Speaker
Description
Type Ia supernovae (SNe Ia) are commonly categorized into two sub-groups: normal-velocity (NV) and high-velocity (HV) SNe, which are distinguished by whether the ejecta velocity of Si II λ6355 exceeds 12,000 km s-1 or not. However, the velocity distributions of these two groups overlap, making it difficult to classify them cleanly using ejecta velocity alone. Therefore, in order to identify potential NV SNe Ia which share the same origin as those HV ones, we would like to apply machine learning (ML) to solve the degeneracy. We use PU Bagging, a method in Positive-Unlabeled Learning (PU learning), as our ML algorithm. Data from Palomar Transient Factory (PTF) and Zwicky Transient Facility (ZTF) are used as dataset for training, testing and final validation of the ML model, using several input features. This approach provides a promising way to uncover shared properties between NV and HV SNe Ia, helping identify NV events that may originate from the same progenitor population as HV ones.
| Participate the oral/poster presentation award competition | Yes |
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