TIC 020209388 Step-by-step instructions for transit encapsulation in light curves. The X and Y axes specify time (days) and normalized flux, respectively. Top panel: 1 — simulated transit profile, 2 — PDC_SAP normalized flux of stars from the sector 1 TESS dataset, both randomly selected. Bottom panel, 3 – light curve of star with encapsulated transit, 4 – magnified range of light curve near encapsulated transit. — astro-ph.EP
In this study, we explore several methods to detect possible exoplanet transits in the TESS (The Transiting Exoplanet Survey Satellite) light curve. The first is a machine learning approach based on the random forest algorithm that we introduced in our previous work.
It was trained on asymmetric transit profiles calculated as a result of comet transit modeling and applied to real stellar light curves from sector 1 of TESS. This allowed us to detect 32 candidates with weak, non-periodic brightness dips that may correspond to comet-like phenomena.
The purpose of this work is to analyze the events identified by visual inspection and to ensure that the detected features are not caused by instrument effects. The second proposed approach to detecting possible exomet passages is an independent statistical method that tests the results of machine learning algorithms and directly looks for asymmetric minima within the light curve.
This approach was applied to the Pictoris beta light curve using TESS data from sectors 5, 6, 32, and 33. This algorithm reproduces almost all previously known phenomena deeper than 0.03% of the star flux and has been shown to be efficient in detecting shallow and irregular flux changes in different sectors and different levels of noise in the TESS data.
A combination of machine learning, visual inspection, and statistical analysis facilitates the identification of faint asymmetric transits in photometric data. Although the number of confirmed exocomet transits is still small, the increasing number of observations indicates that exocomets may exist in many young planetary systems.
DV Dobricheva, IV Kulik, DR Karakuts, M.Yu. Vasilenko, Ya.V. Pavlenko, OS Shubina, IV Lukyanik
Comments: 14 pages, 14 figures, 30 references
Subject: Earth and Planetary Astrophysics (astro-ph.EP). Astrophysical Instruments and Methods (astro-ph.IM)
MSC class: 85A35, 85-08
ACM class: I.2.6; J.2
Quote: arXiv:2602.02701 [astro-ph.EP](or arXiv:2602.02701v1 [astro-ph.EP] for this version)
https://doi.org/10.48550/arXiv.2602.02701
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Related DOI:
https://doi.org/10.15407/knit2025.06.080
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Birthplace: Daria Dobricheva
[v1] Monday, February 2, 2026 19:12:44 UTC (1,167 KB)
https://arxiv.org/abs/2602.02701
astrobiology, interstellar,
