def doit(self):
theory_updates = {
"PTO": 2,
"ModEv": "perturbative-exact",
# "XIR": 0.5,
# "fact_to_ren_scale_ratio": 2.0,
"Q0": 2,
"Qref": 2.0,
"alphas": 0.35,
"kbThr": 2.71,
**vfns,
}
# t0 = theory_updates.copy()
# t0["PTO"] = 0
self.skip_pdfs = lambda _theory: [
22,
-6,
6,
"ph",
"V35",
"V24",
"V15",
"V8",
"T35",
]
self.run(
[theory_updates], # , t0],
operators.build(self.generate_operators()),
["ToyLH"],
)
def benchmark_sv(self, pto):
"""Scale Variation"""
th = self.vfns_theory.copy()
th.update({"PTO": [pto], "XIR": [0.7071067811865475, 1.4142135623730951]})
self.run(
cartesian_product(th), operators.build(operators.apfel_config), ["ToyLH"]
)
def benchmark_plain(self, pto):
"""Plain configuration"""
th = self.vfns_theory.copy()
th.update({"PTO": [pto]})
self.run(
cartesian_product(th), operators.build(operators.apfel_config), ["ToyLH"]
)
def benchmark_sv(self, pto):
"""Scale Variation"""
th = self.zm_theory.copy()
th.update({
"PTO": [pto],
"fact_to_ren_scale_ratio": [np.sqrt(0.5),
np.sqrt(2.0)],
})
self.run(cartesian_product(th),
operators.build(operators.pegasus_config), ["ToyLH"])
def benchmark_kthr(self, pto):
"""Threshold scale different from heavy quark mass"""
th = self.vfns_theory.copy()
th.update(
{
"PTO": [pto],
"kcThr": [1.23],
"kbThr": [1.45],
}
)
self.run(
cartesian_product(th), operators.build(operators.apfel_config), ["ToyLH"]
)
def benchmark_sv(self, pto):
"""Scale Variation"""
ts = []
th = self.ffns_theory.copy()
th.update(
{
"PTO": [pto],
"XIR": [np.sqrt(0.5)],
"fact_to_ren_scale_ratio": [np.sqrt(2.0)],
}
)
ts.extend(cartesian_product(th))
th = self.ffns_theory.copy()
th.update(
{
"PTO": [pto],
"XIR": [np.sqrt(2.0)],
"fact_to_ren_scale_ratio": [np.sqrt(0.5)],
}
)
ts.extend(cartesian_product(th))
self.run(ts, operators.build(operators.apfel_config), ["ToyLH"])
