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Activity Number: 378
Type: Contributed
Date/Time: Tuesday, August 11, 2015 : 11:35 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #317813
Title: Functional Template Learning for Type Ia Supernova
Author(s): Shiyuan He* and Jianhua Huang and Lifan Wang
Companies: Texas A&M University and Texas A&M University and Texas A&M University
Keywords: Astrostatistics ; functional PCA ; nonlinear least squares ; regularization ; roughness penalty
Abstract:

Type Ia supernova is an important class of supernova. They serve as the standard candle for measuring the distance of hosting galaxy, thereby, helping us to measure the expansion rate of the universe and the dark energy. In this talk, I will present a method for estimating the light curves of supernovae from sparsely sampled observations, by using ideas from the functional data analysis. The method learns a template of light curve from discrete observations of a collection of supernovae. The light curve of a supernova in the test set is then fit using this data-generated template, and well represented by several interpretable parameters. One parameter is linearly correlated the frequently cited $\Delta m_15$ parameter in astronomy. Another parameter is the peak magnitude, which is typically used to determine the distance of hosting galaxy. Several other parameters are principal component scores, helpful for K-correction and extinction correction. The method is applied to fit the light curves of filter U and filter V of nearby Type Ia supernovae. This talk is based on a joint work with Jianhua Huang and Lifan Wang.


Authors who are presenting talks have a * after their name.

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