An algorithm for automatic identification of asymmetric transits in the TESS database

Currently, the Transiting Exoplanet Survey Satellite (TESS) searches for Earth-size planets around nearby dwarf stars. To identify specific weak variations in the light curves of stars, sophisticated data processing methods and analysis of the light curve shapes should be developed and applied. We r...

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Bibliographic Details
Published inProceedings of the International Astronomical Union Vol. 15; no. S364; pp. 264 - 266
Main Authors Vasylenko, M., Pavlenko, Ya, Dobrycheva, D., Kulyk, I., Shubina, O., Korsun, P.
Format Journal Article
LanguageEnglish
Published Cambridge, UK Cambridge University Press 01.10.2021
Subjects
Algorithms
Astronomy
Data processing
Data search
Dwarf stars
Extrasolar planets
Light
Light curve
Machine learning
Planet detection
Poster Paper
Space telescopes
Transit
Transits
exoplanets
exocomets
TESS
MAST
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Summary:Currently, the Transiting Exoplanet Survey Satellite (TESS) searches for Earth-size planets around nearby dwarf stars. To identify specific weak variations in the light curves of stars, sophisticated data processing methods and analysis of the light curve shapes should be developed and applied. We report some preliminary results of our project to find and identify minima in the light curves of stars collected by TESS and stored in the MAST (Mikulski Archive for Space Telescopes) database. We developed Python code to process the short-cadence (2-min) TESS PDCSAP (Pre-search Data Conditioning Simple Aperture Photometry) light curves. Our code allows us to create test samples to apply machine learning methods to classify minima in the light curves taking into account their morphological signatures. Our approach will be used to find and analyze some sporadic events in the observed light curves originating from transits of comet-like bodies.
ISSN:1743-9213
1743-9221
DOI:10.1017/S1743921322000023