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The IDL package Defringeflat identifies and removes fringe patterns from images such as spectrograph flat fields. It uses a wavelet transform to calculate the frequency spectrum in a region around each point of a one-dimensional array. The wavelet transform amplitude is reconstructed from (smoothed) parameters obtaining the fringe's wavelet transform, after which an inverse wavelet transform is performed to obtain the computed fringe pattern which is then removed from the flat.
TEA (Thermal Equilibrium Abundances) calculates gaseous molecular abundances under thermochemical equilibrium conditions. Given a single T,P point or a list of T,P pairs (the thermal profile of an atmosphere) and elemental abundances, TEA calculates mole fractions of the desired molecular species. TEA uses 84 elemental species and thermodynamical data for more then 600 gaseous molecular species, and can adopt any initial elemental abundances.
Least Asymmetry finds the center of a distribution of light in an image using the least asymmetry method; the code also contains center of light and fitting a Gaussian routines. All functions in Least Asymmetry are designed to take optional weights.
BART implements a Bayesian, Monte Carlo-driven, radiative-transfer scheme for extracting parameters from spectra of planetary atmospheres. BART combines a thermochemical-equilibrium code, a one-dimensional line-by-line radiative-transfer code, and the Multi-core Markov-chain Monte Carlo statistical module to constrain the atmospheric temperature and chemical-abundance profiles of exoplanets.
MC3 (Multi-core Markov-chain Monte Carlo) is a Bayesian statistics tool that can be executed from the shell prompt or interactively through the Python interpreter with single- or multiple-CPU parallel computing. It offers Markov-chain Monte Carlo (MCMC) posterior-distribution sampling for several algorithms, Levenberg-Marquardt least-squares optimization, and uniform non-informative, Jeffreys non-informative, or Gaussian-informative priors. MC3 can share the same value among multiple parameters and fix the value of parameters to constant values, and offers Gelman-Rubin convergence testing and correlated-noise estimation with time-averaging or wavelet-based likelihood estimation methods.
MARGE (Machine learning Algorithm for Radiative transfer of Generated Exoplanets) generates exoplanet spectra across a defined parameter space, processes the output, and trains, validates, and tests machine learning models as a fast approximation to radiative transfer. It uses BART (ascl:1608.004) for spectra generation and modifies BART’s Bayesian sampler (MC3, ascl:1610.013) with a random uniform sampler to propose models within a defined parameter space. More generally, MARGE provides a framework for training neural network models to approximate a forward, deterministic process.
HOMER (Helper Of My Eternal Retrievals) is a machine-learning-accelerated Bayesian inverse modeling code. Given some data and uncertainties, the code determines the posterior distribution of a model. HOMER uses MC3 (ascl:1610.013) for its MCMC; its forward model is a neural network surrogate model trained by MARGE (ascl:2003.010). The code produces plots of the 1D marginalized posteriors, 2D pairwise posteriors, and parameter history traces, and can also overplot the 1D and 2D posteriors for comparison with another posterior. HOMER computes the Bhattacharyya coefficient to compare the similarity of two 1D marginalized posteriors.