Bayesian Fit with BlackJAX

In this tutorial, we will look at an example runcard to run a Bayesian fit with the BlackJAX nested sampler [CCLL24, YKH25]. We will do so for the Les Houches parametrisation model (see this tutorial for details on the Les Houches model and how to implement it).

An advantage of using BlackJAX is that it can run entirely on GPU (as well as on CPU), so we will discuss the optimal settings to do so.

We will then look at the command to execute the runcard.

Runcard

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: 0                  # 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

#####################
# 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_fit_t0: 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]


# Nested Sampling settings
blackjax_settings:
    n_posterior_samples: 100
    n_live: 500
    repeats: 3
    delete_fraction: 0.5
    log_precision: -3
    posterior_resampling_seed: 52
    seed: 0



actions_:
- run_blackjax_fit                      # Choose from ultranest_fit, monte_carlo_fit, analytic_fit

Note that the prior_settings are the same as in an UltraNest fit, and so all the settings described there can be used (e.g. global bounds).

`blackjax_settings`

n_posterior_samples: Number of posterior samples (‘replicas’) drawn (resampled) from the posterior distribution. The default is 1000. See this tutorial for details on resampling.
n_live: Number of live points at any given time. More live points results in a better estimate of the error.
repeats: Number of successful Monte Carlo steps required. Should be a multiple of the dimentionality of parameter space.
delete_fraction: Fraction of live points allowed to be deleted. The more deleted points, the higher the risk of getting stuck at a local minimum, but the lower the memory usage. This setting is analogous to min_live_points in an UltraNest fit, in that a delete_fraction of 0.5 is equivalent to 250 min_live_points.
log_precision: Termination ratio. This setting is analogous to frac_remain in an UltraNest fit, in that a log_precision of -3 would be equivalent to a frac_remain of 0.001.
posterior_resampling_seed: Random seed used when resampling posterior samples. Fixing this seed ensures reproducible posterior replicas for a given nested sampling run.
blackjax_seed: Global random seed for the BlackJAX nested sampler. Setting this seed makes the nested sampling reproducible.

Running on GPU

BlackJAX nested sampling is designed to run entirely in memory on a GPU, which avoids slow CPU↔GPU transfers, allowing accelerated fits. In order to run on a GPU, you may want to adjust the settings to take into account the following points:

n_live can be made much larger than in a CPU run, namely of the order of ~2000-10000, affording more accurate calculations of model evidences.
The number n_live x delete_fraction is effectively the number of Markov Chains running in parallel. For example, a delete_fraction of 0.5 and a n_live 0f 5000 does 2500 updates in parallel. A larger number of parallel updates is advantageous provided the throughput of the GPU isn’t saturated.
The algorithm natively supports single precision numerics, which can help if engineering for fast GPU throughput.

You can check the point at which the GPU becomes saturated by timing the computation of the likelihood, which is shown in this tutorial.

Running the fit

In general, Colibri runcards can be executed by running the following command:

model_executable runcard.yaml

This must be done after installing the dependencies specific to the model. For example, for the Les Houches parametrisation model presented in this tutorial, the first step would be to run

pip install -e .

from the examples/les_houches_example directory.

Then, you can use the above runcard with the following command:

les_houches_exe runcard.yaml

Running fits will generate fit folders, the details of which can be found in this section.

The next step would be to evolve your fit.

Bayesian Fit with BlackJAX

Runcard

blackjax_settings

Running on GPU

Running the fit

`blackjax_settings`