TTC: Transformer-based TDE Classifier for the Wide Field Survey Telescope (WFST)

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 are 0.79 and 0.76, respectively, and can be modified by adjusting the threshold. It can also efficiently find TDE candidates within 30 days from the first detection. For comparison, the parametric fitting module yields values of 0.72 and 0.40, respectively, while it is $>$10 times faster in average speed. Hence, the setup of modules allows a trade-off between performance and time, as well as precision and recall. \texttt{TTC} has successfully picked out all spectroscopically identified TDEs among ZTF transients in a real-time classification test, and selected $\sim$20 TDE candidates in the deep field survey data of WFST. The discovery rate will greatly increase once the differential database for the wide field survey is ready.