YOUNG Star detrending for Transiting Exoplanet Recovery (YOUNGSTER) II: Using Self-Organising Maps to explore young star variability in Sectors 1-13 of TESS data

no code implementations • 31 Jan 2022 • Matthew P. Battley, David J. Armstrong, Don Pollacco

This technique was found to be particularly effective at separating the signals of young eclipsing binaries and potential transiting objects from stellar variability, a list of which are provided in this paper.

Exoplanet Validation with Machine Learning: 50 new validated Kepler planets

no code implementations • 24 Aug 2020 • David J. Armstrong, Jevgenij Gamper, Theodoros Damoulas

Over 30% of the ~4000 known exoplanets to date have been discovered using 'validation', where the statistical likelihood of a transit arising from a false positive (FP), non-planetary scenario is calculated.

BIG-bench Machine Learning

Identifying Exoplanets with Deep Learning III: Automated Triage and Vetting of TESS Candidates

2 code implementations • 4 Apr 2019 • Liang Yu, Andrew Vanderburg, Chelsea Huang, Christopher J. Shallue, Ian J. M. Crossfield, B. Scott Gaudi, Tansu Daylan, Anne Dattilo, David J. Armstrong, George R. Ricker, Roland K. Vanderspek, David W. Latham, Sara Seager, Jason Dittmann, John P. Doty, Ana Glidden, Samuel N. Quinn

We apply our model on new data from Sector 6, and present 335 new signals that received the highest scores in triage and vetting and were also identified as planet candidates by human vetters.

Earth and Planetary Astrophysics