IRAM 30-meter EMIR observations informativity

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%.

Installation

To install infobs:

Step 1: Create a virtual environment and activate it:

python -m venv .venv
source .venv/bin/activate

Step 2 : install with pip:

pip install -e .

Note 1: to deactivate the virtual env :

deactivate

**Note 2:**To delete the virtual environment:

rm -r .venv

Get started

To get started, check out the jupyter notebooks presented in the examples folder.

Tests

To test, run:

python -m pytest && coverage-badge -o coverage.svg -f

Documentation

cd docs
sphinx-apidoc -o . ../infobs
make html

Outputs are in docs/_build/html.

Associated packages

A&A paper repository: Reproduce the results in Einig et al. (2024)

InfoVar: Estimating informativity of features.

Neural network-based model approximation: handle the creation and the training of neural networks to approximate interstellar medium numerical models.

References

[1] Einig, L, Palud, P. & Roueff, A. & Pety, J. & Bron, E. & Le Petit, F. & Gerin, M. & Chanussot, J. & Chainais, P. & Thouvenin, P.-A. & Languignon, D. & Bešlić, I. & Coudé, S. & Mazurek, H. & Orkisz, J. H. & G. Santa-Maria, M. & Ségal, L. & Zakardjian, A. & Bardeau, S. & Demyk, K. & de Souza Magalhães, V. & Javier R. Goicoechea & Gratier, P. & V. Guzmán, V. & Hughes, A. & Levrier, F. & Le Bourlot, J. & Darek C. Lis & Liszt, H. S. & Peretto, N. & Roueff, E & Sievers, A. (2024). Quantifying the informativity of emission lines to infer physical conditions in giant molecular clouds. I. Application to model predictions. Astronomy & Astrophysics. 10.1051/0004-6361/202451588.

[2] Palud, P. & Einig, L. & Le Petit, F. & Bron, E. & Chainais, P. & Chanussot, J. & Pety, J. & Thouvenin, P.-A. & Languignon, D. & Beslić, I. & G. Santa-Maria, M. & Orkisz, J.H. & Ségal, L. & Zakardjian, A. & Bardeau, S. & Gerin, M. & Goicoechea, J.R. & Gratier, P. & Guzman, V. (2023). Neural network-based emulation of interstellar medium models. Astronomy & Astrophysics. 10.1051/0004-6361/202347074.