Codes

python   BayEsian infErence with spaTial Regularization of nOisy multi-line ObservaTion mapS (BEETROOTS), Pierre Palud   pypi-beetroots   docs-beetroots   repo-beetroots

Python package that performs Bayesian inference of physical parameters from multispectral-structured cubes with a dedicated sampling algorithm. Thanks to this sampling algorithm, beetroots provides maps of credibility intervals along with estimated maps.

Related publication:   paper   Palud et al (2025)

python   Informative Variables (InfoVar), Lucas Einig   pypi-infovar   docs-infovar   repo-infovar

This package provides tools to efficiently study the usefulness of variables on data of interest

Related publication:   paper   Einig, Palud et al (2024)

python   Neural Network-Based Model Approximation (NNBMA), Lucas Einig, Pierre Palud   pypi-nnbma   docs-nnbma   repo-nnbma

Python package that handles the creation and the training of neural networks to approximate numerical models. It was originally designed and used to derive an approximation of the Meudon PDR code, a complex astrophysical numerical code.

Related publication:   paper   Palud, Einig et al (2023)

python   Radio AstroNomy Cubes Handler for data science (RANCH), Lucas Einig   pypi-ranch   docs-ranch   repo-ranch

An API to easily handle radio astronomy FITS files with Python.

python   IRAM 30-meter EMIR observations informativity (Infobs), Lucas Einig   docs-infobs   repo-infobs

This package implements tools to quantitatively estimate the usefulness of spectral line observations for estimating physical conditions. It provides a tool for simply reproducing observations made at IRAM 30-meter millimeter-wave telescope coupled with the EMIR receiver. Other instruments can also be simulated.

Line intensity predictions are made using a neural network emulation of the Meudon PDR code. This emulator enables a thousand predictions to be made in around 10 ms on a laptop, with an average error of less than 5%.

Related publication:   paper   Einig, Palud et al (2024)

python   BeehiveNet, Lucas Einig   docs-beehivenet   repo-beehivenet

Python package for defining, training and making predictions with a machine learning model composed of several specialist artificial neural networks. This type of model was originally designed to infer astrophysical parameters in a robust and interpretable way, using neural networks specialized to a specific physical regime.

python   LaTeX ISM emission lines, Lucas Einig, Pierre Palud   repo-latexlines

This code implements a conversion of emission lines formatted according to the convention of the Meudon PDR code as well as a number of user-friendly methods for manipulating these lines.