AI-driven spatio-temporal engine for finding gravitationally lensed type Ia supernovae

Abstract

We present a spatio-temporal AI framework that concurrently exploits both the spatial and time-variable features of gravitationally lensed supernovae in optical images to ultimately aid in future discoveries of such exotic transients in wide-field surveys. Our spatio-temporal engine is designed using recurrent convolutional layers, while drawing from recent advances in variational inference to quantify approximate Bayesian uncertainties via a confidence score. Using simulated Young Supernova Experiment (YSE) images of lensed and non-lensed supernovae as a showcase, we find that the use of time-series images adds relevant information from time variability of spatial light distribution of partially blended images of lensed supernova, yielding a substantial gain of around 20 per cent in classification accuracy over single-epoch observations. Preliminary application of our network to mock observations from the Legacy Survey of Space and Time (LSST) results in detections with accuracy reaching around 99 percent. Our innovative deep learning machinery is versatile and can be employed to search for any class of sources which exhibit variability both in flux and spatial distribution of light.

arXiv comment14 pages, 13 figures, 2 tables. Accepted for publication in MNRAS

References in corpus (53)

• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• Correcting for the Effects of Interstellar Extinction
• H0LiCOW XIII. A 2.4% measurement of $H_0$ from lensed quasars: $5.3\sigma$ tension between early and late-Universe probes
• The Pan-STARRS1 Photometric System
• SALT2: using distant supernovae to improve the use of Type Ia supernovae as distance indicators
• Gravitationally lensed quasars and supernovae in future wide-field optical imaging surveys
• TDCOSMO IV: Hierarchical time-delay cosmography -- joint inference of the Hubble constant and galaxy density profiles
• Cosmological Constraints from Measurements of Type Ia Supernovae discovered during the first 1.5 years of the Pan-STARRS1 Survey
• Lenstronomy: multi-purpose gravitational lens modelling software package
• Multiple Images of a Highly Magnified Supernova Formed by an Early-Type Cluster Galaxy Lens
• K-corrections and Extinction Corrections for Type Ia Supernovae
• Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks
• iPTF16geu: A multiply imaged, gravitationally lensed type Ia supernova
• Spectrophotometric time series of SN 2011fe from the Nearby Supernova Factory
• CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding
• Fast calculation of a family of elliptical mass gravitational lens models
• Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
• Measuring Type Ia Supernova Populations of Stretch and Color and Predicting Distance Biases
• The Young Supernova Experiment: Survey Goals, Overview, and Operations
• Detection of the Gravitational Lens Magnifying a Type Ia Supernova
• Finding Strong Gravitational Lenses in the DESI DECam Legacy Survey
• Discovering New Strong Gravitational Lenses in the DESI Legacy Imaging Surveys
• Gravitational lens time delays for distant supernovae: break the degeneracy between radial mass profiles and the Hubble constant
• Illuminating a Dark Lens : A Type Ia Supernova Magnified by the Frontier Fields Galaxy Cluster Abell 2744
• The image separation distribution of strong lenses: Halo versus subhalo populations
• Deep Convolutional Neural Networks as strong gravitational lens detectors
• Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder
• Microlensing of Lensed Supernovae
• SN Refsdal : Photometry and Time Delay Measurements of the First Einstein Cross Supernova
• HOLISMOKES -- II. Identifying galaxy-scale strong gravitational lenses in Pan-STARRS using convolutional neural networks
• How to Find Gravitationally Lensed Type Ia Supernovae
• Model-independent determination of $H_0$ and ${\Omega }_{K0}$ from strong lensing and type Ia supernovae
• Magnified or multiply imaged? - Search strategies for gravitationally lensed supernovae in wide-field surveys
• Using Convolutional Neural Networks to identify Gravitational Lenses in Astronomical images
• Finite Source Effects in Strong Lensing: Implications for the Substructure Mass Scale
• Turning Gravitationally Lensed Supernovae into Cosmological Probes
• Interpreting the strongly lensed supernova iPTF16geu: time delay predictions, microlensing, and lensing rates
• Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing
• The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples
• A Simple Method To Find All Lensed Quasars
• Improved time-delay lens modelling and $H_0$ inference with transient sources
• Machine learning astrophysics from 21 cm lightcones: impact of network architectures and signal contamination
• Transient-optimised real-bogus classification with Bayesian Convolutional Neural Networks -- sifting the GOTO candidate stream
• HOLISMOKES -- IV. Efficient mass modeling of strong lenses through deep learning
• A highly magnified supernova at z=1.703 behind the massive galaxy cluster Abell 1689
• Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant
• Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting
• Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique
• Auto-identification of unphysical source reconstructions in strong gravitational lens modelling
• Difference Imaging of Lensed Quasar Candidates in the SDSS Supernova Survey Region
• Low-redshift measurement of the sound horizon through gravitational time-delays
• Lensed quasar search via time variability with the HSC transient survey
• Simulating time-varying strong lenses

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• Strong lensing time-delay cosmography in the 2020s
• Strong gravitational lensing and microlensing of supernovae
• A search for gravitationally lensed supernovae within the Zwicky Transient Facility public survey
• DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing Data
• Detecting unresolved lensed SNe Ia in LSST using blended light curves
• DeepRed: an architecture for redshift estimation
• DeepZipper II: Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning