I. V. Kulyk | Searcharxiv

arXiv2026

Visual inspection of potential exocomet transits identified through machine learning and statistical methods

In this work, we explore several ways to detect possible exocomet transits in the TESS (The Transiting Exoplanet Survey Satellite) light curves. The first one has been presented in our previous work, a machine learning approach based on the Random Forest algorithm. It was trained on asymmetric transit profiles calculated as a result of the modelling of a comet transit, and then applied to real star light curves from Sector 1 of TESS. This allowed us to detect 32 candidates with weak and non-periodic brightness dips that may correspond to comet-like events. The aim of this work is to analyse the events identified by the visual inspection to make sure that the features detected were not caused by instrumental effects. The second approach to detect possible exocomet transits, which is proposed, is an independent statistical method to test the results of the machine learning algorithm and to look for asymmetric minima directly in the light curves. This approach was applied to \b{eta} Pictoris light curves using TESS data from Sectors 5, 6, 32, and 33. The algorithm reproduced nearly all previously known events deeper than 0.03 % of the star flux, showing that it is efficient to detect shallow and irregular flux changes in the different sectors of the TESS data and at the different levels of noise. The combination of machine learning, visual inspection, and statistical analysis facilitates the identification of faint and short-lived asymmetric transits in photometric data. Although the number of confirmed exocomet transits is still small, the growing amount of observations points to their likely presence in many young planetary systems.

astro-ph.EP↗

arXiv2024

Hunting for exocomet transits in the TESS database using the Random Forest method

This study introduces an approach to detecting exocomet transits in the dataset of the Transiting Exoplanet Survey Satellite (TESS), specifically within its Sector 1. Given the limited number of exocomet transits detected in the observed light curves, creating a sufficient training sample for the machine learning method was challenging. We developed a unique training sample by encapsulating simulated asymmetric transit profiles into observed light curves, thereby creating realistic data for the model training. To analyze these light curves, we employed the TSFresh software, which was a tool for extracting key features that were then used to refine our Random Forest model training. Considering that cometary transits typically exhibit a small depth, less than 1% of the star's brightness, we chose to limit our sample to the CDPP parameter. Our study focused on two target samples: light curves with a CDPP of less than 40 ppm and light curves with a CDPP of up to 150 ppm. Each sample was accompanied by a corresponding training set. This methodology achieved an accuracy of approximately 96%, with both precision and recall rates exceeding 95% and a balanced F1-score of around 96%. This level of accuracy was effective in distinguishing between 'exocomet candidate' and 'non-candidate' classifications for light curves with a CDPP of less than 40 ppm, and our model identified 12 potential exocomet candidates. However, when applying machine learning to less accurate light curves (CDPP up to 150 ppm), we noticed a significant increase in curves that could not be confidently classified, but even in this case, our model identified 20 potential exocomet candidates. These promising results within Sector 1 motivate us to extend our analysis across all TESS sectors to detect and study comet-like activity in the extrasolar planetary systems.

astro-ph.EP↗