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| File | Description |
|---|---|
| classifier.safetensors | Model selection classifier |
| classifier_training.png | Model selection classifier |
| posterior_burn_slab_n1.safetensors | Posterior model (.safetensors) |
| posterior_burn_slab_n2.safetensors | Posterior model (.safetensors) |
| posterior_burn_slab_n3.safetensors | Posterior model (.safetensors) |
| posterior_burn_slab_n4.safetensors | Posterior model (.safetensors) |
| posterior_burn_slab_n5.safetensors | Posterior model (.safetensors) |
| posterior_external_dispersion_n1.safetensors | Posterior model (.safetensors) |
| posterior_external_dispersion_n2.safetensors | Posterior model (.safetensors) |
| posterior_external_dispersion_n3.safetensors | Posterior model (.safetensors) |
| posterior_external_dispersion_n4.safetensors | Posterior model (.safetensors) |
| posterior_external_dispersion_n5.safetensors | Posterior model (.safetensors) |
| posterior_faraday_thin_n1.safetensors | Posterior model (.safetensors) |
| posterior_faraday_thin_n2.safetensors | Posterior model (.safetensors) |
| posterior_faraday_thin_n3.safetensors | Posterior model (.safetensors) |
| posterior_faraday_thin_n4.safetensors | Posterior model (.safetensors) |
| posterior_faraday_thin_n5.safetensors | Posterior model (.safetensors) |
| posterior_internal_dispersion_n1.safetensors | Posterior model (.safetensors) |
| posterior_internal_dispersion_n2.safetensors | Posterior model (.safetensors) |
| posterior_internal_dispersion_n3.safetensors | Posterior model (.safetensors) |
| posterior_internal_dispersion_n4.safetensors | Posterior model (.safetensors) |
| posterior_internal_dispersion_n5.safetensors | Posterior model (.safetensors) |
| spectral_shape_posterior.safetensors | |
| training_burn_slab_n1.png | Training plot |
| training_burn_slab_n2.png | Training plot |
| training_burn_slab_n3.png | Training plot |
| training_burn_slab_n4.png | Training plot |
| training_burn_slab_n5.png | Training plot |
| training_external_dispersion_n1.png | Training plot |
| training_external_dispersion_n2.png | Training plot |
| training_external_dispersion_n3.png | Training plot |
| training_external_dispersion_n4.png | Training plot |
| training_external_dispersion_n5.png | Training plot |
| training_faraday_thin_n1.png | Training plot |
| training_faraday_thin_n2.png | Training plot |
| training_faraday_thin_n3.png | Training plot |
| training_faraday_thin_n4.png | Training plot |
| training_faraday_thin_n5.png | Training plot |
| training_internal_dispersion_n1.png | Training plot |
| training_internal_dispersion_n2.png | Training plot |
| training_internal_dispersion_n3.png | Training plot |
| training_internal_dispersion_n4.png | Training plot |
| training_internal_dispersion_n5.png | Training plot |
| training_spectral_shape.png | Training plot |
| training_summary.txt | Summary file |
2000 cases, 1000 posterior
samples each). calibrated means the empirical coverage curve stayed within
the null band around the diagonal; under-confident / over-confident /
mixed describe the direction of any deviation.| Model Type | N | Verdict | Unsigned Area | Calibrated |
|---|---|---|---|---|
| burn_slab | 1 | mixed | 0.0414 | no |
| burn_slab | 2 | mixed | 0.0190 | no |
| burn_slab | 3 | calibrated | 0.0032 | yes |
| burn_slab | 4 | under-confident | 0.0141 | no |
| burn_slab | 5 | under-confident | 0.0171 | no |
| external_dispersion | 1 | calibrated | 0.0058 | yes |
| external_dispersion | 2 | calibrated | 0.0048 | yes |
| external_dispersion | 3 | calibrated | 0.0069 | yes |
| external_dispersion | 4 | calibrated | 0.0098 | yes |
| external_dispersion | 5 | calibrated | 0.0051 | yes |
| faraday_thin | 1 | under-confident | 0.0396 | no |
| faraday_thin | 2 | mixed | 0.0372 | no |
| faraday_thin | 3 | under-confident | 0.0392 | no |
| faraday_thin | 4 | under-confident | 0.0464 | no |
| faraday_thin | 5 | under-confident | 0.0146 | no |
| internal_dispersion | 1 | calibrated | 0.0077 | yes |
| internal_dispersion | 2 | calibrated | 0.0123 | yes |
| internal_dispersion | 3 | calibrated | 0.0053 | yes |
| internal_dispersion | 4 | calibrated | 0.0097 | yes |
| internal_dispersion | 5 | calibrated | 0.0100 | yes |
1from src.inference import InferenceEngine
2
3engine = InferenceEngine(model_dir="path/to/downloaded/models", device="cuda") # falls back to CPU
4engine.load_models()
5
6# qu_obs: np.ndarray, shape (2*n_freq,) = [Q_0..Q_{n-1}, U_0..U_{n-1}]
7result, all_results = engine.infer(qu_obs, n_samples=5000)
8print(f"Best model: {result.n_components} components")Pal & Jagannathan, submitted to AJ (The Astronomical Journal).