Skip to content

Latest commit

 

History

History
55 lines (42 loc) · 2.01 KB

README.md

File metadata and controls

55 lines (42 loc) · 2.01 KB
Raw

Astro-Machines:

Machines Learn to Infer Stellar Parameters Just by Looking at a Large Number of Spectra

Here you can find the code and (pointers to) the data used in the paper: arxiv.org/abs/2009.12872 (DOI:10.1093/mnras/staa3540)

alt

Please refer to the main project page, to try out the 'sliders' and 'RETR-SPECT' interfaces.

How to use

  • Clone the repository to a local directory of your choice. e.g. astro-machines.

    • git clone https://github.com/NimSed/astro-machines.git
    • cd astro-machines

  • Extract the lists, metadata and labels for HARPS spectra:

    • cd lists
    • tar xfz list.tar.gz
    • cd ..

  • Play with the provided notebook to visualize the learned features and finally reproduce the plots in section 6 of the paper.

    • cd notebooks
    • jupyter-notebook

Optional -- to pass individual HARPS-like spectra through pretrained networks

  • Fetch pretrained models:

    • cd models
    • sh fetch_models.sh
    • cd ..

  • Test/infer on the provided sample.fits:

    • cd infer
    • python3 infer.py

You can of course use an arbitrarily chosen HARPS spectrum (in fits format). Just pass it to infer.py using the --fits_file argument.

Dependencies

pip3 install astropy