RATS - Revealing Atmospheres with Transmission Spectra

Purpose:

• This is a code for analysis of high-resolution transmission spectra. Currently, implemented instruments are HARPS and ESPRESSO. NIRPS DRS pipeline uses the same DRS as these two instruments, and while untested, the loading functions should work correctly.

  • Adaptation for other instrument is mainly about:
    1. Loading functions and formating spectra in RATS format.
    2. Instrument-specific corrections
    3. Checking the tree-directory setup works properly.

• The goal of this pipeline is to automatize most of the tasks when reducing the data without user input, making it possible to quickly setup multiple datasets. This is done by generalized template for transmission spectroscopy (RM analysis to be implemented), which takes as input:

  • Filename location as downloaded from DACE. No other ways of downloading data have been tested. RATS as first step when opening data creates a organized tree directory with all the data, which is then assumed by the pipeline.

• This package is still work in progress.

• Example usage:

  • Template_transmission_spectroscopy.py shows a typical pipeline to reduce transmission spectroscopy HARPS and ESPRESSO data of a transit.
  • Template_rossiter_mclaughlin_effect.py shows a typical pipeline to reduce RM effect using HARPS and ESPRESSO spectrographs, using the "Revolutions" method (Bourrier et al. 2021)
  • TODO: Create a script to run in terminal for automatic setup of these.

Before use:

• Few modules are depending on external libraries, that need further setup. Generally, this includes filepaths to the external libraries.
• TODO: The setup will be moved to singular file

molecfit:

• Before use:
  • Few adjustment for pathing is needed for run_molecfit_all. In particular, connection to the esorex recipe needs to be properly established.
  • The rest of the pathing should be setup automatically.

petitRADTrans:

• Before use:
  • Few adjustment for pathing needs to be done for petitRADtrans to be usable
  • The main importing one is location of high-resolution line lists, through the OPACITY_LIST_LOCATION variable in the rats.modeling_CCF.py file.

LDCU:

• Before use:
• A filepath to the code main directory needs to be provided.

StarRotator:

• Before use:
  • A filepath to main directory of the code needs to be provided.
  • TODO: Implement the code so StarRotator is loaded as external library, instead of being within the RATS package

To be done:

• Upper-limits calculation for non-detection
• Detection functions (Fitting, significance calculations)
• CCF functions
• RM + CLV simulation (for now StarRotator can be used)
• Clean up of plots functions
• RM Revolutions technique for characterization of RM effect

Feedback:

• Please provide any feedback to Michal Steiner (Michal.Steiner@unige.ch) or through the issues interface on GitHub.