ExoIris: fast exoplanet transmission spectroscopy in Python

I present ExoIris, a user-friendly Python package for exoplanet transmission and emission spectroscopy. Unlike existing tools, ExoIris models two-dimensional spectrophotometric transit time series directly and supports the joint analysis of multiple datasets obtained with different instruments and at different epochs, as well as modeling stellar spot crossings and the influence of unocculted heterogeneities (the transit light source effect). These features enable a self-consistent estimation of both wavelength-independent and wavelength-dependent parameters. They offer a more robust workflow than the commonly used two-step approach, in which a “white” light curve is fitted first, and the transmission spectrum is then derived from independent fits constrained by the white-light solution. Despite its increased flexibility and robustness, ExoIris remains computationally efficient. A low-resolution transmission spectrum can be estimated from a single JWST NIRISS transit observation in ~5 minutes assuming white noise, and in ~15 minutes when accounting for time-correlated systematics using a Gaussian process noise model, on a standard desktop computer.

💡 Research Summary

ExoIris is an open‑source Python package designed to model exoplanet transmission and emission spectroscopy in a fully two‑dimensional (time‑wavelength) framework. Unlike the conventional two‑step workflow—first fitting a “white” light curve and then deriving a transmission spectrum from independent spectroscopic fits—ExoIris jointly fits all spectrophotometric light curves, allowing wavelength‑independent (orbital) and wavelength‑dependent (planet‑star radius ratio, limb darkening) parameters to be inferred self‑consistently.

The core of the package is an interpolating function for the planet‑star radius ratio k(λ). Users define a set of knots across the wavelength range; the knot values become free parameters, and the radius ratio at each spectral channel is obtained via one of several interpolation schemes (nearest‑neighbor, linear, quadratic/cubic B‑splines, PCHIP, Makima). This decouples the spectral resolution of the retrieved transmission spectrum from the native resolution of the data, enabling high‑resolution sampling of narrow absorption features while keeping the dimensionality of the model manageable. Knot placement can be uniform, logarithmic, or clustered around expected spectral features, and knot positions themselves may be treated as free parameters for adaptive refinement.

ExoIris supports two approaches to limb darkening. Analytic laws (quadratic, power‑2) are treated analogously to the radius‑ratio knots, with wavelength‑dependent coefficients interpolated across user‑defined knots. Alternatively, a physics‑based LDTk model generates limb‑darkening profiles from three stellar parameters (T_eff, log g,