01. The Challenge
The Transiting Exoplanet Survey Satellite (TESS) monitors millions of stars, but in dense stellar fields, light from nearby stars ("crowding") contaminates the target aperture. The TESS pipeline estimates this contamination using a metric called CROWDSAP (\( c_p \)).
However, for faint targets or complex scenes, this metric can be inaccurate, leading to errors in critical astrophysical measurements like exoplanet transit depths. We needed a way to empirically validate these values across the entire sky.
02. Technical Methodology
Flux Recalibration Algorithm
We leveraged White Dwarfs (WDs) as calibrators because they are intrinsically stable. By anchoring the observed flux to the expected flux derived from the TESS Input Catalog (TIC), we derived a "Ground Truth" crowding metric (\( c_{est} \)) to compare against the pipeline.
// Recalibration Formula
c_est = c_p * ( F_expected / F_observed )// Discrepancy Ratio
R = c_p / c_estData Sample
Analyzed 8,132 White Dwarfs across 33,396 sector observations using Python and TESS pipeline data headers.
Outlier Detection
Implemented an iterative 5-sigma clipping algorithm to identify statistically significant failures in the pipeline processing.
03. Key Findings
While the pipeline is generally reliable, we identified ~200 specific failure modes where CROWDSAP was grossly inaccurate. These fell into four distinct categories:
- 1 Extremely Faint Targets: Near the detection limit, background noise is misattributed as source flux.
- 2 Intrinsic Variability: Cataclysmic variables or binaries that violate the stable flux assumption.
- 3 Saturation Bleed: Nearby bright stars saturating the detector and bleeding charge into the target aperture.
- 4 Transient Artifacts: Solar system objects (asteroids) passing through the aperture.
Project Resources
This project has been concluded. The codebase and related analysis notebooks are available for review.
Analysis CodeDr. Keaton Bell
Nikoo Hosseininezhad