Constraint Format

Every entry of eos.Constraints is described on disk by a YAML map whose type key selects one of the concrete field schemas below. The full list of constraints currently shipped with EOS is documented separately on the List of Constraints page.

Amoroso

key

kind

required

description

observable

scalar

yes

The qualified name of the observable this constraint applies to.

kinematics

map

yes

The kinematic variables at which the observable is evaluated.

options

map

yes

The options with which the observable is evaluated.

physical-limit

scalar

yes

The distribution’s physical limit.

theta

scalar

yes

The distribution’s scale parameter theta.

alpha

scalar

yes

The distribution’s shape parameter alpha.

beta

scalar

yes

The distribution’s shape parameter beta.

references

sequence

no

The list of reference names this constraint originates from.

Example:

B^0_s->mu^+mu^-::BR@CMS:2013B:
  type: Amoroso
  observable: B_q->ll::BR@Untagged
  kinematics: {}
  options: {l: mu, q: s}
  physical-limit: 0
  theta: 1.98596335e-09
  alpha: 2.7971996
  beta: 2.02188458
  references:
    []

Gaussian

key

kind

required

description

observable

scalar

yes

The qualified name of the observable this constraint applies to.

kinematics

map

yes

The kinematic variables at which the observable is evaluated.

options

map

yes

The options with which the observable is evaluated.

mean

scalar

yes

The measurement’s central value.

sigma-stat

map

yes

The measurement’s statistical uncertainty, as ‘hi’/’lo’ one-sigma values.

sigma-sys

map

yes

The measurement’s systematic uncertainty, as ‘hi’/’lo’ one-sigma values.

references

sequence

no

The list of reference names this constraint originates from.

Example:

B^0_s->mu^+mu^-::BR@CMS:2016A:
  type: Gaussian
  observable: B_q->ll::BR@Untagged
  kinematics: {}
  options: {l: mu, q: s}
  mean: 3e-09
  sigma-stat: {hi: 1e-09, lo: 9e-10}
  sigma-sys: {hi: 0, lo: 0}
  references:
    []

LogGamma

key

kind

required

description

observable

scalar

yes

The qualified name of the observable this constraint applies to.

kinematics

map

yes

The kinematic variables at which the observable is evaluated.

options

map

yes

The options with which the observable is evaluated.

mode

scalar

yes

The distribution’s mode.

sigma

map

yes

The distribution’s width, as ‘hi’/’lo’ one-sigma values.

alpha

scalar

yes

The distribution’s shape parameter alpha.

lambda

scalar

yes

The distribution’s shape parameter lambda.

references

sequence

no

The list of reference names this constraint originates from.

Example:

# No constraint of type LogGamma currently ships with EOS; the following illustrates the format.
Example::Constraint@LogGamma:2026A:
    type: LogGamma
    observable: mass::b(MSbar)
    kinematics: {}
    options: {}
    mode: 0.53
    sigma: { hi: 0.1, lo: 0.19 }
    alpha: 0.383056
    lambda: 0.0687907

Mixture

key

kind

required

description

observables

sequence

yes

The qualified names of the observables this constraint applies to.

kinematics

sequence

yes

One kinematics map per observable, in the same order as ‘observables’.

options

sequence

yes

One options map per observable, in the same order as ‘observables’.

components

sequence

yes

The mixture’s components, each a map with keys ‘means’ and ‘covariance’.

weights

sequence

yes

The mixture weight of each component, in the same order as ‘components’.

test statistics

map

yes

The test-statistic map, with keys ‘sigma’ and ‘densities’.

dof

scalar

no

The number of degrees of freedom; defaults to the dimension of the first component.

references

sequence

no

The list of reference names this constraint originates from.

Example:

ublnu::P(WET)@LMNRvD:2023A:
  type: Mixture
  observables:
    - ubenue::Re{cVL}
    - ubenue::Re{cVR}
    - ubenue::Re{cSL}
    - ubenue::Re{cSR}
    - ubenue::Re{cT}
  kinematics:
    - {}
    - {}
    - {}
    - {}
    - {}
  options:
    - {}
    - {}
    - {}
    - {}
    - {}
  components:
    - means: [0.239210427, 0.745753385, 0.0134320629, 0.00712165087, 0.07476569]
      covariance:
        - [0.00413002622, -0.00332498388, -0.000848109332, -0.000406691892, 0.00149330229]
        - [-0.00332498388, 0.00423865185, 0.000609995065, 0.000447884848, -0.00198342431]
        - [-0.000848109332, 0.000609995065, 0.0179180233, -0.0139526675, -0.000419354195]
        - [-0.000406691892, 0.000447884848, -0.0139526675, 0.0176090923, -0.000427770435]
        - [0.00149330229, -0.00198342431, -0.000419354195, -0.000427770435, 0.00201519131]
    - means: [0.757398556, 0.228773992, 0.00110913152, -0.000873656294, 0.0677685293]
      covariance:
        - [0.00372855018, -0.00283559277, 7.81683737e-05, -5.53574956e-05, -0.00175599948]
        - [-0.00283559277, 0.00370910325, -0.00013336092, 0.00012761393, 0.00128510668]
        - [7.81683737e-05, -0.00013336092, 0.0177483896, -0.0130322168, 6.4885982e-05]
        - [-5.53574956e-05, 0.00012761393, -0.0130322168, 0.0169730206, -4.18798439e-05]
        - [-0.00175599948, 0.00128510668, 6.4885982e-05, -4.18798439e-05, 0.00195438375]
    - means: [0.198516518, 0.792744105, -0.0472557982, -0.0333198714, -0.00746000472]
      covariance:
        - [0.00233305594, -0.00112909094, 0.000503448472, 0.000338975635, -0.000152968596]
        - [-0.00112909094, 0.00192412867, 0.000601037901, 1.78341407e-05, 0.000272259322]
        - [0.000503448472, 0.000601037901, 0.0148029835, -0.0123754828, 0.000114676622]
        - [0.000338975635, 1.78341407e-05, -0.0123754828, 0.0141841404, -9.50121964e-05]
        - [-0.000152968596, 0.000272259322, 0.000114676622, -9.50121964e-05, 0.00307078657]
    - means: [0.55162879, 0.421223066, -0.00326568492, 0.0073358451, -0.136095987]
      covariance:
        - [0.0136726272, -0.0124196149, -6.77457689e-05, -0.000186221927, 0.00113994882]
        - [-0.0124196149, 0.0131990105, -0.0001525186, 0.000187918672, -0.00072572775]
        - [-6.77457689e-05, -0.0001525186, 0.0231210904, -0.0194229642, -0.000140483516]
        - [-0.000186221927, 0.000187918672, -0.0194229642, 0.0217373176, 7.90708268e-05]
        - [0.00113994882, -0.00072572775, -0.000140483516, 7.90708268e-05, 0.000671170943]
    - means: [0.5034653, 0.46872954, 0.00419595692, -0.00736434769, 0.138663722]
      covariance:
        - [0.0143097582, -0.0131789312, 5.84795881e-05, -0.000179578405, -0.000449735291]
        - [-0.0131789312, 0.0141062439, 0.000283478865, 0.00014711228, 5.31608868e-05]
        - [5.84795881e-05, 0.000283478865, 0.0235784881, -0.0198343226, -0.0001527965]
        - [-0.000179578405, 0.00014711228, -0.0198343226, 0.0220192126, 0.000118910175]
        - [-0.000449735291, 5.31608868e-05, -0.0001527965, 0.000118910175, 0.00060768473]
    - means: [0.767568627, 0.220278888, -0.00097655811, 0.000878976359, -0.057475313]
      covariance:
        - [0.00317257454, -0.00234444581, -6.06395131e-05, 3.61794251e-05, 0.00156711916]
        - [-0.00234444581, 0.00326005989, 0.00010849244, -9.86664846e-05, -0.00111646512]
        - [-6.06395131e-05, 0.00010849244, 0.0174746435, -0.012628733, 7.86544453e-05]
        - [3.61794251e-05, -9.86664846e-05, -0.012628733, 0.0166955983, -5.66194913e-05]
        - [0.00156711916, -0.00111646512, 7.86544453e-05, -5.66194913e-05, 0.00210933949]
    - means: [0.250297463, 0.733580913, 0.0125323274, 0.0115232794, -0.0826979928]
      covariance:
        - [0.0048791311, -0.00412718468, -0.0007456679, -0.000709134398, -0.00172573604]
        - [-0.00412718468, 0.00513665626, 0.000498154877, 0.000598726604, 0.00223295979]
        - [-0.0007456679, 0.000498154877, 0.0183029766, -0.0144801713, 0.000482037239]
        - [-0.000709134398, 0.000598726604, -0.0144801713, 0.0178354501, 0.000332905413]
        - [-0.00172573604, 0.00223295979, 0.000482037239, 0.000332905413, 0.00193181161]
  weights: [0.171118343, 0.190789926, 0.0943923462, 0.0940510903, 0.0906591629, 0.194029573, 0.16490176]
  test statistics:
    sigma: []
    densities: []
  references:
    []
  dof: 5

MultivariateGaussian

key

kind

required

description

observables

sequence

yes

The qualified names of the observables this constraint applies to.

kinematics

sequence

yes

One kinematics map per observable, in the same order as ‘observables’.

options

sequence

yes

One options map per observable, in the same order as ‘observables’.

means

sequence

yes

The measurements’ central values, in the same order as ‘observables’.

sigma-stat-hi

sequence

yes

The measurements’ upper statistical one-sigma uncertainties.

sigma-stat-lo

sequence

yes

The measurements’ lower statistical one-sigma uncertainties.

sigma-sys

sequence

yes

The measurements’ (symmetric) systematic one-sigma uncertainties.

correlations

sequence

yes

The measurements’ correlation matrix, as a sequence of rows.

dof

scalar

no

The number of degrees of freedom; defaults to the number of measurements.

references

sequence

no

The list of reference names this constraint originates from.

Example:

B^0->K^*0gamma::S_K+C_K@BaBar:2008A:
  type: MultivariateGaussian
  observables:
    - B->K^*gamma::S_K^*gamma
    - B->K^*gamma::C_K^*gamma
  kinematics:
    - {}
    - {}
  options:
    - {q: d, tag: BFS2004}
    - {q: d, tag: BFS2004}
  means: [-0.03, -0.14]
  sigma-stat-hi: [0.29, 0.16]
  sigma-stat-lo: [0.29, 0.16]
  sigma-sys: [0.03, 0.03]
  correlations:
    - [1, 0.05]
    - [0.05, 1]
  references:
    []
  dof: 2

MultivariateGaussian(Covariance)

key

kind

required

description

observables

sequence

yes

The qualified names of the observables (predictions) this constraint applies to.

kinematics

sequence

yes

One kinematics map per observable, in the same order as ‘observables’.

options

sequence

yes

One options map per observable, in the same order as ‘observables’.

means

sequence

yes

The measurements’ central values.

covariance

sequence

yes

The measurements’ covariance matrix, as a sequence of rows.

response

sequence

no

The response matrix mapping predictions to measurements, if they are not in 1:1 correspondence.

dof

scalar

no

The number of degrees of freedom; defaults to the number of measurements.

references

sequence

no

The list of reference names this constraint originates from.

Example:

B->pi::f_0+f_+@FLAG:2024A:
  type: MultivariateGaussian(Covariance)
  observables:
    - B->pi::f_+(q2)
    - B->pi::f_+(q2)
    - B->pi::f_+(q2)
    - B->pi::f_0(q2)
    - B->pi::f_0(q2)
  kinematics:
    - {q2: 18}
    - {q2: 22}
    - {q2: 26}
    - {q2: 18}
    - {q2: 26}
  options:
    - {form-factors: G2026}
    - {form-factors: G2026}
    - {form-factors: G2026}
    - {form-factors: G2026}
    - {form-factors: G2026}
  means: [1.07854, 2.0037, 6.11813, 0.48709, 0.95403]
  covariance:
    - [0.0034726032, 0.00499931193, 0.0114332044, -6.23611537e-06, -1.11172232e-05]
    - [0.00499931193, 0.00911756815, 0.0263339334, -1.1469038e-05, -2.1550285e-05]
    - [0.0114332044, 0.0263339334, 0.118033742, -1.94665745e-05, -3.47890026e-05]
    - [-6.23611537e-06, -1.1469038e-05, -1.94665745e-05, 0.000460187487, 0.000709222282]
    - [-1.11172232e-05, -2.1550285e-05, -3.47890026e-05, 0.000709222282, 0.00146191433]
  references:
    []
  dof: 5

UniformBound

key

kind

required

description

observables

sequence

yes

The qualified names of the observables this constraint applies to.

kinematics

sequence

yes

One kinematics map per observable, in the same order as ‘observables’.

options

sequence

yes

One options map per observable, in the same order as ‘observables’.

bound

scalar

yes

The shared upper bound on all observables.

uncertainty

scalar

yes

The (relative) uncertainty on the bound.

references

sequence

no

The list of reference names this constraint originates from.

Example:

b->u::MesonicBound[1^+_A]:
  type: UniformBound
  observables:
    - B->pipi::Saturation[1^+_A]
  kinematics:
    - {}
  options:
    - {C: +-}
  bound: 1
  uncertainty: 0.05
  references:
    []