Automated supernova classification скачать в хорошем качестве

Automated supernova classification

In this edition of the ZTF science vlog, Yashvi Sharma, a graduate student at Caltech tells us of the development of CCSNscore - a system that uses machine learning to combine detection & light curve data from the Zwicky Transient Facility with spectral data from the SEDM to predict the type of supernova discovery. Initial results suggest that the system works reliably for the vast majority of data. CCSNscore can enable astronomers to receive classification information in almost real time after spectroscopic observations, enabling rapid discoveries. Such systems are also necessary in the new era of time-domain astronomy when the number of supernova discoveries will increase vastly once the Vera Rubin Observatory begins operations in 2025. CCSNscore is described in detail in this publication on ArXiv https://ui.adsabs.harvard.edu/abs/202... This video is part of the science vlog series of the Zwicky Transient Facility. To learn more about ZTF, visit ztf.caltech.edu