th4CMS'][0:182,0], nlo0['th4CMS'][0:182,1], 'C0--')
ylim(0.07,7.25)
yscale('log')
sca(axs[1])
errorband(lo0['th3CMS'][0:182], col='C2')
plot(nlo0['th3CMS'][0:182,0], nlo0['th3CMS'][0:182,1], 'C0')
plot(nlo0['th4CMS'][0:182,0], nlo0['th4CMS'][0:182,1], 'C0--')
errorband(mergebins(nnlo0['th3CMS'][0:182], 3), col='C3')
plot(Z0['th3CMS'][0:181,0], Z0['th3CMS'][0:181,1], 'C1')
plot(Z0['th4CMS'][0:181,0], Z0['th4CMS'][0:181,1], 'C1--')
ylim(-0.05,0.06)
axhline(0, color='black', linewidth=0.4, zorder=1)
fig.text(0.02, 0.5,
r'$\D\sigma/\D \theta_{\tau^\pm}^*\ /\ {\rm pb}$',
va='center', rotation='vertical'
)
xlabel(r"$\theta_{\tau^\pm}^*\,/\,{\rm deg}$")
mulify(fig)
h=mklegend(
['C2', 'C0', 'C3', 'C1', 'black', 'black'],
['-', '-', '-', '-', '-', '--'],
[
r'$\sigma^{(0)}_{\rm QED}$', r'$\sigma^{(1)}_{\rm QED}$', r'$\sigma^{(2)}_{\rm QED}$',
r'$\sigma_{\rm EW}$',
r'$\tau^-$', r'$\tau^+$'
],
ctx=fig,
ncol=6,
loc=9,
)
fig.savefig('plots/thCMS.pdf')
Let us consider the ratio between the polarised and unpolarised distributions for the angular distribution of the $\tau^-$, both in the lab frame with cuts ($\theta_{\tau^-}$) and the CMS frame without ($\theta_{\tau^-}^*$) \begin{align} \mathcal{R}^{(*)}(\pm+) = \frac{\D\sigma(\pm+)/\D\theta_{\tau^-}^{(*)}} {\D\sigma( 00 )/\D\theta_{\tau^-}^{(*)}} -\frac{ \sigma(\pm+)}{ \sigma(00)} \end{align} We include the normalisation factor $\sigma(\pm+)/\sigma(00)$ to account for the fact that the integrated cross section is vastly different.
def mkplot(dp, d0, k, n=5, **kwargs):
pp = mergebins(dp[k], n) ; p0 = mergebins(d0[k], n)
s = integratehistogram(pp) / integratehistogram(p0)
mask = abs(pp[:,1]) > 0
errorband([1,100,100]*(
divideplots(pp, p0)[mask] - [0,s,0]
), **kwargs)
fig, axs = plt.subplots(
2, sharex=True, gridspec_kw={'hspace': 0}
)
sca(axs[0])
mkplot(NLO1, NLO0, 'th3CMS')
mkplot(NLO2, NLO0, 'th3CMS')
mklab(
r'$\sigma^{(0)}_{\rm QED} + '
r' \sigma^{(1)}_{\rm QED}$',
loc='upper right'
)
ylim(-0.9, 0.9)
axhline(0, color='black', linewidth=0.4, zorder=1)
sca(axs[1])
mkplot(ALL1, ALL0, 'th3CMS')
mkplot(ALL2, ALL0, 'th3CMS')
mklab(
r'$\sigma^{(0)}_{\rm QED} + '
r' \sigma^{(1)}_{\rm QED} + '
r' \sigma^{(2)}_{\rm QED} + '
r' \sigma_{\rm EW}$',
loc='upper right'
)
ylim(-0.9, 0.9)
axhline(0, color='black', linewidth=0.4, zorder=1)
fig.legend(['$P=-+$', '$P=++$'], ncol=2, loc=9)
fig.text(0.03, 0.5,
r"$\mathcal{R}(\pm+) / \%$",
va='center', rotation='vertical'
)
xlabel(r"$\theta_{\tau^-}^*\,/\,{\rm deg}$")
mulify(fig)
fig.savefig('plots/polCMS.pdf')
def mkplot(dp, d0, k, n=10, m=5, **kwargs):
pp = mymerge(dp[k], n, m) ; p0 = mymerge(d0[k], n, m)
s = integratehistogram(pp) / integratehistogram(p0)
mask = abs(pp[:,1]) > 0
errorband([1,100,100]*(
divideplots(pp, p0)[mask] - [0,s,0]
), **kwargs)
fig, axs = plt.subplots(
2, sharex=True, gridspec_kw={
'hspace': 0,
'height_ratios':[1,0.4]}
)
sca(axs[0])
mkplot(ALL1, ALL0, 'th3')
mkplot(ALL2, ALL0, 'th3')
ylim(-5, 5)
axhspan(-0.25,0.25, color=colours.alpha_composite('white', 'black', 0.1), lw=0)
axhline(0, color='black', linewidth=0.4, zorder=1)
mklab(
r'$\sigma^{(0)}_{\rm QED} + '
r' \sigma^{(1)}_{\rm QED} + '
r' \sigma^{(2)}_{\rm QED} + '
r' \sigma_{\rm EW}$',
loc='upper right'
)
sca(axs[1])
mkplot(ALL1, ALL0, 'th3')
mkplot(ALL2, ALL0, 'th3')
ylim(-0.25, 0.25)
axhline(0, color='black', linewidth=0.4, zorder=1)
fig.legend(['$P=-+$', '$P=++$'], ncol=2, loc=9)
fig.text(0.03, 0.5,
r"$\mathcal{R}(\pm+) / \%$",
va='center', rotation='vertical'
)
xlabel(r"$\theta_{\tau^-}\,/\,{\rm deg}$")
mulify(fig)
fig.savefig('plots/pol.pdf')
