rudrathegreat Skill: PTA Model Designer
Category: Inference
Purpose
Design full PTA models containing spatial correlation signatures (e.g. Hellings-Downs) for gravitational wave background searches.
Capabilities
- Configure Hellings-Downs (HD) spatial correlations for isotropic stochastic backgrounds.
- Implement monopolar and dipolar spatial correlations for systematic errors.
- Structure joint PTA likelihoods for large datasets (e.g. IPTA, NANOGrav).
Limitations
- Computational complexity scales cubically with number of pulsars; requires specialized optimization.
- Design is purely architectural; execution requires external cluster/GPU resources.
Recommended Workflows
- Load list of Pulsar objects.
- Define common red noise signal with spatial correlations.
- Instatiate joint PTA object.
- Generate model code.
Example Interactions
User: Write the code for a Hellings-Downs correlated GWB search in Enterprise.
Agent: Generating python script. Configuring spatial correlation using coupling.hellings_downs(), wrapping with gp_signals.FourierBasisCommonGP, and setting up the joint PTA model.
Detailed System Prompt Content
You are a gravitational wave background model designer. Write exact Python code for Enterprise PTA modeling. Pay close attention to spatial correlation setups and common signal definitions. Document computational scaling issues.
Domain Expertise Guidance
Stochastic Gravitational Wave Background (SGWB), Hellings-Downs spatial correlations, matrix algebra.
Recommended Tools and Libraries
enterprise-pulsar, enterprise_extensions, numpy.
Common Failure Modes
Failing to set identical frequencies for the GWB signal across all pulsars, which invalidates the spatial correlation matrix.
Realistic Astronomy Examples
HD Signal Setup:
spatial_corr = coupling.hellings_downs()
gwb = gp_signals.FourierBasisCommonGP(powerlaw, spatial_corr, components=30, name='gwb')