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Exploring the spectroscopic diversity of type Ia supernovae with Deep Learning and Unsupervised Clustering

Published online by Cambridge University Press:  30 May 2017

Emille E. O. Ishida
Affiliation:
Clermont Université, Université Blaise Pascal, CNRS/IN2P3, Laboratoire de Physique Corpusculaire, BP 10448, F-63000 - Clermont-Ferrand, France email: emille.ishida@clermont.in2p3.fr
Michele Sasdelli
Affiliation:
Astrophysics Research Institute, Liverpool John Moores University, Liverpool L3 5RF, UK
Ricardo Vilalta
Affiliation:
Department of Computer Science, University of Houston, 4800 Calhoun Rd., Houston TX 77204-3010, USA
Michel Aguena
Affiliation:
Departamento de Física Matemática, Instituto de Física, Universidade de São Paulo, CP 66318, CEP 05508-090, São Paulo - SP, Brazil
Vinicius C. Busti
Affiliation:
Departamento de Física Matemática, Instituto de Física, Universidade de São Paulo, CP 66318, CEP 05508-090, São Paulo - SP, Brazil
Hugo Camacho
Affiliation:
Departamento de Física Matemática, Instituto de Física, Universidade de São Paulo, CP 66318, CEP 05508-090, São Paulo - SP, Brazil
Arlindo M. M. Trindade
Affiliation:
Instituto de Astrofisica e Ciências do Espaço, Universidade do Porto, CAUP, Rua das Estrelas, PT4150-762 Porto, Portugal Departamento de Física e Astronomia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre 687, PT4169-007 - Porto, Portugal
Fabian Gieseke
Affiliation:
Institute for Computing and Information Sciences, Radboud University Nijmegen, Toernooiveld 212, 6525 EC - Nijmegen, Netherlands
Rafael S. de Souza
Affiliation:
MTA Eötvös University, EIRSA “Lendulet” Astrophysics Research Group, Budapest 1117, Hungary IAG, Universidade de São Paulo, Rua do Matão 1226, 05508-900, São Paulo, Brazil
Yabebal T. Fantaye
Affiliation:
Department of Mathematics, University of Rome Tor Vergata, Rome, Italy
Paolo A. Mazzali
Affiliation:
Astrophysics Research Institute, Liverpool John Moores University, Liverpool L3 5RF, UK
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Abstract

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The existence of multiple subclasses of type Ia supernovae (SNeIa) has been the subject of great debate in the last decade. In this work, we show how machine learning tools facilitate identification of subtypes of SNe Ia. Using Deep Learning for dimensionality reduction, we were capable of performing such identification in a parameter space of significantly lower dimension than its principal component analysis counterpart. This is evidence that the progenitor system and the explosion mechanism can be described with a small number of initial physical parameters. All tools used here are publicly available in the Python package DRACULA (Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy) and can be found within COINtoolbox (https://github.com/COINtoolbox/DRACULA).

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2017 

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