Localizing Sources of Variability in Crowded TESS Photometry

TL;DR

This paper introduces a novel method and open-source tool to accurately localize variable sources within crowded TESS photometry, addressing the challenge of source blending due to TESS's large pixel scale.

Contribution

We develop a new technique that localizes variability sources to less than a fifth of a pixel using frequency analysis and pixel response models, improving source attribution in TESS data.

Findings

Method achieves localization accuracy better than 0.2 pixels.

Open-source Python package TESS Localize available for community use.

Systematics in residuals characterized to improve localization reliability.

Abstract

The Transiting Exoplanet Survey Satellite (TESS) has an exceptionally large plate scale of 21"/px, causing most TESS light curves to record the blended light of multiple stars. This creates a danger of misattributing variability observed by TESS to the wrong source, which would invalidate any analysis. We develop a method that can localize the origin of variability on the sky to better than one fifth of a pixel. Given measured frequencies of observed variability (e.g., from periodogram analysis), we show that the corresponding best-fit sinusoid amplitudes to raw light curves extracted from each pixel are distributed the same as light from the variable source. The primary assumption of this method is that other nearby stars are not variable at the same frequencies. Essentially, we are using the high frequency resolution of TESS to overcome limitations from its low spatial resolution. We have implemented our method in an open source Python package, TESS Localize (github.com/Higgins00/TESS-Localize), that determines the location of a variable source on the sky given TESS pixel data and a set of observed frequencies of variability. Our method utilizes the TESS Pixel Response Function models, and we characterize systematics in the residuals of fitting these models to data. Given the ubiquity of source blending in TESS light curves, verifying the source of observed variability should be a standard step in TESS analyses.