ExoIris 0.23.2

Easy and robust exoplanet transmission spectroscopy.

ExoIris: Fast and Flexible Transmission Spectroscopy in Python

ExoIris is a Python package for modeling exoplanet transmission spectroscopy. ExoIris removes the typical limitations of the two-step workflow by modeling the full two-dimensional spectroscopic transit time series directly. It supports combining transmission spectroscopy datasets from multiple instruments observed in different epochs, yielding self-consistent wavelength-independent and wavelength-dependent parameters, simplifying joint analyses, and delivering results quickly.

Why ExoIris?

Transmission spectroscopy is often done following a two-step workflow: (1) fit a white light curve to infer wavelength-independent parameters; (2) fit each spectroscopic light curve independently, constrained by the white-light solution. This split can introduce approximations and inconsistencies.

ExoIris takes a different approach. It models spectrophotometric time series end-to-end, enabling:

• Self-consistent inference of shared (wavelength-independent) and spectral (wavelength-dependent) parameters.
• Joint modeling of multiple datasets from different instruments and epochs.
• Accounting for transit timing variations and dataset-dependent offsets within a unified framework.

This design is a natural fit for JWST-class data, where correlated noise, multi-epoch observations, and cross-instrument combinations are the norm.

Documentation

Full documentation and tutorials: https://exoiris.readthedocs.io

Installation

Install from PyPI:

pip install exoiris

Latest development version:

git clone https://github.com/hpparvi/ExoIris.git
cd ExoIris
pip install -e .

ExoIris supports Python 3.9+. See the docs for dependency details and optional extras.

Key Features

• Direct modelling of spectroscopic transit time series
  Built on PyTransit’s TSModel, optimised for transmission spectroscopy; scales to hundreds–thousands of light curves simultaneously.

• Flexible limb darkening
  Use standard analytical laws (quadratic, power-2, non-linear), numerical intensity profiles from stellar atmosphere models, or user-defined radially symmetric functions.

• Robust noise treatment
  Choose white noise or time-correlated noise via a Gaussian Process likelihood, without changing the overall workflow.

• Full control of spectral resolution
  The transmission spectrum is represented as a cubic spline with user-defined knots, allowing variable resolution across wavelength.

• Reproducible, incremental workflows
  Save and reload models to refine a low-resolution run into a high-resolution analysis seamlessly.

• Joint multi-dataset analyses
  Combine instruments and epochs in one fit, with support for transit timing variations and dataset-specific systematics and offsets.

Performance

ExoIris is designed for speed and stability:

• A transmission spectroscopy analysis of a single JWST/NIRISS dataset at R ≈ 100 typically runs in 3–5 minutes assuming white noise, or 5–15 minutes with a GP noise model, on a standard desktop CPU.
• A high-resolution analysis of the JWST/NIRISS WASP-39 b dataset (~3800 spectroscopic light curves; see Feinstein et al. 2023) can be optimised and sampled in about 1.5 hours on an AMD Ryzen 7 5800X (8 cores, ~3-year-old desktop).

---

© 2025 Hannu Parviainen