Downloads · 30 days
0
vroomsbi
skunkworks-ra/vroomsbi
vroomsbi is a machine learning model from skunkworks-ra. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Trained neural posterior estimators for Rotation Measure (RM) synthesis.
Access
Public
Updated Aug 6, 2026
Repo size
1.5 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors955 MB · 100%
At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Jul 13, 2026
- Updated
- Aug 6, 2026
- SHA
- 4babb537
Model card
From the Hugging Face model README
VROOM-SBI Trained Models
Trained neural posterior estimators for Rotation Measure (RM) synthesis.
Repository: github.com/arpan-52/vroom-sbi
Model Information
- Model Types: burn, external, faraday, internal
- Max Components: 5
- Classifier: Yes
- Upload Date: 2026-07-12
Files
| 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 |
TARP Calibration
Joint posterior coverage was verified with the TARP (Test of Accuracy with
Random Points) diagnostic (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 |
Usage
from src.inference import InferenceEngine
engine = InferenceEngine(model_dir="path/to/downloaded/models", device="cuda") # falls back to CPU
engine.load_models()
# qu_obs: np.ndarray, shape (2*n_freq,) = [Q_0..Q_{n-1}, U_0..U_{n-1}]
result, all_results = engine.infer(qu_obs, n_samples=5000)
print(f"Best model: {result.n_components} components")
Citation
If you use these models, please cite:
Pal & Jagannathan, submitted to AJ (The Astronomical Journal).
- License
- apache-2.0
- Created
- Jul 13, 2026
- Updated
- Aug 6, 2026