Usage

Ekomark mimics the same inputs needed to run eko, namely a theory card, an observable card and also the name of a pdf set whenever the external program can be used together with lhapdf.

Both the theory and observable card can be generated automatically from a default: the former with banana, the latter with something similar to generate_observable() provided sandbox.py.

In addition to run ekomark you need to specify the external program you would benchmark against. To do so, you will have to initialize a class of type ekomark.benchmark.runner. To speed up the calculations null PDFs can be skipped setting the attribute skip_pdfs Finally you can decide to display the output in Flavor or in Evolution basis setting rotate_to_evolution_basis

In the following section we describe some available runners which are the most useful example.

The minimal setup of the input cards must contain:

minimal theory input runcard

Name

Type

default

description

PTO

int

[required]

order of perturbation theory: 0 = LO, …

alphas

float

[required]

reference value of the strong coupling \(\alpha_s(\mu_0^2)\) (Note that we have to use \(\alpha_s(\mu_0^2)\) here, instead of \(a_s(\mu_0^2)\) for legacy reasons)

Q0

float

[required]

reference scale from which to start

mc

float

2.0

charm mass in GeV

mb

float

4.5

bottom mass in GeV

mt

float

173.0

top mass in GeV
minimal operator input runcard

Name

Type

description

interpolation_xgrid

list(float)

the interpolation grid

interpolation_polynomial_degree

int

polynomial degree of the interpolating function

interpolation_is_log

bool

use logarithmic interpolation?

mu2grid

dict

all operators at the requested values of \(Q^2\) represented by the key

The output of ekomark will be stored in data/benchmark.db inside a Pandas.DataFrame table. You can then use the ekonavigator app to inspect your database and produce plots.

Available Runners

In benchmarks/runners we provide a list of established benchmarks

  • sandbox.py:

    • it is used to provide the boilerplate needed for a basic run, in order to make a quick run for debugging purpose, but still fully managed and registered by the ekomark machinery and then available in the ekonavigator

  • apfel_bench.py:

    • it is used by the corresponding workflow to run the established benchmarks against APFEL. The complete run of this script will benchmark EKO against all the compatible APFEL features.

    • the necessary python bindings are provided by the APFEL itself

  • pegaus_bench.py:

    • it is used by the corresponding workflow to run the established benchmarks against Pegasus. The complete run of this script will benchmark EKO against all the compatible Pegasus features.

    • the necessary python bindings are provided by us externally.

  • paper_LHA_bench.py:

    • it is used by the corresponding workflow to run the established benchmarks against the LHA papers.

    • There are no external python bindings needed since the LHA data are stored in ekomark/benchmark/external/LHA.yaml.

All of them are examples useful to understand how to use the ekomark package for benchmarking.