Tutorials¶
Three executable notebooks live in
tutorials_for_user/.
They run with no survey data and no configuration: real JADES spectra are
bundled alongside them, and weights download from the Hub on first use.
pip install "specsr[hub]"
jupyter lab tutorials_for_user/01_quickstart.ipynb
What each one covers¶
1 — Quickstart. Load the pretrained chain, run it on one real galaxy, and
read the result. Covers the three stages (SR1 → ZHead → SR2), what each field of
SpecSRResult means, and how to plot a prediction
against a true grating spectrum without misleading yourself about scale.
2 — Your own spectrum. The case you will actually have: a spectrum on its
own wavelength grid. Covers passing wavelength=, why the model’s grid is
logarithmic, and a measurement of what grid coarseness does to integrated line
flux — the dominant effect, larger than the choice of interpolant.
3 — Batches and trust. Batch inference, then the harder question: how far to believe the output. Uncertainty calibration, the shape of redshift failures, and line-flux recovery as a function of line brightness. Read this one before putting a number from this model into a paper.
Bundled data¶
file |
contents |
|---|---|
|
a single galaxy (z = 3.00) |
|
24 galaxies spanning z = 0.31–9.43 |
Both are drawn from the validation split of the paired DR4 dataset — real
spectra the model never trained on, sampled across the redshift range rather
than cherry-picked. Each carries wavelength, flux_low (the prism input),
flux_low_err, flux_high (the grating truth, for comparison only), z,
target_id and field.
The 24-galaxy sample reaches z ~ 9.4 deliberately, past where the training data is dense. That is where most of the model’s failures are.
Two caveats the notebooks make concrete¶
Absolute flux scale is not predicted
Only spectral shape is. Work in normalized units; see the note on the front page.
These weights are not the product of an exhaustive search
Hyperparameters carry over from an early sweep, and the released checkpoints are selected on a composite criterion that does not track redshift accuracy directly. The numbers the notebooks print are the model’s current behaviour, and are expected to improve.