TESS Data for Asteroseismology: Photometry

TL;DR

This paper introduces an open-source pipeline for extracting high-quality light curves from TESS data across all observed stars and cadences, enhancing asteroseismic research capabilities.

Contribution

The paper presents a comprehensive pipeline that produces uniform light curves for all TESS targets, including those at longer cadences, filling a critical data gap for asteroseismology.

Findings

Pipeline successfully generates light curves for stars down to TESS magnitude 15.

The pipeline includes multiple photometric extraction methods, with aperture and halo photometry.

Noise metrics indicate high-quality data suitable for asteroseismic analysis.

Abstract

Over the last two decades, asteroseismology has increasingly proven to be the observational tool of choice for the study of stellar physics, aided by the high quality of data available from space-based missions such as CoRoT, Kepler, K2 and TESS. TESS in particular will produce more than an order of magnitude more such data than has ever been available before. While the standard TESS mission products include light curves from 120-sec observations suitable for both exoplanet and asteroseismic studies, they do not include light curves for the vastly larger number of targets observed by the mission at a longer 1800-sec cadence in Full Frame Images (FFIs). To address this lack, the TESS Data for Asteroseismology (T'DA) group under the TESS Asteroseismic Science Consortium (TASC), has constructed an open-source pipeline focused on producing light curves for all stars observed by TESS at all cadences, currently including stars down to a TESS magnitude of 15. The pipeline includes target identification, background estimation and removal, correction of FFI timestamps, and a range of potential photometric extraction methodologies, though aperture photometry is currently the default approach. For the brightest targets, we transparently apply a halo photometry algorithm to construct a calibrated light curve from unsaturated pixels in the image. In this paper, we describe in detail the algorithms, functionality, and products of this pipeline, and summarize the noise metrics for the light curves. Companion papers will address the removal of systematic noise sources from our light curves, and a stellar variability classification from these.