Guidelines

Good practices

ForMoSA is a code that handles multiple inputs to produce multiple outputs, considering various models and algorithms. That is why we recommend organizing the working environment according to the following architecture:

~/YOUR/PATH/formosa_desk/
├── atm_grids/
├── project/
│   └── homo_sample/
│      ├── adapted_grid/
│      └── targetname/
│         ├── data/
│         └── results/
├── (ForMoSA/)
├── (PyMultiNest/)
└── (MultiNest/)

• atm_grids contains the native grids formatted with xarray that can be downloaded below or generated upon request.

• project contains the characterization you want to perform in the context of a project that includes different observations or tests.

• homo_sample contains the analysis of a homogeneous library of observations (same wavelength sampling). This allows you to use the same adapted grid for several homogeneous datasets.

• adapted_grid contains the grids of models adapted to a homogeneous library of observations (same wavelength sampling).

• targetname contains the analysis of a single dataset.

• data contains the dataset.

• results contains the results of the fit.

• (ForMoSA/) is the location of ForMoSA in case of an installation with GitHub.

• (PyMultiNest/) is the location of PyMultiNest in case of an installation with GitHub.

• (MultiNest/) is the location of MultiNest in case of an installation with GitHub.

Inputs

Observation(s)

The data need to be in a .fits file. It should have (at least) the following extensions:

• ‘WAV’ The wavelengths sampling

• ‘WAVE_UNIT’ The wavelengths unit

• ‘FLX’ The corresponding flux

• ‘ERR’ or ‘COV’ The corresponding flux error (or covariance matrix)

• ‘RES’ The corresponding spectral resolution given for each wavelength

• ‘FAC’ The observatory where the data come from (need to be consistent with VOSA for photometric points).

• ‘INS’ The instrument used to obtain the data (need to be consistent with VOSA for photometric points).

• ‘FILT’ The photometric filter used in case of photometric point (need to be consistent with VOSA)

Note

All of these extensions need to be numpy arrays of the same length.

Learn more about how to format your observation(s):

• Observation format

ForMoSA enable you to invert multiple datasets observed with different instruments. In that case, each dataset should be defined separately, in different files (data_1.fits, data_2.fits, ect…), and should be saved in the subdirectory data

Atmospheric grids

You now need an atmospheric grid on which to run your inversion.

This is the list of the publically available grids which we have formated for ForMoSA. Download the grid you want to use by clicking over it’s name. Ideally, save it inside the atm_grids/ subdirectory. If you need to generate a grid which is not in this list, please contact us. We can generate custom grids under request.

• ATMO from M.W. Phillips et al. 2020

• BT-Settl from Allard et al. 2013

• ExoREM from B. Charnay et al. 2018

Learn more about the format of the grids used in ForMoSA:

• Grids format

Configuration file

Finally, you need to prepare a configuration file.

This file (config.ini) allows you to communicate with ForMoSA.

Learn how to set it up in various cases:

• config.ini format

Demos

In this section, we detail the different steps required to perform an end-to-end analysis of spectroscopic and photometric data with ForMoSA.

The first example focuses on the inversion of the medium-resolution spectrum of AB Pic b obtained in the K band with the instrument SINFONI at the VLT.

• One medium-resolution spectrum : AB Pic b

The second example considers only photometric points of VHS 1256 b.

• Photometric points : VHS 1256 b

The third example combines photometric and spectroscopic information on the atmosphere of 51 Eri b observed with different instruments.

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