jwspecfit 1.1.6

JWST NIRSpec emission-line fitting with resolution-aware Gaussian models

jwspecfit

Emission-line fitting, MCMC, and chemical abundances for JWST NIRSpec spectra.

Three packages, one pipeline — from 1-D NIRSpec spectra to element abundances.

Package	What it does
jwspecfit	Resolution-aware Gaussian line fitting with bootstrap errors
jwspecmcmc ⭐	Bayesian MCMC fitting (NUTS · emcee · nautilus) — recommended
jwspecabund	Chemical abundances — direct T_e · forward model · strong-line

Recommended fitter: for science-quality results the authors recommend the Bayesian MCMC fitter jwspecmcmc (full posteriors and faithful uncertainties). Use the least-squares jwspecfit engine for quick looks, initial guesses, and BIC model selection.

Key features

• Resolution-aware line profiles: bin-averaged Gaussians via erf — correct for prism, gratings, and stacks.
• Broad-Balmer detection: BIC-based selection across four nested models.
• UV doublets: flux-ratio and kinematic tying for C IV, N V, N III], O III], C III], N IV].
• Lyα + DLA: skewed Gaussian + IGM transmission + dynesty N_HI retrieval.
• Dust correction: multi-Balmer A_V anchored on Hβ or Hα, Salim+18 or Cardelli+89 curves.
• Abundances: direct T_e ([O III] 4363 or UV 1666), Cullen+25 forward model, Sanders+25 strong-line.
• ICFs: Martinez+25 (N/O) · Izotov+06 (S, Ne, Ar) · Garnett+97 (C/O).
• Lyα escape fraction with Monte Carlo propagation of A_V uncertainty.

Install

pip install jwspecfit

Or with all optional extras (MCMC backends, abundances, DLA fitter):

pip install "jwspecfit[nuts,mcmc,abund,dla]"

For development (editable install from source):

git clone https://github.com/raunaq-rai/jwspecfit.git
cd jwspecfit
pip install -e ".[dev,nuts,mcmc,abund]"

Requires Python ≥ 3.10. See the installation guide for individual extras.

Example

import jwspecfit, jwspecabund

spec   = jwspecfit.read_fits("spectrum.fits", z=6.0)
result = jwspecfit.fit_lines(spec, z=6.0)
abund  = jwspecabund.compute_abundances(result, z=6.0)

print(abund.summary())

Line fitting

The recommended fitter is the Bayesian jwspecmcmc engine sampled with NumPyro NUTS:

import numpyro
numpyro.set_host_device_count(6)  # required: run this before sampling

import jwspecmcmc

result = jwspecmcmc.fit_lines(spec, z=6.0, sampler="nuts")

Important: call numpyro.set_host_device_count(6) (matching the number of CPU cores you want to use) before any sampling. NumPyro otherwise sees a single host device, which forces multiple chains to run sequentially and can prevent NUTS from working properly. Set it once, at the top of your script or notebook, before importing/calling the fitter.

Model and parameters

The continuum is first estimated with a low-order polynomial fitted to the spectrum after masking the regions around known emission lines and iteratively rejecting positive outliers, and is then subtracted. Each emission line is modelled as a resolution-aware Gaussian, integrated over the wavelength range spanned by each spectral pixel (via the error function) so that the model is compared to the data on the same footing for the prism, the gratings, and stacked spectra alike. The line model is the sum of these Gaussians on top of the fitted continuum.

For every line three parameters are fit: an amplitude (the integrated line flux), a central wavelength, and a Gaussian width. Physically related transitions are tied to reduce the dimensionality and to respect atomic physics: the Balmer series can share a common velocity and width with the bright rest-optical lines (e.g. [O III] λ5007), doublets share kinematics, and unresolved doublets are held at fixed or bounded flux ratios. Centroids and widths are bounded to physically motivated ranges set by the instrumental resolution and a maximum velocity offset from the systemic redshift.

Sampling and inference

Inference is fully Bayesian: full posteriors are obtained via Markov Chain Monte Carlo using a Gaussian likelihood (identical to the weighted chi-squared used by the least-squares engine, so both fitters treat the data identically) with uniform priors over the parameter bounds. Sampling uses the No-U-Turn Sampler (NUTS), a self-tuning variant of Hamiltonian Monte Carlo. The likelihood is JIT-compiled in JAX and differentiated automatically, so the sampler uses the gradient to propose distant, high-acceptance moves and explores the high-dimensional parameter space far more efficiently than random-walk methods.

During warmup the step size is adapted to a target acceptance probability (target_accept_prob = 0.8) and the trajectory length is bounded by a maximum tree depth (max_tree_depth = 10), with the No-U-Turn criterion terminating each trajectory automatically; there is no hand-tuned proposal scale. By default the chain is run for 500 warmup (adaptation) steps followed by 2000 posterior samples, initialised at a validated finite-likelihood seed (a least-squares pre-fit) so that every transition starts in bounds.

Uncertainties on the line fluxes are taken as the 16th–84th percentiles of the posterior, and all quantities are reported as posterior medians with the corresponding credible intervals. The fit yields full posterior chains for every parameter, including a posterior distribution of the integrated flux of each line; these per-line flux posteriors are resampled by the downstream abundance routines to propagate measurement uncertainties through the (non-linear) abundance calculation.

Convergence and inspecting the chains

Convergence is assessed with the Gelman–Rubin statistic ($\hat{R}$) and the effective sample size (ESS), computed for every free parameter. These are attached to the result in the convergence dictionary:

conv = result.convergence
conv["converged"]   # bool: True if R-hat < 1.05 and ESS > 100 for all params
conv["r_hat_max"]   # worst (largest) R-hat across parameters
conv["ess_min"]     # smallest effective sample size
conv["r_hat"]       # per-parameter R-hat array
conv["ess"]         # per-parameter ESS array

The Gelman–Rubin statistic compares the within-chain and between-chain variance, so it is only meaningful with two or more chains. Run multiple chains (and set numpyro.set_host_device_count to at least that many cores, as above) to get a usable $\hat{R}$:

import numpyro
numpyro.set_host_device_count(4)

result = jwspecmcmc.fit_lines(spec, z=6.0, sampler="nuts", n_chains=4)
print(result.convergence["r_hat_max"], result.convergence["ess_min"])

The chains themselves can be inspected directly. The raw, per-chain samples are available as result.chains with shape (n_chains, n_steps, n_free) — ideal for trace plots to check that the chains are well-mixed and stationary:

import matplotlib.pyplot as plt

chains = result.chains                       # (n_chains, n_steps, n_free)
fig, ax = plt.subplots()
for c in range(chains.shape[0]):
    ax.plot(chains[c, :, 0], alpha=0.6)      # trace of the first free parameter
ax.set_xlabel("step"); ax.set_ylabel("parameter 0")

The flattened posterior across all chains is result.flat_chains, of shape (n_samples, 3 * n_lines), ordered as all amplitudes, then all central wavelengths, then all widths, following result.line_names. So for line i:

nL = len(result.line_names)
amp  = result.flat_chains[:, i]            # amplitude (= integrated flux)
cen  = result.flat_chains[:, nL + i]       # central wavelength
wid  = result.flat_chains[:, 2 * nL + i]   # width

For convenience, each line also carries its full flux posterior directly:

result.lines["OIII_5007"].flux_posterior   # 1-D array of flux samples

Two other backends are available for cross-checks (sampler="emcee", an affine-invariant ensemble sampler, and sampler="nautilus", an importance-nested sampler), but NUTS is the default and recommended choice.

Abundances

jwspecabund turns a set of fitted line fluxes into gas-phase chemical abundances. Fluxes are dust-corrected from the Balmer decrement before any abundance is computed, and measurement uncertainties are propagated by resampling the line-flux posteriors.

import jwspecabund

abund = jwspecabund.compute_abundances(result, z=6.0)
print(abund.summary())

Direct (T_e-based) metallicity

The direct method measures the gas-phase oxygen abundance from the electron temperature, with all atomic physics handled by PyNEB. The temperature is set by an auroral-to-nebular line ratio — primarily [O III] λ4363/(λ4959+λ5007), or the UV intercombination line O III] λ1666 relative to λ5007 when λ4363 is unavailable — giving the temperature of the high-ionisation (O²⁺) zone. The low-ionisation (O⁺) temperature is obtained either from [N II] λ5755/λ6585 or from a temperature–temperature relation. The electron density comes from a density-sensitive doublet appropriate to the zone ([S II] λ6716/λ6731 or [O II] λ3726/λ3729 at low ionisation, with C III] λ1907/λ1909 and N IV] λ1483/λ1486 available for higher-ionisation zones).

The ionic abundances are then computed at the relevant temperature and density: O⁺/H⁺ from the [O II] λ3726,3729 doublet and O²⁺/H⁺ from [O III] λ5007, each relative to a hydrogen Balmer line. The total oxygen abundance is the direct sum

O/H = O⁺/H⁺ + O²⁺/H⁺

so no ionisation-correction factor is required for the dominant ionisation stages.

Strong-line metallicity

When the auroral lines are too faint for a direct temperature, metallicity is estimated from bright strong-line ratios using the Sanders et al. (2025) calibrations. The implemented diagnostics are O3 ([O III] λ5007/Hβ), O2 ([O II]/Hβ), R23 (([O III] λ5007 + [O II])/Hβ), and O32 ([O III] λ5007/[O II]). Available diagnostics are solved simultaneously against the calibration, with Monte Carlo error propagation.

C/O and N/O

Carbon and nitrogen abundance ratios are built from whichever ionic stages are detected:

• C/O uses carbon from C II] λ2326, C III] λ1907,1909, and C IV λ1548,1551, with oxygen from [O II] λ3726,3729 and [O III] λ5007.
• N/O uses nitrogen from [N II] λ6585, N III] λ1750, N IV] λ1486, and N V λ1240, again with oxygen from [O II] λ3726,3729 and [O III] λ5007.

The code automatically uses the ions present in the fit, so UV-only spectra (C III], C IV, N III], N IV]) and rest-optical spectra ([N II], [O II], [O III]) are both supported.

Documentation

Usage guides, API reference, and methodology: https://jwspecfit.readthedocs.io/

Worked examples: docs/notebooks/.

Tests

pytest tests/

Citation

If you use jwspecfit in your research, please cite it. Choose whichever format your reference manager or journal prefers.

📖 DOI: 10.5281/zenodo.19679793

Concept DOI — always resolves to the latest Zenodo-archived release.

Plain text

Rai, R. (2026). jwspecfit: Resolution-aware emission-line fitting, MCMC sampling, and chemical abundances for JWST NIRSpec (v1.0.1). Zenodo. https://doi.org/10.5281/zenodo.19679793

BibTeX

@software{rai_jwspecfit,
  author       = {Rai, Raunaq},
  title        = {{jwspecfit}: Resolution-aware emission-line fitting,
                  MCMC sampling, and chemical abundances for JWST NIRSpec},
  year         = {2026},
  version      = {1.0.1},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.19679793},
  url          = {https://doi.org/10.5281/zenodo.19679793},
}

Other formats

APA · Chicago · IEEE · Harvard · MLA · CSL-JSON · DataCite XML are all available from the Zenodo record page (Export panel on the right).

GitHub's "Cite this repository" button (top-right of the repo page) reads CITATION.cff and produces APA/BibTeX on the fly.

Pinning a specific version

The concept DOI above always points to the latest release. If a paper needs to cite the exact code version used for reproducibility, pick the per-version DOI from the "Versions" list on the Zenodo page.

Licence

MIT — see LICENCE.