This repository contains code to select targets for follow-up observations based on candidates from the TESS alerts platform (https://tev.mit.edu). It can be applied to a list of alerts to evaluate their visibility at a specified site and filter them for various parameters.
A typical use-case would be to answer the question: "Which of these candidates are M dwarfs and observable from my observatory and when should I target them?"
- Run
jupyter notebook TESSalerts.ipynb - Adapt the program parameters to your needs
- Follow the steps in the notebook
The code was written for Python 3.6 and makes use of the following packages:
- numpy
- pandas
- matplotlib
- astropy
- astroplan
- astroquery
- jupyter (optional)
- openpyxl (optional)
To facilitate readability and maintainability, the pipeline is quite granularized with every step represented in a python function. These functions are equipped with docstrings, live in observability.py, and can be called independently. A Jupyter notebook TESSalerts.ipynb presents an example pipeline that uses these functions to perform all steps from reading a list of TESS alerts to arriving at a table of suitable targets.
A number of plots are supposed to help with prioritizing those targets, for example:
In case the IERS (International Earth Rotation and Reference Systems) server is inaccessable, you can temporarily disable auto-downloading of recent IERS tables,
>>> from astropy.utils import iers
>>> iers.conf.auto_download = False
or set the max age parameter to a higher value (or None)
>>> iers.conf.auto_max_age = None
Note that this can give you inaccurate time estimates! For more info, see https://docs.astropy.org/en/stable/utils/iers.html
The code in this repository was written by Martin Schlecker ([email protected]) with contributions by Paz Bluhm ([email protected]). It is being actively developed in an open repository, so if you have any trouble please raise an issue.