TESSELLATE: Piecing Together the Variable Sky with TESS

Abstract We present TESSELLATE , a dedicated pipeline for performing an untargeted search documenting all variable phenomena captured by the TESS space telescope. Building on the TESSreduce difference imaging pipeline, TESSELLATE extracts calibrated and reduced photometric data for every full frame image in the TESS archive. Using this data, we systematically identify transient, variable, and nonsidereal signals across timescales ranging from minutes to weeks. The high cadence and wide field of view of TESS enables us to conduct a comprehensive search of the entire sky to a depth of ∼17 m i . Based on the volumetric rates for known fast transients, we expect there to be numerous fast blue optical transients and gamma-ray burst afterglows present in the existing TESS data set. Beyond transients, TESSELLATE can also identify new variable stars and exoplanet candidates and recover known asteroids. We classify events using machine learning techniques and the work of citizen scientists via the Zooniverse Cosmic Cataclysms project. Finally, we introduce the TESSELLATE Sky Survey: a complete, open catalog of the variable sky observed by TESS.

Authors

Institutions

Publication Details

Journal
The Astronomical Journal
Published
2025-09-01
DOI
https://doi.org/10.3847/1538-3881/adf21b
Citations
1
Primary Topic
Optical measurement and interference techniques
Type
article
Field-Weighted Citation Impact
1.28

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

TESSELLATE: Piecing Together the Variable Sky with TESS

Qinan Wang, Michele T. Bannister, A. Rest, James Freeburn et al.
1 citations
The Astronomical Journal
Optical measurement and interference techniques
1.28
article

TESSELLATE: Piecing Together the Variable Sky with TESS

Qinan Wang, Michele T. Bannister, A. Rest, James Freeburn, Jeff Cooke, Andrew Ridden-Harper, Ryan Ridden-Harper, Michael Fausnaugh, H. L. Roxburgh, Rebekah Hounsell, D. A. Coulter, Andrew C. Moore, Zachary G. Lane, Lancia Hubley, Brayden Leicester, Clarinda Montilla
article en
1 citations

Abstract

Abstract We present TESSELLATE , a dedicated pipeline for performing an untargeted search documenting all variable phenomena captured by the TESS space telescope. Building on the TESSreduce difference imaging pipeline, TESSELLATE extracts calibrated and reduced photometric data for every full frame image in the TESS archive. Using this data, we systematically identify transient, variable, and nonsidereal signals across timescales ranging from minutes to weeks. The high cadence and wide field of view of TESS enables us to conduct a comprehensive search of the entire sky to a depth of ∼17 m i . Based on the volumetric rates for known fast transients, we expect there to be numerous fast blue optical transients and gamma-ray burst afterglows present in the existing TESS data set. Beyond transients, TESSELLATE can also identify new variable stars and exoplanet candidates and recover known asteroids. We classify events using machine learning techniques and the work of citizen scientists via the Zooniverse Cosmic Cataclysms project. Finally, we introduce the TESSELLATE Sky Survey: a complete, open catalog of the variable sky observed by TESS.

The Astronomical JournalVol. 170(3)
Texas Tech University (US), Space Telescope Science Institute (US), Goddard Space Flight Center (US), Johns Hopkins University (US), University of Canterbury (NZ), Curtin University (AU), Université Bourgogne Franche-Comté (FR), ARC Centre of Excellence for Gravitational Wave Discovery (AU), International Centre for Radio Astronomy Research (AU), Swinburne University of Technology (AU), Massachusetts Institute of Technology (US), University of Maryland, Baltimore County (US)
National Aeronautics and Space Administration, Australian Government, New Zealand Government, Royal Society Te Apārangi, Swinburne University of Technology, State Government of Victoria, Goddard Space Flight Center, Marsden Fund
Openalex Percentile: Top 16%
Optical measurement and interference techniques
1.28
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.