Photometric Data-driven Classification of Type Ia Supernovae in the Open Supernova Catalog

Abstract

We propose a novel approach for a machine-learning-based detection of the type Ia supernovae using photometric information. Unlike other approaches, only real observation data is used during training. Despite being trained on a relatively small sample, the method shows good results on real data from the Open Supernovae Catalog. We also investigate model transfer from the PLAsTiCC simulations train dataset to real data application, and the reverse, and find the performance significantly decreases in both cases, highlighting the existing differences between simulated and real data.

arXiv comment27 pages, 10 figures, accepted for publication in Astronomy and Computing

References in corpus (27)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant
• Measurements of Omega and Lambda from 42 High-Redshift Supernovae
• The NumPy array: a structure for efficient numerical computation
• The Complete Light-curve Sample of Spectroscopically Confirmed Type Ia Supernovae from Pan-STARRS1 and Cosmological Constraints from The Combined Pantheon Sample
• The Zwicky Transient Facility: System Overview, Performance, and First Results
• SALT2: using distant supernovae to improve the use of Type Ia supernovae as distance indicators
• An Open Catalog for Supernova Data
• SNANA: A Public Software Package for Supernova Analysis
• The Zwicky Transient Facility Bright Transient Survey. II. A Public Statistical Sample for Exploring Supernova Demographics
• First Cosmology Results using Type Ia Supernovae from the Dark Energy Survey: Constraints on Cosmological Parameters
• The Spectroscopic Diversity of Type Ia Supernovae
• Photometric Supernova Classification With Machine Learning
• SuperNNova: an open-source framework for Bayesian, Neural Network based supernova classification
• Measuring Dark Energy Properties with Photometrically Classified Pan-STARRS Supernovae. II. Cosmological Parameters
• RAPID: Early Classification of Explosive Transients using Deep Learning
• Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)
• Avocado: Photometric Classification of Astronomical Transients with Gaussian Process Augmentation
• Machine Learning-based Brokers for Real-time Classification of the LSST Alert Stream
• The Foundation Supernova Survey: Measuring Cosmological Parameters with Supernovae from a Single Telescope
• First Cosmology Results using Type Ia Supernova from the Dark Energy Survey: Simulations to Correct Supernova Distance Biases
• SuperRAENN: A Semi-supervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium Deep Survey Supernovae
• Supernova Photometric Classification Pipelines Trained on Spectroscopically Classified Supernovae from the Pan-STARRS1 Medium-Deep Survey
• DASH: Deep Learning for the Automated Spectral Classification of Supernovae and their Hosts
• Measuring the Properties of Dark Energy with Photometrically Classified Pan-STARRS Supernovae. I. Systematic Uncertainty from Core-Collapse Supernova Contamination
• STACCATO: A Novel Solution to Supernova Photometric Classification with Biased Training Sets
• Photometric classification of HSC transients using machine learning

Cited by in corpus (4)

• Pan-chromatic photometric classification of supernovae from multiple surveys and transfer learning for future surveys
• Understanding of the properties of neural network approaches for transient light curve approximations
• Supernova Light Curves Approximation based on Neural Network Models
• Optimizing Supernova Classification with Interpretable Machine Learning Models