Applefy calculates detection limits for exoplanet high contrast imaging (HCI) datasets. The package provides features and functionalities to improve the accuracy and robustness of contrast curve calculations. Applefy implements the classical approach based on the t-test, as well as the parametric boostrap test for non-Gaussian residual noise. Applefy enables the comparison of imaging results across instruments with different noise characteristics.

fm4ar (flow matching for atmospheric retrievals) infers atmospheric properties of exoplanets from observed spectra. It uses flow matching posterior estimation (FMPE) for its machine learning (ML) approach to atmospheric retrieval; this approach provides many of the advantages of neural posterior estimation (NPE) while also providing greater architectural flexibility and scalability. The package uses importance sampling (IS) to verify and correct ML results, and to compute an estimate of the Bayesian evidence. fm4ar's ML models are conditioned on the assumed noise level of a spectrum (i.e., error bars), thus making them adaptable to different noise models.