specsr-roman

Physics-informed super-resolution of Roman grism spectra — recovering emission lines without inventing them.

Roman's High Latitude Spectroscopic Survey will deliver slitless grism spectra (R ≈ 461 λ/µm over 1–1.93 µm) for millions of emission-line galaxies. At that resolving power the diagnostic complexes stay blended — Hα+[N II], [O III]+Hβ — across wide redshift ranges, biasing redshifts and everything derived from them. Assembling survey-scale high-resolution training pairs to fix that observationally is prohibitive.

specsr-roman super-resolves those spectra with a three-stage network trained on simulated Roman products, and is built around one constraint:

A model that invents plausible emission lines is worse than useless in a survey. Every design decision here follows from that.

The pipeline is calibrated on recoverability — whether the data could have revealed a given line at all — and is explicitly penalised for drawing what it could not have seen. It ships with the two audits that keep those claims honest, and both are run in CI-adjacent form on every release.

This is the Roman half of a two-instrument project. The JWST/NIRSpec original is specsr (Haghjoo et al. 2026).

Install

pip install specsr-roman                 # inference + training
pip install "specsr-roman[hub]"          # + published checkpoints and data
pip install "specsr-roman[extract]"      # + dataset building (grizli, photutils)
pip install "specsr-roman[all]"          # everything

Python 3.10 or newer.

Use it

from specsr_roman import RomanPipeline

pipe = RomanPipeline.from_pretrained()      # downloads the published chain

out = pipe.predict(
    flux_low,                                # (864,) on the native grism grid
    flux_low_err,
    phot=roman_medium_fluxes,                # F106, F129, F158 — in that order
)

out.flux_sr        # super-resolved spectrum, 2500 px over 1.0–1.93 µm
out.flux_sr_err    # per-pixel uncertainty
out.z, out.z_err   # redshift point estimate and its spread
out.pz, out.z_grid # the full P(z) — keep it, see "Redshifts are multimodal"
out.presence       # per-line presence probability, over 98 features

phot=None drops the colour prior, and the redshift degrades sharply — 26 % catastrophic against 5 %, see the table below — because a single in-band line is alias-degenerate. It is not the same thing as a grism-only model: this head was trained with photometry, so None hands it its training-mean colours.

New here? tutorials/01_getting_started.ipynb (rendered) runs the whole thing end to end on a 512-spectrum sample: one super-resolved spectrum, the redshift PDF, what photometry buys, and the recoverability-binned line recovery below. Two minutes on a CPU, no dataset download.

From the command line:

specsr-roman info                                    # what's installed and canonical
specsr-roman predict spectra.npz --out predictions.npz
specsr-roman evaluate metrics --cache pred_cache.npz
specsr-roman evaluate figures --outdir figures/

Results

Canonical chain sr1_ou2024_v6 → zhead_ou2024_roman_med3_noisy → sr2_ou2024_v5_romanonly, evaluated on the held-out object-id split of the OU2024 dataset (N = 7,334 spectra, 3,098 galaxies), with 0.05 mag photometric noise applied at evaluation.

Redshift

The deployable row uses only the imaging that actually ships with the HLWAS grism, with realistic noise at train and eval. Three broadband colours break most of the alias degeneracy, which is the whole reason the head takes photometry at all.

The second row is what this chain does when you call it with phot=None, and it is not the grism-only information floor. The head was trained with photometry, so passing None feeds it its training-mean colours: mean imputation on an out-of-distribution input rather than a clean ablation of the information. A head actually trained without colours is a separate experiment, and has not been run. The floor itself is set by physics rather than by architecture — with one line in band the identification is genuinely alias-degenerate, and ~37 % catastrophic is what that allows.

Both rows come from specsr-roman evaluate ablation, which measures the published head with and without its three colours and sweeps the photometric noise. Every configuration it reports uses those three bands and no others.

Line amplitude recovery — median recovered flux fraction, split by whether the line was recoverable from the LR data at all:

Read the first row first. SR2 sharpens recoverable lines toward truth while leaving undetectable ones at ~0.03 of a line it does not draw. A model that scored 0.85 in the bottom row and 0.85 in the top would be worthless, and no single averaged metric would tell you.

Prior-dominance audit (specsr-roman evaluate prior): response exponent r = 0.45 for the published SR1 on OU2024 — 1 means the model reads line strengths from the data, 0 means it recites the training manifold. See Limitations.

How it works

Three stages, each doing one job it can be held to.

SR1 is deliberately conservative. It sees [flux, err] scaled by a shared factor, so the channel ratio is the per-pixel S/N and the network can matched-filter rather than guess which bumps are noise.

ZHead predicts a distribution, not a number. Redshift from a grism is a line-identification problem: one observed line is consistent with Hα, [O III], [O II] or Lyα. A Gaussian head must average between aliases, which is what produced the ~40 % catastrophic floor of two earlier designs. A softmax over a grid carries the whole P(z) — the point estimate is the mode, and ambiguity survives as PDF width instead of becoming a wrong answer.

SR2 refines SR1 with one attention token per rest-frame feature, run once per redshift hypothesis and combined by mode mass. That is what makes it alias-robust: when the top mode is wrong the correct identification is usually the second or third, and the right lines still get drawn.

ARCHITECTURE.md has the full design, including the losses and why each one is shaped the way it is.

Redshifts are multimodal — keep the PDF

out.z is the mode of out.pz, not its mean. For an alias-ambiguous source the second mode is real information, and out.z_err widens precisely when the model is torn between two line identifications. Discarding pz throws away the part of the answer that tells you when not to trust the rest of it.

Training

Every published checkpoint is reproducible from a config file:

specsr-roman train sr1   --config configs/sr1.yaml
specsr-roman train zhead --config configs/zhead.yaml     # needs SR1
specsr-roman train sr2   --config configs/sr2.yaml       # needs SR1 + ZHead

The configs are the published chain — configs/sr1.yaml produces sr1_ou2024_v6 — and the dataclass defaults in specsr_roman.config match them, so "what was this trained with" always has a readable answer. Any field can be overridden on the command line (--epochs 50, --no-augment); an unknown key is an error rather than a silent no-op.

Two settings deserve a warning if you change them:

• phot_tier must stay on bands that ship with the grism. medium is the only tier, and grids.MAX_PHOT_BANDS caps an explicit band list at three: hand a model more bands than the survey delivers alongside a spectrum and it reads the redshift off an effectively complete SED instead of the grism.
• phot_eval_mag_err must stay above zero. A metric measured on noiseless truth photometry is not a metric.

Checkpoint selection is not the validation loss

Both SR1 and SR2 select on custom goals, and this is load-bearing rather than fussy. SR1's total validation loss is dominated by the NLL, which rises as the model commits to line amplitudes — a confident half-amplitude line costs more than a hedged flat one. SR2's plain NLL is continuum-dominated and reliably selects the model that draws nothing.

So SR1 monitors line-flux recovery plus the hallucination penalty, and SR2 monitors -recov_amp + lam_hallu * hallu_amp. SR2's best epoch is 4. That is the design working, not a truncated run: hallucination amplitude climbs from 0.26 to 0.62 by epoch 150 while recoverable amplitude barely moves.

Building the dataset

OpenUniverse2024 provides direct images, truth catalogues and Diffsky SEDs but no grism images, so specsr-roman disperses the scene itself with grizli and extracts it back. That is not a workaround: the same configuration file disperses and extracts, so any residual is the fault of noise, blending and the extraction — not of a mismatched instrument model.

specsr-roman extract run --max-scas 60 --workers 4    # resumable; safe to kill
specsr-roman extract merge                            # → one training npz

Or skip it: the built dataset is on the Hub, together with a 3.8 MB, 512-row tutorial/ subset drawn from the held-out split for the notebook above.

Each (visit, SCA) runs in its own subprocess — grizli's numba disperser corrupts the heap when thousands of sources go through one process — and caches its result, so a crash costs one SCA rather than the run.

Split by object, never by row. The same galaxy appears in many visits as independent noise realisations. specsr_roman.data.get_or_make_group_split keys membership on a hash of the object id, which also makes it stable under dataset growth: adding SCAs never moves an existing galaxy across the boundary.

Limitations

Stated plainly, because they bound what the numbers mean.

• Results are on the Diffsky manifold. Targets are simulated SEDs with simulation line physics. A model can score well by learning that manifold rather than by measuring anything, and no reconstruction metric distinguishes the two. The prior-dominance audit puts the published SR1 at r ≈ 0.45 — it reads the data about half the time it could.
• Anti-prior augmentation is implemented but not used. It raises r to ~0.51 at fixed detectability while suppressing absolute line recovery, so the published SR1 is unaugmented. A version that jitters only recoverable information is open work.
• Trained on simulations, not sky. Real Roman spectra will differ. Domain adaptation via cross-instrument overlaps (DESI/PFS/JWST) is the intended route once real data exists.
• The 5.1 % catastrophic rate is physics, not a bug to be tuned away. With a single line in band the identification is genuinely ambiguous; photometry breaks most of it and cannot break all of it.

Citation

If you use this software, please cite the paper and the code. The method paper for the JWST original is Haghjoo et al. (2026), Learning to See Sharper: A Physics-Informed Artificial Intelligence Framework for Super-Resolving Galaxy Spectra, ApJ.

The code is archived on Zenodo. Cite 10.5281/zenodo.22119689 for the software in general — it always resolves to the newest version — or the version DOI on that record to pin the exact release you ran. CITATION.cff carries both, and GitHub's "Cite this repository" button renders it as BibTeX or APA.

Simulation products: OpenUniverse et al. (2025), OpenUniverse2024; Wang et al. (2022) for the Roman GRS grism configuration.

License

MIT — see LICENSE.