DASH: Deep Learning for the Automated Spectral Classification of Supernovae and Their Hosts

• Published in: The Astrophysical journal Vol. 885; no. 1; pp. 85 - 102
• Main Authors: Muthukrishna, Daniel, Parkinson, David, Tucker, Brad E.
• Format: Journal Article
• Language: English
• Published: Philadelphia The American Astronomical Society 01.11.2019; IOP Publishing
• Subjects: Algorithms; Artificial neural networks; Astrophysics; Automation; Classification; Dark energy; Deep learning; Galaxies; Identification methods; Iterative methods; Machine learning; methods: data analysis; methods: statistical; Neural networks; Red shift; Sky surveys (astronomy); Software; Spectra; Spectral classification; Supernova; Supernovae; supernovae: general; surveys; techniques: spectroscopic; Template matching
• ISSN: 0004-637X; 1538-4357; 1538-4357
• DOI: 10.3847/1538-4357/ab48f4

We present DASH (Deep Automated Supernova and Host classifier), a novel software package that automates the classification of the type, age, redshift, and host galaxy of supernova spectra. DASH makes use of a new approach that does not rely on iterative template-matching techniques like all previous software, but instead classifies based on the learned features of each supernova's type and age. It has achieved this by employing a deep convolutional neural network to train a matching algorithm. This approach has enabled DASH to be orders of magnitude faster than previous tools, being able to accurately classify hundreds or thousands of objects within seconds. We have tested its performance on 4 yr of data from the Australian Dark Energy Survey (OzDES). The deep learning models were developed using TensorFlow and were trained using over 4000 supernova spectra taken from the CfA Supernova Program and the Berkeley SN Ia Program as used in SNID (Supernova Identification software). Unlike template-matching methods, the trained models are independent of the number of spectra in the training data, which allows for DASH's unprecedented speed. We have developed both a graphical interface for easy visual classification and analysis of supernovae and a Python library for the autonomous and quick classification of several supernova spectra. The speed, accuracy, user-friendliness, and versatility of DASH present an advancement to existing spectral classification tools. We have made the code publicly available on GitHub and PyPI (pip install astrodash) to allow for further contributions and development. The package documentation is available at https://astrodash.readthedocs.io.