TESS Lightcurve Preprocessing
SkillDev toolsLoad TESS text-format light curves and prepare them for transit detection by applying quality flag filtering, NaN removal, outlier rejection, normalization, and Savitzky-Golay flattening to remove stellar variability.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the TESS Lightcurve Preprocessing skill
What this skill tells your AI
The instructions your AI receives, as published by openlair/openskill in tasks-evolved/exoplanet-detection-period/environment/skills/evo-tess-lightcurve-preprocessing/SKILL.md and read by ahel’s review.
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-tess-lightcurve-preprocessing/scripts')
from utils import load_tess_lightcurve, filter_quality_and_nans, remove_outliers, estimate_rotation_period, flatten_lightcurve
time, flux, flux_err = load_tess_lightcurve('/root/data/tess_lc.txt')
time, flux, flux_err = filter_quality_and_nans(time, flux, flux_err, quality)
time, flux, flux_err = remove_outliers(time, flux, flux_err, sigma=5)
rot_p = estimate_rotation_period(time, flux)
time_f, flux_f = flatten_lightcurve(time, flux, window_length=0.5)
Key rules
- Drop quality flag != 0
- Drop NaN/inf values in flux
- 5-sigma outlier clipping iteratively
- Use Savitzky-Golay filter with window length sized between transit duration (~hours) and stellar rotation (~days)
- Best to use lightkurve's
flatten()method which uses Savitzky-Golay
Signals
- GitHub stars
- 89
- Forks
- 4
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
evo-tess-lightcurve-preprocessing- Source
- github.com/openlair/openskill