TESS-Gaia Light Curve (TGLC) Data

• TESS-Gaia Light Curve (TGLC) data is a method combining TESS full-frame imaging with Gaia astrometric and photometric priors to yield precise, decontaminated light curves.
• The methodology employs forward modeling of the effective PSF with simultaneous background subtraction to achieve robust photometry even in crowded stellar fields.
• These high-quality light curves enable critical advances in exoplanet detection, asteroseismology, binary star analysis, and time-domain transient studies.

The TESS-Gaia Light Curve (TGLC) data product and its broader methodological landscape represent a major evolution in the extraction and scientific exploitation of photometric time series from wide-field, space-based imaging missions. By combining the high-precision, high-cadence, full-sky coverage of the Transiting Exoplanet Survey Satellite (TESS) with the exquisite astrometry and all-sky photometry of Gaia, TGLC and its family of approaches enable empirical measurement of stellar properties, robust variability characterization, and systematic removal of blending in crowded fields. These innovations underpin critical advances in exoplanet discovery, asteroseismology, stellar astrophysics, and time-domain transient science across a range of stellar populations.

1. Methodological Foundations: Construction and Processing of TGLC Data

The TGLC pipeline for extracting high-quality light curves from TESS full-frame images (FFIs) is built on forward modeling of the TESS effective Point Spread Function (ePSF) within small (∼150×150 pixel) cutouts across each FFI (Han et al., 2023). The ePSF, produced by convolving the optical PSF with the detector response, is assumed constant across each cutout per cadence and is simultaneously fit to all pixel data, using stellar positions and magnitudes from Gaia Data Release 3 (DR3) as strong priors.

This approach explicitly includes:

• Simultaneous Background Modeling: A spatial model is fit per cutout per cadence, accounting for a flat term, linear gradients from stray light, and columnar detector artifacts.
• Linear Least Squares Solution: The observed pixel values P are modeled as

$P \simeq A F$

where A encodes oversampled ePSFs positioned using Gaia priors, and F contains the free parameters (ePSF, background terms). A weighting exponent $l \simeq 1.4$ is applied such that faint, less contaminated pixels contribute more strongly, optimizing for precision in crowded fields.

• Decontamination: Neighboring stars’ contributions are subtracted in the model, yielding “decontaminated” images for precise photometry.
• PSF and Aperture Light Curves: Both PSF-fitting (anchored by Gaia positions/magnitudes) and classic (3×3 pixel) aperture photometry are performed after decontamination. For many targets, a weighted combination outperforms each individual method (Han et al., 2023).

The forward model enables robust photometric extraction even under severe crowding, critical for densely populated clusters, the galactic plane, and extragalactic fields.

2. Essential Role of Gaia DR3 Astrometric and Photometric Priors

Gaia DR3 provides sub-milliarcsecond positional precision and millimagnitude-level broadband photometry for more than a billion sources. These priors serve two instrumental roles:

• Astrometric Anchoring: Each star’s position is fixed in the TGLC model, allowing for unambiguous allocation of flux across wide TESS pixels (21″/px).
• Photometric Constraint and Bandpass Transformation: Gaia’s G, G_BP, and G_RP fluxes are converted to TESS magnitudes via an empirically-calibrated color law:

$$T = G - 0.00523\, (G_{BP} - G_{RP})^3 + 0.08913\, (G_{BP} - G_{RP})^2 - 0.63392\, (G_{BP} - G_{RP}) + 0.03245$$

or a fallback $T = G - 0.430$ when no color data are available (Han et al., 2023). This allows accurate initialization of expected fluxes and calibration of extracted light curves, ensuring that “absolute” light curves (not just relative flux) are delivered per target.

By encoding the astrophysical and positional context from Gaia, the TGLC pipeline minimizes contamination and calibration uncertainties, allowing for true multi-source spectral-temporal analysis from TESS data.

3. Photometric Performance, Precision Metrics, and Noise Characterization

The TGLC product demonstrates photometric precision matching or exceeding TESS pre-launch requirements through all sampled FFIs and down to faint magnitudes (TESS mag ∼16) (Han et al., 2023). The median photometric precision (30-min cadence) for TGLC PSF or TGLC Weighted light curves is ≲2% at T=16, even in highly blended fields, owing to the use of Gaia positional priors and simultaneous decontamination.

Precision is quantified using the robust estimator:

$$\text{precision} = \frac{1.48}{\sqrt{2}} \, \mathrm{median}(|\Delta \text{flux}|)$$

where $|\Delta \text{flux}|$ is the set of consecutive flux differences in the light curve. For bright stars, TGLC approaches the TESS photon noise floor; for fainter targets, crowding is the limiting factor, but the pipeline suppresses contamination effectively via PSF modeling.

Comparative studies across a range of science cases—exoplanet transits, eclipsing binaries, RR Lyrae, and rapid stellar rotators—show that TGLC light curves provide enhanced signal recovery and consistent amplitude/frequency estimates relative to competing FFI pipelines.

4. Scientific Applications and Impact Across Stellar Populations

TGLC data underpin a range of advanced studies leveraging the wide sky coverage and high-cadence monitoring of TESS, integrated with the depth of Gaia astrometric and photometric data. Key applications include:

• Exoplanet Detection and Characterization: The ability to retrieve high-precision, decontaminated light curves for faint or blended sources enables transit detection around stars previously inaccessible, expands potential host populations (including crowded cluster and galactic plane members), and ensures reliability of derived planetary parameters (Han et al., 2023).
• Precision Asteroseismology: The TGLC approach supports ensemble asteroseismic studies for variable star classification, period, and amplitude extraction (e.g., γ Dor, δ Scuti, β Cephei, and SPB stars) (Hey et al., 2 May 2024, Fritzewski et al., 12 Aug 2024, Mani et al., 26 Aug 2025). The enhanced light-curve quality is key for frequency analysis, mode identification, and population-level modeling.
• Binary and Multiple Star Systems: High-fidelity light curves enable accurate modeling of eclipses (e.g., via PHOEBE, Wilson-Devinney, or MCMC-based approaches (Poro et al., 10 Feb 2025, Poro et al., 2021)), extraction of precise physical parameters when combined with Gaia distances, and robust classification using machine learning (Wang et al., 1 Apr 2025).
• Time-Domain Transients: The TGLC methodology ensures detection and accurate photometric characterization of rapid transients (e.g., optical afterglows of gamma-ray bursts, exotic variables, and rare eruptive young stellar objects) over TESS’s broad field (Fausnaugh et al., 2023, Hodapp et al., 2019).
• Cluster and Galactic Structure Science: Systematic light curve extraction for all Gaia-identified cluster members or field stars allows rotational evolution, binarity, and population studies across diverse age and metallicity ranges (Bouma et al., 2019, Doyle et al., 4 Mar 2024).

This breadth of applications is realized via the public release of TGLC products as MAST High-Level Science Products, and the “tglc” open-source codebase for customized extraction and analysis (Han et al., 2023).

5. Integration With Other Methods and Cross-Mission Synergy

TGLC light curves are specifically designed to enhance, rather than supplant, other data products and analysis methods. The synergy between TESS and Gaia extends beyond PSF-based light curve extraction:

• Empirical Determination of Stellar Properties: By linking TESS light curve granulation signals (surface gravity), Gaia parallaxes (distances), and SED-fitted bolometric flux, the TGLC methodology enables direct, “model-independent” measurement of stellar masses and radii—a foundation for both exoplanet science and calibration of stellar evolution models (Stassun et al., 2017).
• Transit Candidate Vetting: Joint use of Gaia epoch photometry and TESS light curves allows for resolution of source confusion in transit signals, rapid identification of background eclipsing binaries, and more efficient allocation of follow-up resources (Panahi et al., 2022).
• Machine Learning Classification: Cross-matching periodic signals from TESS with Gaia’s variable star catalog supports robust supervised learning approaches for variable star taxonomy and discovery of new astrophysical object classes (Wang et al., 1 Apr 2025).

TGLC thus operates as part of an integrated data ecosystem, leveraging cross-mission strengths to maximize scientific reliability and scope.

6. Limitations, Ongoing Improvements, and Future Prospects

Although TGLC achieves near–photon-limited precision for most targets, certain limitations persist:

• Extreme Crowding/Unresolved Blends: In star fields at the confusion limit of Gaia, or for sources below Gaia’s detection threshold, decontamination by PSF modeling becomes incomplete. Faint sources below the Gaia magnitude limit can introduce residual flux contamination.
• Artifacts and Systematics: The pipeline’s reliance on ePSF constancy within cutouts and the need for accurate background subtraction are susceptible to low-level instrumental artifacts or unmodeled background variability in some sectors.
• Color Transform Uncertainties: For intrinsically peculiar stars or those with missing color data, the bandpass conversion from Gaia to TESS magnitudes introduces additional uncertainty.
• Photometric Time Sampling: For sources with intrinsic variability on timescales shorter than the TESS FFI cadence (e.g., some δ Scuti or white dwarf pulsators), signal recovery can be incomplete, especially for targets without high-cadence stamps (Mani et al., 26 Aug 2025, Priyatikanto, 2022).

Ongoing improvements, such as higher-fidelity ePSF modeling, sector cross-calibration, and the integration of future Gaia data releases with more precise photometry and enhanced completeness, are expected to further suppress systematics and enable expanded sensitivity to low-amplitude and complex signals (Han et al., 2023).

The TGLC approach is extensible to future wide-field missions (e.g., PLATO), with applicability to forthcoming ultra-large time-domain surveys and deeper, all-sky variable star catalogs.

7. Data Access, Community Tools, and Outlook

All TGLC data for stars down to TESS mag ∼ 16 (and in crowded fields), including both PSF-derived and aperture photometry light curves, are available through MAST as a High Level Science Product (Han et al., 2023). The associated Python package “tglc” provides user-level, customizable access to the full methodology, supporting cutout selection, parameter tuning (aperture size, background model), and direct reprocessing for special cases or science targets.

This high degree of openness, modularity, and integration ensures that TGLC data products serve as both an astrophysical resource and a methodological benchmark for the extraction and exploitation of precise light curves from crowded, wide-field CCD imaging.

In summary, the TESS-Gaia Light Curve (TGLC) methodology and products instantiate a robust, PSF-based approach to light curve extraction that leverages Gaia’s astrometric and photometric power to recover, calibrate, and deblend TESS photometric time series at scale. The resultant data products have demonstrably advanced stellar, exoplanetary, and time-domain astrophysics and represent a foundational pillar in the modern era of space-based survey astronomy.