TESS Aperture Photometry Pollution eStimator

Note: this module is not yet fully developed. For commercial use, please wait for v1.0.0. It is expected to be in development for at least another month.

Description

A python module that calculates a contamination factor in the aperture region of a tpf based on the relative flux of the background stars and the target.

Prerequisites

Mandatory:

• numpy (2.4.2): to make all calculations
• scipy (1.17.1): to integrate the gaussian function
• astropy (8.0.1): to build data used in this module
• astroquery (0.4.11): to find stars in the target's region

Optional:

• lightkurve (2.6.0): to fetch tpf files (unless they already exist locally)
• matplotlib (3.10.8): to plot the result (shows aperture mask with a caption at the bottom)

A future version will include minimum working versions (these are the only tested so far)

Running the module

A tpf is needed to run this module. If no tpf is supplied, the default is a file with HAT-P-1 (the example picture above). So, here is the minimal example:

import tessapps as tapps

tapps.run()

>>> [0.57675, 0.00191]

The first number is the contamination factor, and the second is its error. Here is a more realistic example with CoRoT-1 (also in the example images):

import lightkurve as lk
import tessapps as tapps

tpf_CoRoT_1 = lk.search_targetpixelfile('CoRoT 1')[1].download()
contamination, error = tapps.run(tpf_CoRoT_1, result=True)

>>> The background contributes (2.249 ± 0.020)% of the total flux in the aperture region.

Alternatively, from a terminal:

tapps --tpf tpf_file
python3 -m tessapps --tpf tpf_file

And also:

tapps --tpf tpf_file --result --sigma=1

File Organization

Files in /src:

src
└── tessapps
    ├── __init__.py
    ├── __main__.py
    ├── main.py
    ├── tpf_to_nearby_stars.py
    ├── nearby_stars_to_data.py
    └── data_to_contamination.py

2 directories, 6 files

File description

• __init__.py: makes it so that the user can run tessapps.run(...)
• __main__.py: executes main.py
• main.py: the file that runs all other relevant files
• tpf_to_nearby_stars.py: produces a list of stars near the target star only using the tpf file
• nearby_stars_to_data.py: builds a data table containing all relevant information about stars, in a format readable by this module
• data_to_contamination.py constructs the gaussian fields and calulates the contamination filter

Possible Issues

• ConnectionResetError: happens sometimes due to urllib3. Run again and it should compile normally.

Authors

Leonidas Liponis

Links

Codeberg page: https://codeberg.org/leolip128/tessapps

PyPI page: https://pypi.org/project/tessapps/

Email (for contact): lliponis (at) physics.auth.gr

AI Disclosure

No LLMs were used in the making of this project. All code was generated and verified by humans, so it will only contain human-made mistakes. All commits on github made by bots will be refused, and commits made by humans will be checked: published code should be fully understandable by the authors.

To-Do list

• add description
• specify dependencies' versions
• add options to run the module
• redo examples since they reference the flux instead of the contamination factor
• add the scoring function
• add more stuff in this to-do list

Notes

Examples

The default tpf is lk.search_targetpixelfile("HAT-P-1", mission="TESS", sector=84, exptime=120)[0].download(), with [0] specified for reproducability (there is only one such file as of October 2026). HAT-P-1 has a star directly next to it with similar brightness, so it is a good default file as it is expected to find a contamination factor of about 0.5 (actual: 0.42).

All examples provided were generated with the following tpfs:

• HAT-P-1: lk.search_targetpixelfile("HAT-P-1", mission="TESS", sector=84, exptime=120)[0].download()
• WASP-12: lk.search_targetpixelfile("WASP-12", mission="TESS", sector=20, exptime=120).download()
• TOI-122: lk.search_targetpixelfile("TOI-122", mission="TESS", exptime=1800).download()
• AUMic: lk.search_targetpixelfile('20:45:09.5323 -31:20:27.241')[4].download()
• 55-Cnc: lk.search_targetpixelfile('55 Cnc')[6].download()
• CoRoT-1: lk.search_targetpixelfile('CoRoT 1')[1].download()
• CoRoT-10: lk.search_targetpixelfile('19:24:15.2868 +00:44:46.003')[1].download()

The caption on each example was edited manually to show the name of each star.

Other

The directory in File Organization has its files sorted manually.