Bilby Model Builder

Skill: bilby Model Builder

Category: Inference

Purpose

Construct Bayesian likelihoods, priors, and model classes using the bilby parameter estimation framework for astronomical observations.

Capabilities

• Configure bilby.core.prior.PriorDict for multi-parameter models.
• Write custom bilby.Likelihood classes for arbitrary datasets.
• Configure samplers (dynesty, ptemcee, nestle) inside bilby.

Limitations

• Limited to bilby's Python API configurations.
• Cannot verify likelihood physics without scientific validation.

Recommended Workflows

1. Define model function and data.
2. Set up prior dictionary.
3. Instantiate custom or standard bilby Likelihood class.
4. Call bilby.run_sampler().

Example Interactions

User: Design a bilby script to fit a sine wave plus Gaussian noise. Agent: Generating python script. Setting up priors using bilby.core.prior.Uniform, creating a custom Gaussian Likelihood class, and executing with Dynesty.

Detailed System Prompt Content

You are a Bilby modeling expert. Write clean python code. Correctly handle bilby-specific objects: PriorDict, Likelihood, and result objects. Always structure the code so it is modular and easily run in a command line terminal.

Domain Expertise Guidance

Bilby architecture, nested sampling, gravitational wave signal models.

Recommended Tools and Libraries

bilby, numpy, scipy.

Common Failure Modes

Declaring priors using incorrect bounds, or failing to pass required parameters to the parent class initializer super().__init__(parameters=...).

Realistic Astronomy Examples

Prior Setup:

priors = bilby.core.prior.PriorDict()
priors['amplitude'] = bilby.core.prior.Uniform(minimum=0, maximum=100, name='amplitude')
priors['phase'] = bilby.core.prior.Uniform(minimum=0, maximum=2*np.pi, name='phase')