Integrand

class madspace.Integrand(self: madspace._madspace_py.Integrand, mapping: madspace._madspace_py.PhaseSpaceMapping, diff_xs: collections.abc.Sequence[madspace._madspace_py.DifferentialCrossSection], adaptive_map: None | madspace._madspace_py.VegasMapping | madspace._madspace_py.Flow = None, discrete_sym: None | madspace._madspace_py.DiscreteSampler | madspace._madspace_py.DiscreteFlow = None, discrete_flavor: None | madspace._madspace_py.DiscreteSampler | madspace._madspace_py.DiscreteFlow = None, pid_options: collections.abc.Sequence[collections.abc.Sequence[SupportsInt]] = [], pdf_grid: None | madspace._madspace_py.PdfGrid | madspace._madspace_py.CustomPdf = None, running_coupling: madspace._madspace_py.RunningCoupling | None = None, energy_scale: madspace._madspace_py.EnergyScale | None = None, prop_chan_weights: madspace._madspace_py.PropagatorChannelWeights | None = None, subchan_weights: madspace._madspace_py.SubchannelWeights | None = None, chan_weight_net: madspace._madspace_py.ChannelWeightNetwork | None = None, first_chan_weight_remap: collections.abc.Sequence[collections.abc.Sequence[SupportsInt]] = [], first_remapped_chan_count: SupportsInt = 0, second_chan_weight_remap: collections.abc.Sequence[SupportsInt] = [], second_remapped_chan_count: SupportsInt = 0, madnis_training: bool = False, drop_cuts_and_rescale: bool = False, partial_weights: bool = False, channel_indices: collections.abc.Sequence[SupportsInt] = [], active_flavors: collections.abc.Sequence[collections.abc.Sequence[SupportsInt]] = [], flavor_remap: collections.abc.Sequence[SupportsInt] = [], flavor_factors: collections.abc.Sequence[SupportsFloat] = [], flavor_mirror: collections.abc.Sequence[bool] = [], flavor_diff_xs_indices: collections.abc.Sequence[SupportsInt] = [], flavor_subproc_indices: collections.abc.Sequence[SupportsInt] = [], flavor_per_subproc_remap: collections.abc.Sequence[SupportsInt] = [], compressed_channel_weight_count: SupportsInt = 50, custom_bias: madspace._madspace_py.CustomFunction | None = None, pdf_grid2: madspace._madspace_py.PdfGrid | None = None)

Bases: FunctionGenerator

The full single-channel integrand, from random numbers to a weighted event.

Chains all the pieces of a leading-order calculation into one FunctionGenerator. It draws the unit-hypercube numbers, optionally reshapes them with an adaptive VegasMapping or Flow, runs the PhaseSpaceMapping, evaluates the DifferentialCrossSection (matrix element, PDFs, scales), applies the Cuts and the multi-channel weights, and returns the event weight with the momenta and the sampled discrete quantities [1]. This is the object that is integrated and unweighted.

Warning:

For internal use within MadGraph. Its interface (in particular the long constructor and the argument / return layout) is not part of the stable public API and may change without notice.

batch is the leading batch dimension.

Arguments

  • batch_size – the number of events to generate.

Returns (the common set; madnis_training selects a different set for the training loss)

  • weight – float, shape (batch,) – the event weight.

  • momenta – float, shape (batch, particle_count, 4) – the momenta.

  • color_index, helicity_index, diagram_index, flavor_index – int, shape (batch,) – the sampled discrete quantities.

  • ren_scale, alpha_qcd, x1, x2, fact_scale1, fact_scale2 – float, shape (batch,) – scales and momentum fractions.

  • random – float, shape (batch, mapping.random_dim()) – the underlying random numbers.

  • bias – float, shape (batch,) – the event bias, present only with a custom bias. weight then includes the bias factor.

References

Parameters:

  • mapping – The phase-space mapping.

  • diff_xs – One differential cross section per active flavour.

  • adaptive_map – Optional continuous adaptive sampler (VegasMapping or Flow).

  • discrete_sym – Optional adaptive sampler for the permutation channel.

  • discrete_flavor – Optional adaptive sampler for the flavour choice.

  • pid_options – Allowed initial-parton flavour combinations.

  • pdf_grid – Optional shared PdfGrid or CustomPdf.

  • running_coupling – Optional shared RunningCoupling.

  • energy_scale – Optional shared EnergyScale.

  • prop_chan_weights – Optional PropagatorChannelWeights.

  • subchan_weights – Optional SubchannelWeights.

  • chan_weight_net – Optional ChannelWeightNetwork.

  • first_chan_weight_remap – Remap applied to the channel weights before the network.

  • first_remapped_chan_count – Channel count after that remap.

  • second_chan_weight_remap – Remap applied after the network.

  • second_remapped_chan_count – Channel count after that remap.

  • madnis_training – If true, return the quantities needed for the MadnisLoss instead of a finished event.

  • drop_cuts_and_rescale – If true, ignore the cut mask and rescale the weight accordingly.

  • partial_weights – If true, emit the per-factor weights for buffered unweighting.

  • channel_indices – Global indices of this integrand’s channels.

  • active_flavors – Flavour indices this integrand covers.

  • flavor_remap – Map from flavour index to diff_xs index.

  • flavor_factors – Per-flavour multiplicative weight factors.

  • flavor_mirror – Per-flavour beam-swap flags.

  • flavor_diff_xs_indices – Per-flavour diff_xs indices.

  • flavor_subproc_indices – Per-flavour sub-process indices.

  • flavor_per_subproc_remap – Per-sub-process flavour remap.

  • compressed_channel_weight_count – Channel-weight entries kept per event.

  • custom_bias – Optional user-supplied event bias of type CustomFunction::bias. Its PDG-id options must match pid_options.

  • pdf_grid2 – PDF of the second beam when it differs from pdf_grid (which is then the first beam’s). Its globals must be registered with the prefix pdf2_prefix. Not allowed with a CustomPdf, which receives the beam index.

active_flavors(self: madspace._madspace_py.Integrand) → list[int]

Flavour indices this integrand covers.

adaptive_map(self: madspace._madspace_py.Integrand) → None | madspace._madspace_py.VegasMapping | madspace._madspace_py.Flow

The continuous adaptive sampler.

chan_weight_net(self: madspace._madspace_py.Integrand) → madspace._madspace_py.ChannelWeightNetwork | None

The channel-weight network, if any.

channel_indices(self: madspace._madspace_py.Integrand) → list[int]

Global indices of this integrand’s channels.

diff_xs(self: madspace._madspace_py.Integrand) → list[madspace._madspace_py.DifferentialCrossSection]

The per-flavour differential cross sections.

discrete_flavor(self: madspace._madspace_py.Integrand) → None | madspace._madspace_py.DiscreteSampler | madspace._madspace_py.DiscreteFlow

The flavour-choice adaptive sampler.

discrete_sym(self: madspace._madspace_py.Integrand) → None | madspace._madspace_py.DiscreteSampler | madspace._madspace_py.DiscreteFlow

The permutation-channel adaptive sampler.

energy_scale(self: madspace._madspace_py.Integrand) → madspace._madspace_py.EnergyScale | None

The shared energy scale, if any.

latent_dims(self: madspace._madspace_py.Integrand) → tuple[list[int], list[bool]]

The continuous latent dimensions and, per dimension, whether it is discrete.

madnis_training(self: madspace._madspace_py.Integrand) → bool

Whether the integrand emits the MadnisLoss training quantities.

mapping(self: madspace._madspace_py.Integrand) → madspace._madspace_py.PhaseSpaceMapping

The phase-space mapping.

matrix_element_inputs = [<MatrixElementInput.momenta_in: 0>, <MatrixElementInput.alpha_s_in: 1>, <MatrixElementInput.flavor_in: 2>, <MatrixElementInput.random_color_in: 3>, <MatrixElementInput.random_helicity_in: 4>, <MatrixElementInput.random_diagram_in: 5>]
matrix_element_outputs = [<MatrixElementOutput.matrix_element_out: 0>, <MatrixElementOutput.diagram_amp2_out: 1>, <MatrixElementOutput.color_index_out: 2>, <MatrixElementOutput.helicity_index_out: 3>, <MatrixElementOutput.diagram_index_out: 4>]
particle_count(self: madspace._madspace_py.Integrand) → int

Total number of particles, incoming and outgoing.

prop_chan_weights(self: madspace._madspace_py.Integrand) → madspace._madspace_py.PropagatorChannelWeights | None

The propagator-based channel weights, if any.

random_dim(self: madspace._madspace_py.Integrand) → int

Number of continuous random numbers the integrand consumes.

vegas_bin_count(self: madspace._madspace_py.Integrand) → int

Bin count of the adaptive VegasMapping, or 0 if none.

vegas_dimension(self: madspace._madspace_py.Integrand) → int

Dimension of the adaptive VegasMapping, or 0 if none.

vegas_grid_name(self: madspace._madspace_py.Integrand) → str | None

Global name of the VEGAS grid, if an adaptive VegasMapping is used.