# Exoplanet Spectra Model Benchmark

> Evaluates the consistency of 1D radiative-convective atmospheric models in predicting exoplanet emission and transmission spectra, specifically probing how differences in opacity linelists, line shape treatments, and chemical equilibrium assumptions affect spectral predictions relative to JWST observational uncertainties. Use when the user wants to benchmark on Exoplanet Atmospheric Test Cases, or asks about evaluating this task. Reports spectral_resolution_and_jwst_error_bar_comparison.

- Skill: `qhjqhj00/exoplanet-spectra-model-benchmark` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/exoplanet-spectra-model-benchmark`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/exoplanet-spectra-model-benchmark/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/exoplanet-spectra-model-benchmark

---


# exoplanet-spectra-model-benchmark

> Toward the analysis of JWST exoplanet spectra: Identifying troublesome model parameters — Baudino et al. (2017) (arXiv:1710.08235, 2017)

## What this evaluates

Evaluates the consistency of 1D radiative-convective atmospheric models in predicting exoplanet emission and transmission spectra, specifically probing how differences in opacity linelists, line shape treatments, and chemical equilibrium assumptions affect spectral predictions relative to JWST observational uncertainties.

## Datasets

- **Exoplanet Atmospheric Test Cases** — total ?; splits: (unstated)

## Metrics

- `spectral_resolution_and_jwst_error_bar_comparison` **(primary)** — range: other
  - Models are run under identical physical conditions and output spectra at 20 cm-1 resolution. Predicted emission/transmission spectra and molecular abundance profiles are compared visually and numerically. Discrepancies are evaluated against simulated JWST NIRSpec/Prism and MIRI/LRS observational error bars (0.5 hr integration) to determine if model differences exceed expected measurement uncertainties.

## Input / output format

**Input**: Atmospheric parameters (Teff, metallicity, planetary target properties) and physical assumptions (linelists, line shapes, chemistry schemes, condensation models).

**Output**: Emission and transmission spectra (flux vs wavelength), molecular abundance profiles, and temperature profiles.

## Scoring recipe

```python
for model in [ATMO, Exo-REM, petitCODE]:
    run_model(params)
    spectrum = get_spectrum(resolution=20, range='near-to-mid IR')
    abundances = get_abundance_profiles()
    temp_profile = get_temperature_profile()
compare_spectra(spectra)
compare_abundances(abundances)
check_if_diff_exceeds_jwst_errorbars(simulated_nirspec_miri_noise)
```

## Common pitfalls

- Failing to standardize linelists (HITRAN vs ExoMol) and line shape treatments (far wings, cut-offs) before comparison, leading to artificial discrepancies.
- Ignoring the impact of condensation/cold traps on mean molecular weight and alkali abundances, which shifts transmission spectra.
- Not accounting for model validity ranges (e.g., Exo-REM's correlated-k coefficients are invalid above 1800 K at low pressures).

## Evidence (verbatim from paper)

> All spectra are plotted at the same spectral resolution (corresponding that of Exo-REM, i.e. with a step and resolution of 20 cm-1). ... To obtain these error bars, we use the JWST Estimator Time Calculator to simulate a half an hour observation with the NIRSpec JWST instrument using the prism mode and with the MIRI JWST instruments using the Low Resolution Spectroscopic mode.

## Citation

```bibtex
@misc{baudino2017toward,
  title={Toward the analysis of JWST exoplanet spectra: Identifying troublesome model parameters},
  author={Baudino et al. (2017)},
  year={2017},
  note={arXiv:1710.08235}
}
```

- arXiv: 1710.08235