Speaker
Description
LSST is expected to find about several thousands of Tidal Disruption Events (TDEs) in its 10 years of operation. The physical mechanisms governing TDE emission, as well as the diversity of TDE subclasses, are only partially understood, and in order to clarify them, we require spectroscopic follow-up observations during the pre-peak rise phase and around peak brightness. This promotes attempts at developing early photometry-based detection of TDE candidates. In this talk, we investigate early classification strategies using the dataset developed for the MALLORN Kaggle challenge, which focused on nuclear transient classification with an emphasis on TDE identification. The dataset consists of LSST-like synthetic light curves and was originally designed for full light-curve classification. We repurposed this dataset to evaluate classification performance for incomplete light curves, in particular, at peak and pre-peak phases. We show that with partial light curves, the best F1 score drops from ~0.68 to 0.3-0.4, with over the half of the TDEs being missed during classification. The absence of post-peak color evolution and full event duration features strongly impacts model performance. We report possible ways of mitigate performance deterioration and what are the main contaminants to the TDEs, and consider the possibility that thorough follow-up photometric observations of transient events on the rise are needed (in addition to LSST), to provide us with the light curves with higher-cadence and finer wavelength coverage, so that we could search for better discriminating features for early TDE detection.