PELICAN: deeP architecturE for the LIght Curve ANalysis

Abstract

We developed a deeP architecturE for the LIght Curve ANalysis (PELICAN) for the characterization and the classification of light curves. It takes light curves as input, without any additional features. PELICAN can deal with the sparsity and the irregular sampling of light curves. It is designed to remove the problem of non-representativeness between the training and test databases coming from the limitations of the spectroscopic follow-up. We applied our methodology on different supernovae light curve databases. First, we evaluated PELICAN on the Supernova Photometric Classification Challenge for which we obtained the best performance ever achieved with a non-representative training database, by reaching an accuracy of 0.811. Then we tested PELICAN on simulated light curves of the LSST Deep Fields for which PELICAN is able to detect 87.4% of supernovae Ia with a precision higher than 98%, by considering a non-representative training database of 2k light curves. PELICAN can be trained on light curves of LSST Deep Fields to classify light curves of LSST main survey, that have a lower sampling rate and are more noisy. In this scenario, it reaches an accuracy of 96.5% with a training database of 2k light curves of the Deep Fields. It constitutes a pivotal result as type Ia supernovae candidates from the main survey might then be used to increase the statistics without additional spectroscopic follow-up. Finally we evaluated PELICAN on real data from the Sloan Digital Sky Survey. PELICAN reaches an accuracy of 86.8% with a training database composed of simulated data and a fraction of 10% of real data. The ability of PELICAN to deal with the different causes of non-representativeness between the training and test databases, and its robustness against survey properties and observational conditions, put it on the forefront of the light curves classification tools for the LSST era.

References in corpus (25)

• Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant
• Measurements of Omega and Lambda from 42 High-Redshift Supernovae
• The Complete Light-curve Sample of Spectroscopically Confirmed Type Ia Supernovae from Pan-STARRS1 and Cosmological Constraints from The Combined Pantheon Sample
• Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples
• SALT2: using distant supernovae to improve the use of Type Ia supernovae as distance indicators
• Improved Distances to Type Ia Supernovae with Multicolor Light Curve Shapes: MLCS2k2
• The Sloan Digital Sky Survey-II Supernova Survey: Technical Summary
• Improving galaxy morphologies for SDSS with Deep Learning
• SNANA: A Public Software Package for Supernova Analysis
• The Sloan Digital Sky Survey-II Supernova Survey: Search Algorithm and Follow-up Observations
• The Data Release of the Sloan Digital Sky Survey-II Supernova Survey
• Photometric Supernova Classification With Machine Learning
• Photometric redshifts from SDSS images using a Convolutional Neural Network
• Results from the Supernova Photometric Classification Challenge
• Deep Recurrent Neural Networks for Supernovae Classification
• Kernel PCA for type Ia supernovae photometric classification
• Deep learning Approach for Classifying, Detecting and Predicting Photometric Redshifts of Quasars in the Sloan Digital Sky Survey Stripe 82
• Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning
• A simple and robust method for automated photometric classification of supernovae using neural networks
• Machine Learning Classification of SDSS Transient Survey Images
• Semi-supervised Learning for Photometric Supernova Classification
• Convolutional Neural Networks for Transient Candidate Vetting in Large-Scale Surveys
• Photometric classification and redshift estimation of LSST Supernovae
• A New Method for the Determination of the Hubble Parameter
• Nonparametric Transient Classification using Adaptive Wavelets

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