meta: 'An example fit using Colibri, reduced DIS dataset.'

#######################
# Data and theory specs
#######################

dataset_inputs:    
  # DIS          
- {dataset: SLAC_NC_NOTFIXED_P_EM-F2, variant: legacy_dw}
- {dataset: SLAC_NC_NOTFIXED_D_EM-F2, variant: legacy_dw}
- {dataset: BCDMS_NC_NOTFIXED_P_EM-F2, variant: legacy_dw}
- {dataset: BCDMS_NC_NOTFIXED_D_EM-F2, variant: legacy_dw}
# - {dataset: CHORUS_CC_NOTFIXED_PB_NU-SIGMARED, variant: legacy_dw}
# - {dataset: CHORUS_CC_NOTFIXED_PB_NB-SIGMARED, variant: legacy_dw}
# - {dataset: NUTEV_CC_NOTFIXED_FE_NU-SIGMARED, cfac: [MAS], variant: legacy_dw}
# - {dataset: NUTEV_CC_NOTFIXED_FE_NB-SIGMARED, cfac: [MAS], variant: legacy_dw}
# - {dataset: HERA_NC_318GEV_EM-SIGMARED, variant: legacy}
# - {dataset: HERA_NC_225GEV_EP-SIGMARED, variant: legacy}
# - {dataset: HERA_NC_251GEV_EP-SIGMARED, variant: legacy}
# - {dataset: HERA_NC_300GEV_EP-SIGMARED, variant: legacy}
# - {dataset: HERA_NC_318GEV_EP-SIGMARED, variant: legacy}
# - {dataset: HERA_CC_318GEV_EM-SIGMARED, variant: legacy}
# - {dataset: HERA_CC_318GEV_EP-SIGMARED, variant: legacy}
# - {dataset: HERA_NC_318GEV_EAVG_CHARM-SIGMARED, variant: legacy}
# - {dataset: HERA_NC_318GEV_EAVG_BOTTOM-SIGMARED, variant: legacy}
# - {dataset: NMC_NC_NOTFIXED_EM-F2, variant: legacy_dw}
# - {dataset: NMC_NC_NOTFIXED_P_EM-SIGMARED, variant: legacy}



theoryid: 40000000                     # The theory from which the predictions are drawn.
use_cuts: internal                     # The kinematic cuts to be applied to the data.

closure_test_level: 1                  # The closure test level: False for experimental, level 0
                                       # for pseudodata with no noise, level 1 for pseudodata with
                                       # noise.

closure_test_pdf: LH_PARAM_20250519  # The closure test PDF used if closure_test_level is not False

#flavour_mapping: ["\\Sigma", "g", "V", "V3"]  # Masks other flavours, such that closure tests can be run on desired flavours only. 

#####################
# Loss function specs
#####################

positivity:                            # Positivity datasets, used in the positivity penalty.
  posdatasets:
  - {dataset: NNPDF_POS_2P24GEV_F2U, variant: None, maxlambda: 1e6}

positivity_penalty_settings:
  positivity_penalty: false
  alpha: 1e-7                           
  lambda_positivity: 0                 

# Integrability Settings
integrability_settings:
  integrability: False            

use_t0_covmat: True                       # Whether the t0 covariance is used in the chi2 loss.
t0pdfset: NNPDF40_nnlo_as_01180         # The t0 PDF used to build the t0 covariance matrix.
  

###################
# Methodology specs
###################
prior_settings:
  prior_distribution: uniform_parameter_prior
  prior_distribution_specs:
    bounds:
      alpha_gluon: [-0.1, 1]
      beta_gluon: [9, 13]
      alpha_up: [0.4, 0.9]
      beta_up: [3, 4.5]
      epsilon_up: [-3, 3]
      gamma_up: [1, 6]
      alpha_down: [1, 2]
      beta_down: [8, 12]
      epsilon_down: [-4.5, -3]
      gamma_down: [3.8, 5.8]
      norm_sigma: [0.1, 0.5]
      alpha_sigma: [-0.2, 0.1]
      beta_sigma: [1.2, 3]



# Gradient-descent methodology (shared by Hessian and MC runs)
optimizer_settings:
    optimizer: adam
    optimizer_hyperparams:
        learning_rate: 2e-3

max_epochs: 20000

param_initialiser_settings:
  random_seed: 0
  type: normal
  means:
    alpha_gluon: 0.45
    beta_gluon: 11.0
    alpha_up: 0.65
    beta_up: 3.75
    epsilon_up: 0.0
    gamma_up: 3.5
    alpha_down: 1.5
    beta_down: 10.0
    epsilon_down: -3.75
    gamma_down: 4.8
    norm_sigma: 0.3
    alpha_sigma: -0.05
    beta_sigma: 2.1
  stds:
    alpha_gluon: 0.275
    beta_gluon: 1.0
    alpha_up: 0.125
    beta_up: 0.375
    epsilon_up: 1.5
    gamma_up: 1.25
    alpha_down: 0.25
    beta_down: 1.0
    epsilon_down: 0.375
    gamma_down: 0.5
    norm_sigma: 0.1
    alpha_sigma: 0.075
    beta_sigma: 0.45

hessian_settings:
  tolerance: 1.0
  iter_init: 3
  rng_seed: 1

actions_:
- run_hessian_fit
