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Merge pull request #232 from NNPDF/tlu_ad_v2
Time Like Splitting Functions
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"""Benchmark FFs from LHAPDF""" | ||
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from banana import register | ||
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from eko import interpolation | ||
from ekomark.benchmark.runner import Runner | ||
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register(__file__) | ||
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base_operator = {"ev_op_iterations": 10, "backward_inversion": "exact"} | ||
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base_theory = { | ||
"Qref": 91.1876, | ||
"mc": 1.51, | ||
"mb": 4.92, | ||
"mt": 172.5, | ||
"kcThr": 1.0, | ||
"kbThr": 1.0, | ||
"ktThr": 1.0, | ||
"alphas": 0.118000, | ||
"alphaqed": 0.007496, | ||
"FNS": "ZM-VFNS", | ||
"ModEv": "EXA", | ||
# "ModEv": "TRN", | ||
} | ||
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FF_sets_lo = [ | ||
"NNFF10_PIm_lo", | ||
"NNFF10_PIp_lo", | ||
"NNFF10_PIsum_lo", | ||
"NNFF10_KAm_lo", | ||
"NNFF10_KAp_lo", | ||
"NNFF10_KAsum_lo", | ||
"NNFF10_PRm_lo", | ||
"NNFF10_PRp_lo", | ||
"NNFF10_PRsum_lo", | ||
] | ||
FF_sets_nlo = [ | ||
"NNFF10_PIm_nlo", | ||
"NNFF10_PIp_nlo", | ||
"NNFF10_PIsum_nlo", | ||
"NNFF10_KAm_nlo", | ||
"NNFF10_KAp_nlo", | ||
"NNFF10_KAsum_nlo", | ||
"NNFF10_PRm_nlo", | ||
"NNFF10_PRp_nlo", | ||
"NNFF10_PRsum_nlo", | ||
"MAPFF10NLOPIm", | ||
"MAPFF10NLOPIp", | ||
"MAPFF10NLOPIsum", | ||
"MAPFF10NLOKAm", | ||
"MAPFF10NLOKAp", | ||
"MAPFF10NLOKAsum", | ||
] | ||
FF_sets_nnlo = [ | ||
"NNFF10_PIm_nnlo", | ||
"NNFF10_PIp_nnlo", | ||
"NNFF10_PIsum_nnlo", | ||
"NNFF10_KAm_nnlo", | ||
"NNFF10_KAp_nnlo", | ||
"NNFF10_KAsum_nnlo", | ||
"NNFF10_PRm_nnlo", | ||
"NNFF10_PRp_nnlo", | ||
"NNFF10_PRsum_nnlo", | ||
"MAPFF10NNLOPIm", | ||
"MAPFF10NNLOPIp", | ||
"MAPFF10NNLOPIsum", | ||
"MAPFF10NNLOKAm", | ||
"MAPFF10NNLOKAp", | ||
"MAPFF10NNLOKAsum", | ||
] | ||
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class BenchmarkFF(Runner): | ||
external = "LHAPDF" | ||
rotate_to_evolution_basis = True | ||
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def skip_pdfs(self, _theory): | ||
return [ | ||
-6, | ||
6, | ||
22, | ||
"ph", | ||
"T35", | ||
"V35", | ||
] | ||
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def benchmark_lo(self, ff_index, Q0=10, mugrid=(100,)): | ||
theory_card = { | ||
**base_theory, | ||
"PTO": 0, | ||
"QED": 0, | ||
"Q0": Q0, | ||
"MaxNfPdf": 5, | ||
"MaxNfAs": 5, | ||
} | ||
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operator_card = { | ||
**base_operator, | ||
"mugrid": list(mugrid), | ||
"time_like": True, | ||
"interpolation_xgrid": interpolation.lambertgrid(100, 0.01), | ||
} | ||
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self.run([theory_card], [operator_card], [FF_sets_lo[ff_index]]) | ||
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def benchmark_nlo(self, ff_index, Q0=10, mugrid=(100,)): | ||
theory_card = { | ||
**base_theory, | ||
"PTO": 1, | ||
"QED": 0, | ||
"Q0": Q0, | ||
"MaxNfPdf": 5, | ||
"MaxNfAs": 5, | ||
} | ||
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operator_card = { | ||
**base_operator, | ||
"mugrid": list(mugrid), | ||
"time_like": True, | ||
"interpolation_xgrid": interpolation.lambertgrid(100, 0.01), | ||
} | ||
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self.run([theory_card], [operator_card], [FF_sets_nlo[ff_index]]) | ||
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def benchmark_nnlo(self, ff_index, Q0=10, mugrid=(100,)): | ||
theory_card = { | ||
**base_theory, | ||
"PTO": 2, | ||
"QED": 0, | ||
"Q0": Q0, | ||
"MaxNfPdf": 5, | ||
"MaxNfAs": 5, | ||
} | ||
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operator_card = { | ||
**base_operator, | ||
"mugrid": list(mugrid), | ||
"time_like": True, | ||
"interpolation_xgrid": interpolation.lambertgrid(100, 0.01), | ||
} | ||
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self.run([theory_card], [operator_card], [FF_sets_nnlo[ff_index]]) | ||
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if __name__ == "__main__": | ||
# BenchmarkFF().benchmark_lo(7) | ||
BenchmarkFF().benchmark_nlo(10) | ||
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# BenchmarkFF().benchmark_nnlo(10) |
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@@ -39,6 +39,7 @@ EKO is ... | |
theory/DGLAP | ||
theory/N3LO_ad | ||
theory/Matching | ||
theory/TimeLike | ||
theory/MHOU | ||
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zzz-refs | ||
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Time-Like Evolution | ||
=================== | ||
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Due to confinement in |QCD| we can not observe partons, such as quarks and gluons, | ||
directly in particle collider experiments. | ||
Instead, stable hadrons are detected which originate from parton interactions. | ||
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The fragmentation functions (|FF|) encode the information | ||
on the probability for a hadron carrying a specified momentum fraction to 'fragment' | ||
from a given parton. These functions are non-perturbative and usually require a global |QCD| | ||
analysis of experimental data involving different processes for their reliable | ||
determination. This makes the |FF| similar to |PDF| as both rely | ||
on similar factorization theorems and, thus, on similar |RGE|. | ||
In practice, the relevant Feynman diagrams can indeed be related by a crossing | ||
symmetry which in turn means certain Mandelstam variables become for |FF| | ||
time-like instead of space-like. | ||
The relevant setting in the operator card is thus called ``time_like = True``. | ||
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We implement the time-like |DGLAP| anomalous dimensions up to |NNLO| in :class:`~ekore.anomalous_dimensions.unpolarized.time_like`. | ||
The implementation for the |LO| and |NLO| splitting functions is based on :cite:`Mitov:2006wy, Gluck:1992zx` and the implementation for | ||
the |NNLO| splitting functions is based on :cite:`Mitov:2006ic, Moch:2007tx, Almasy:2011eq`. | ||
Supplying new anomalous dimensions and new matching conditions is the only change required for the eko program (e.g. the | ||
solution strategies are unaffected). | ||
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The time-like evolution has been benchmarked using selected |FF| sets from |lhapdf|. In addition, the splitting functions have been tested | ||
against functions from |MELA|, which is an alternative Mellin Space evolution code. |
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Original file line number | Diff line number | Diff line change |
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@@ -13,6 +13,7 @@ authors = [ | |
"F. Hekhorn <[email protected]>", | ||
"N. Laurenti <[email protected]>", | ||
"G. Magni <[email protected]>", | ||
"T. Sharma <[email protected]>" | ||
] | ||
classifiers = [ | ||
"Programming Language :: Python", | ||
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5 changes: 2 additions & 3 deletions
5
src/ekore/anomalous_dimensions/unpolarized/space_like/as4/gnsm.py
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