The TEQUILA catalog of variables in TESS full-frame images: Differential photometry light curves from the first two years of observations

TL;DR

This work addresses the challenge of extracting comprehensive time-domain information from TESS full-frame images by introducing TEQUILA, a difference image analysis–based catalog of over $6.1\times10^{6}$ light curves for variable point sources observed in the first two years of the mission. The pipeline builds high-S/N reference frames, performs iterative background removal, and applies PSF-matched image subtraction followed by fixed-aperture photometry to yield differential flux time series, subsequently flux-calibrated to produce FLUX measurements. A CNN-based classifier flags instrumental systematics and a Solar System object (SSO) flag helps separate astrophysical variability from non-astrophysical signals, with all light curves publicly accessible via MAST. The dataset, including multi-sector and CVZ entries, significantly expands the discovery space for variable stars, transients, and moving objects, and provides a versatile resource for statistical studies and follow-up analyses, with planned refinements and extensions to the extended mission.

Abstract

Stellar variability and transient events provide critical insights into astrophysics, accelerated by missions like CoRoT, Kepler, and K2. NASA's Transiting Exoplanet Survey Satellite (TESS) adds a unique combination of long baseline and all-sky coverage, though extracting light curves from full-frame images (FFIs) is challenging due to scattered light and blending. We processed TESS FFIs to produce TEQUILA (TESS quick-look and light curve analysis), a comprehensive catalog of variable point sources from the prime mission, enabling diverse studies without requiring raw data processing. We used difference image analysis, constructing reference images from quality-filtered FFIs for each CCD across sectors 1-26. Iterative subtraction mitigated systematics, and light curves were created using aperture photometry for sources varying in residual images. The pipeline yields over six million light curves, including stellar variables, transients, systematics, and moving objects. Approximately $6 \times 10^5$ span multiple sectors, with roughly $10^3$ from continuous viewing zones. We achieve median differential variability noise of $10^{-3}$ to $10^0$ for sources between 5.0 and 16.0 Tmag, while typical photometric RMS variability ranges from $10^{-2}$ to $10^1$. A convolutional neural network identifies light curves caused by instrumental noise, assigning a confidence score to each classification. To avoid confusion with astrophysical variables, we also flag light curves prompted by known Solar System objects (SSOs). All light curves are accessible via MAST as a High-Level Science Product. This catalog serves as a discovery tool for new variables; future work will refine methods and extend coverage to the TESS extended mission.

Figures (11)

• Figure 1: Schematic diagram showing our step-by-step photometry processing method.
• Figure 2: Distribution of variable sources light curves extracted in each sector of the TESS prime mission.
• Figure 3: On-sky plots of the coordinates for the extracted variable point sources in sectors 5 and 7.
• Figure 4: Extracted sources with multisector observation and their distributions for the first two years of TESS observation.
• Figure 5: Left: On-sky plot of the extracted sources for CCD 4 of Camera 1 of sector 5. The streaks represent paths of Solar System objects as they cross the telescope's field of view during the observation. Orange highlights the location of the three light curves in the right panel. Right: Three light curves whose extraction was prompted by the asteroid 339 Dorothea (A892 SC) crossing the FOV at three consecutive epochs. The spikes represent the increase in flux due to the asteroid crossing the on-sky location of the light curves, where otherwise there is no light source.