DeepZipper II: Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning

Abstract

Gravitationally lensed supernovae (LSNe) are important probes of cosmic expansion, but they remain rare and difficult to find. Current cosmic surveys likely contain and 5-10 LSNe in total while next-generation experiments are expected to contain several hundreds to a few thousands of these systems. We search for these systems in observed Dark Energy Survey (DES) 5-year SN fields -- 10 3-sq. deg. regions of sky imaged in the $griz$ bands approximately every six nights over five years. To perform the search, we utilize the DeepZipper approach: a multi-branch deep learning architecture trained on image-level simulations of LSNe that simultaneously learns spatial and temporal relationships from time series of images. We find that our method obtains a LSN recall of 61.13% and a false positive rate of 0.02% on the DES SN field data. DeepZipper selected 2,245 candidates from a magnitude-limited ($m_i$ $<$ 22.5) catalog of 3,459,186 systems. We employ human visual inspection to review systems selected by the network and find three candidate LSNe in the DES SN fields.

arXiv commentAccepted by ApJ

References in corpus (30)

• Scikit-learn: Machine Learning in Python
• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Array Programming with NumPy
• Astropy: A Community Python Package for Astronomy
• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• The Dark Energy Camera
• The Dark Energy Survey: more than dark energy - an overview
• The Zwicky Transient Facility: Science Objectives
• The Supernova Legacy Survey 3-year sample: Type Ia Supernovae photometric distances and cosmological constraints
• Strong Lensing by Galaxies
• The Dark Energy Survey Data Release 2
• Lenstronomy: multi-purpose gravitational lens modelling software package
• Multiple Images of a Highly Magnified Supernova Formed by an Early-Type Cluster Galaxy Lens
• iPTF16geu: A multiply imaged, gravitationally lensed type Ia supernova
• lenstronomy II: A gravitational lensing software ecosystem
• The Difference Imaging Pipeline for the Transient Search in the Dark Energy Survey
• Results from the Supernova Photometric Classification Challenge
• Strong gravitational lensing of explosive transients
• OzDES multi-fibre spectroscopy for the Dark Energy Survey: first-year operation and results
• The Young Supernova Experiment: Survey Goals, Overview, and Operations
• Detection of the Gravitational Lens Magnifying a Type Ia Supernova
• First Cosmology Results Using Type Ia Supernovae From the Dark Energy Survey: Photometric Pipeline and Light Curve Data Release
• Illuminating a Dark Lens : A Type Ia Supernova Magnified by the Frontier Fields Galaxy Cluster Abell 2744
• Strong lens systems search in the Dark Energy Survey using Convolutional Neural Networks
• The Dark Energy Survey 5-year photometrically identified Type Ia Supernovae
• A highly magnified supernova at z=1.703 behind the massive galaxy cluster Abell 1689
• A catalogue of structural and morphological measurements for DES Y1
• DeepZipper: A Novel Deep Learning Architecture for Lensed Supernovae Identification
• deeplenstronomy: A dataset simulation package for strong gravitational lensing
• AI-driven spatio-temporal engine for finding gravitationally lensed type Ia supernovae