A Supernova Recognition Method Based on NPSVM
Abstract
Sky survey is closely related to the developments of many domains such as high energy physics and black hole astrophysics. In order to solve the classification problem between galaxy and supernova, an available supernova recognition method based on NPSVM (Neyman-Pearson Support Vector Machine) has been proposed. The dataset, which is collected from WISeREP (the Weizmann Interactive Supernova data REPository), SDSS (the Sloan Digital Sky Survey) and supernova templates made by Nugent, has 3427 supernova spectra and 2193 galaxy spectra. After preprocessing spectral data, the decomposed spectrum feature based on the Principal Component Analysis (PCA) is extracted, and the redundant features are decreased with the cosine similarity method. The classification model based on Support Vector Machine (SVM) has a low level of generalization error evaluated 0.498\%, and can calculate the classification probability for a new sample. Furthermore, the improved NPSVM model can limit the missing rate on supernovae with the Neyman-Pearson criterion.
- Publication:
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Acta Astronomica Sinica
- Pub Date:
- March 2021
- Bibcode:
- 2021AcASn..62...15W
- Keywords:
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- supernovae: general;
- galaxies: general;
- techniques: spectroscopic;
- methods: data analysis