danielandreasen/odusseas

ODUSSEAS (Observing Dwarfs Using Stellar Spectroscopic Energy-Absorption Shapes):

A Machine-Learning Tool for the derivation of Teff and [Fe/H] of M Dwarf stars

IF YOU USE THIS TOOL IN YOUR RESEARCH, PLEASE CITE THE CORRESPONDING PAPER:

https://doi.org/10.1051/0004-6361/201937194

Usage

$ odusseas --help

 Usage: odusseas [OPTIONS] INPUT_SPECTRA

 Run ODUSSEAS with the arguments as listed below

╭─ Arguments ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ *    input_spectra      TEXT  [default: None] [required]                                                                                         │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --reference                                            [photometry|interferometry]                  choose the reference scale: 'photometry' for │
│                                                                                                     65 stars with Teff from Casagrande08 and     │
│                                                                                                     [Fe/H] from Neves12, or 'interferometry' for │
│                                                                                                     47 stars with Teff from Khata21 and Rabus19, │
│                                                                                                     and [Fe/H] from Neves12                      │
│                                                                                                     [default: interferometry]                    │
│ --regression                                           [linear|ridge|ridgecv|multitasklasso|multit  choose the ML model. Recommended: ridge      │
│                                                        askelasticnet ]                              [default: ridge]                             │
│ --rv-cor                  --no-rv-cor                                                               [default: rv-cor]                            │
│ --verbose                 --no-verbose                                                              [default: no-verbose]                        │
│ --skip-ew-measurements    --no-skip-ew-measurements                                                 If this step is already done, then it can be │
│                                                                                                     skipped in further analysis, as it is a but  │
│                                                                                                     slow                                         │
│                                                                                                     [default: no-skip-ew-measurements]           │
│ --help                                                                                              Show this message and exit.                  │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Example

$ odusseas 1Dfilelist.dat

This should run the example provided with the code. It should output the results from 5 spectra in the folders results/ (the pseudo EWs measured for each spectrum), Model_Prediction_Plots (the plots of the machine-learning predictions) and Parameter_Results.dat (the calculated atmospheric parameters for each star).

Installation

It is recommended to install this package in a virtual environment following with the command

$ pip install git+https://github.com/AlexandrosAntoniadis/ODUSSEAS

A recent version of python should work, but do let us now if you have any issues installing and running the code.

Documentation

odusseas is the code we run.

We select the methods by which the reference parameters have been derived, using the setting reference. This can be: photometry which uses as reference dataset 65 stars with photometric scales of Teff by Casagrande et al (2008) and [Fe/H] by Neves et al (2012), or interferometry (regarded as the new version of ODUSSEAS) which uses as reference dataset 47 stars with interferometry-based Teff by Khata et al (2021) and Rabus et al (2019) and [Fe/H] derived with the method by Neves et al (2012) using the updated parallaxes from Gaia DR3. We can set the regression type using the setting regression. This can be: ridge (recommended), ridgecv, linear, multitasklasso, multitaskelasticnet We can also choose to do r.v. correction to our spectra if they are shifted, by setting the rv_cor option.

Input: inside a folder with the path "spectra/newstars/", there should be the fits files of the 1D spectra of the unknown stars. Their filepaths should be written in a text in same format as 1Dfilelist.dat, and next to them the resolution of each spectrum. See example below:

spectra/newstars/starA.fits 115000
spectra/newstars/starB.fits 94600
spectra/newstars/starC.fits 75000

Output: A text file named Parameter_Results.dat is created. It contains the average values of [Fe/H] and Teff after 100 M.L. runs for each star, along with their dispersion, the mean absolute errors of the models that predicted them, the wide error budget (after taking into consideration the intrinsic uncertainties of the reference parameters into the machine learning process), and the machine-learning scores.

Demo set: 1D spectra of stars from 5 different spectrographs with different resolutions and respective HARPS datasets for them are provided to use our tool. For comparison, the reference values of the respective HARPS spectra are the following: Using the scales of Casagrande08 and Neves12: Gl846 = -0.08 & 3682 ; Gl514 = -0.13 & 3574 ; Gl908 = -0.38 & 3587 ; Gl674 = -0.18 & 3284 and for the HARPS star outside the reference HARPS dataset Gl643 = -0.26 & 3102 by Neves et al (2014). Using the scales of Khata21 & Rabus19 and updated Neves12: Gl846 = -0.07 & 3810 ; Gl514 = -0.15 & 3671 ; Gl908 = -0.40 & 3475 ; Gl674 = -0.19 & 3409 ; Gl643 = -0.32 & 3243.

We already provide precomputed pseudo EWs for a range of spectral resolutions used in popular spectrographs. For completeness, the repository also includes the code "HARPS_dataset.py", which can create a library of M dwarfs from our HARPS sample for any resolution we want to work at (the associated fits files are not uploaded). If you wish to create additional libraries or for any other question, please contact us at : [email protected].

Note: The structure of the repository and associated files should be kept as found, in order for the code to run properly.