TTC: Transformer-based TDE Classifier for the Wide Field Survey Telescope (WFST)
Ranfang Zheng, Zheyu Lin, Xu Kong, Dezheng Meng, Zelin Xu, Lulu Fan, Ji-an Jiang, Ning Jiang, Jie Lin, Tinggui Wang, Qingfeng Zhu, Feng Li, Ming Liang, Hao Liu, Zheng Lou, Wentao Luo, Jinlong Tang, Hairen Wang, Jian Wang, Yongquan Xue, Dazhi Yao, Hong-fei Zhang, Wen Zhao

TL;DR
The paper introduces TTC, a Transformer-based classifier for TDE detection in WFST data, capable of real-time and archival analysis, with improved accuracy and speed over previous methods.
Contribution
It presents a novel Transformer-based TDE classifier that operates effectively with light curves, reducing reliance on external catalogs and enabling faster, more accurate TDE identification.
Findings
Mgformer-based module achieves 0.79 recall and 0.76 precision.
The parametric fitting module is over 10 times faster.
TTC successfully identified all known TDEs in ZTF data.
Abstract
We propose the Transformer-based Tidal disruption events (TDE) Classifier (\texttt{TTC}), specifically designed to operate effectively with both real-time alert streams and archival data of the Wide Field Survey Telescope (WFST). It aims to minimize the reliance on external catalogs and find TDE candidates from pure light curves, which is more suitable for finding TDEs in faint and distant galaxies. \texttt{TTC} consists of two key modules that can work independently: (1) A light curve parametric fitting module and (2) a Transformer (\texttt{Mgformer})-based classification network. The training of the latter module and evaluation for each module utilize a light curve dataset of 7413 spectroscopically classified transients from the Zwicky Transient Facility (ZTF). The \texttt{Mgformer}-based module is superior in performance and flexibility. Its representative recall and precision values…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Gamma-ray bursts and supernovae
