ExoCoDe: MODELING TRANSITS EVENTS VIA STATISTICAL AND MACHINE LEARNING TOOLS
| dc.contributor.author | Karakuts, Denys | |
| dc.date.accessioned | 2024-02-28T14:27:35Z | |
| dc.date.available | 2024-02-28T14:27:35Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | This capstone project uses statistical and machine learning algorithms to detect exocomet transits in TESS telescope data. Exocomets, distinguished by their unique, asymmetric light curves, present a detection challenge due to their subtle signatures compared to planetary transits and light intensity. We develop a framework that integrates data preprocessing, feature extraction, visualizations, statistical methods, and machine learning regressors to characterize these transits efficiently. The project is built upon the existing progress of astrophysical research. It aims to enhance our understanding of exocometary activity, uncovering the potential of machine learning and statistical analysis in astronomical data interpretation. | |
| dc.identifier.uri | https://er.auk.edu.ua/handle/234907866/45 | |
| dc.language.iso | en_US | |
| dc.subject | exocomet | |
| dc.subject | anomaly detection | |
| dc.subject | time-series | |
| dc.subject | machine learning | |
| dc.subject | star brightness | |
| dc.title | ExoCoDe: MODELING TRANSITS EVENTS VIA STATISTICAL AND MACHINE LEARNING TOOLS | |
| dc.type | Thesis |