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Young exoplanets and their corresponding host stars are fascinatinglaboratories for constraining the timescale of planetary evolution andplanet-star interactions. However, because young stars are typically much moreactive than the older population, in order to discover more young exoplanets,greater knowledge of the wide array of young star variability is needed. HereKohonen Self Organising Maps (SOMs) are used to explore young star variabilitypresent in the first year of observations from the Transiting Exoplanet SurveySatellite (TESS), with such knowledge valuable to perform targeted detrendingof young stars in the future. This technique was found to be particularlyeffective at separating the signals of young eclipsing binaries and potentialtransiting objects from stellar variability, a list of which are provided inthis paper. The effect of pre-training the Self-Organising Maps on knownvariability classes was tested, but found to be challenging without asignificant training set from TESS. SOMs were also found to provide anintuitive and informative overview of leftover systematics in the TESS data,providing an important new way to characterise troublesome systematics inphotometric data-sets. This paper represents the first stage of the widerYOUNGSTER program, which will use a machine-learning-based approach toclassification and targeted detrending of young stars in order to improve therecovery of smaller young exoplanets.

monthly notices of the royal astronomical society

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