Input & Output

We use yaml runcards for piping input and output files.

Input

The input is split into two runcard

a theory runcard (compatible with the NNPDF theory database) that defines the physical setup and environment
a operator runcard that defines the numerical setup and the requested operators

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)

ThresholdConfig parameters:

float Q0 reference scale from which to start; Required
float mc charm mass in GeV; Required
float mb bottom mass in GeV; Required
float mt top mass in GeV; Required

InterpolatorDispatcher parameter:

list(float) interpolation_xgrid the interpolation grid; Required
int interpolation_polynomial_degree polynomial degree of the interpolating function; Required
bool interpolation_is_log use logarithmic interpolation? Required
list(float) Q2grid the target grid in \(Q^2\); Required

Output

The Output instance represents the following runcard:

output 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?
`q2_ref`	`float`	starting scale
`pids`	`list(int)`	participating partons listed by their PDG id
`Q2grid`	`dict`	all operators at the requested values of \(Q^2\) represented by the key

The grid elements contains two keys each

operators a dict with all evolution kernel operators where the key indicates which distribution is generated by which other one and the value represents the eko in matrix representation - this can either be the plain list representation or the binary representation (as provided by numpy.ndarray.tobytes())
operator_errors a dict with the integration errors associated to the respective operators following the same conventions as the operator dictionary
each element (EKO) is a rank-4 tensor with the indices ordered in the following way: EKO[pid_out][x_out][pid_in][x_in] where pid_out and x_out refer to the outgoing PDF and pid_in and x_in to the incoming PDF. The ordering of pid_out/pid_in is determined by the pids parameter of the output and the order of x_out/x_in by interpolation_xgrid.

Example Output dump

interpolation_is_log: true
interpolation_polynomial_degree: 4
interpolation_xgrid: [0.0001, 0.001, 0.01, 0.1, 1.0]
pids: [22, -6, ..., -1, 21, 1, ..., 6] # with ... logically continued
q2_ref: 1.0
Q2grid:
  1000.0:
    operators:
      - - - - 0
            - 0
            # more values ...
    operator_errors:
      - - - - 0
            - 0
            # more values ...
  10000.0:
    operators:
      - - - - 0
            - 0
            # more values ...
    operator_errors:
      - - - - 0
            - 0
            # more values ...
  # more q2 values ...

Note that by default PyYAML will put each element of the matrices on a new line - instead here we have (partially) chosen the more compact inline list representation for a better overview.