Speaker
Andy Chen
(Institute of Physics, National Yang Ming Chiao Tung University)
Description
Core-collapse supernovae (CCSNe) are one of the new astrophysical events to be detected by the LVK observatory. Also, it is an interesting candidate for multi-messenger analysis due to their EM and neutrino emission. However, GW signals from CCSNe cannot be exactly modeled due to the stochasticity involved in the collapse dynamics and the dependency on many parameters such as the progenitor mass, rotational state, metallicity, etc. To mitigate false alarms in the detection pipeline, we propose implementing a binary neural network classifier to accurately discriminate between triggers generated by glitches and those originating from core-collapse supernovae (CCSNe).
Primary author
Andy Chen
(Institute of Physics, National Yang Ming Chiao Tung University)
Co-authors
Albert Kong
(National Tsing Hua University)
Dr
Chia-Jui Chou
(Department of Physics, National Yang Ming Chiao Tung University)
Kuo-Chuan Pan
(Institute of Astronomy, National Tsing Hua University)
Yi Yang
(National Yang Ming Chiao Tung University)
