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- Kodi Ramanah, Doogesh, et al.
(author)
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AI-driven spatio-temporal engine for finding gravitationally lensed type Ia supernovae
- 2022
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In: Monthly notices of the Royal Astronomical Society. - : Oxford University Press (OUP). - 0035-8711 .- 1365-2966. ; 512:4, s. 5404-5417
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Journal article (peer-reviewed)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 per cent. Our innovative deep learning machinery is versatile and can be employed to search for any class of sources that exhibit variability both in flux and spatial distribution of light.
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| 2. |
- Ward, Sam M., et al.
(author)
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Relative Intrinsic Scatter in Hierarchical Type Ia Supernova Sibling Analyses : Application to SNe 2021hpr, 1997bq, and 2008fv in NGC 3147
- 2023
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In: Astrophysical Journal. - 0004-637X .- 1538-4357. ; 956:2
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Journal article (peer-reviewed)abstract
- We present Young Supernova Experiment grizy photometry of SN 2021hpr, the third Type Ia supernova sibling to explode in the Cepheid calibrator galaxy, NGC 3147. Siblings are useful for improving SN-host distance estimates and investigating their contributions toward the SN Ia intrinsic scatter (post-standardization residual scatter in distance estimates). We thus develop a principled Bayesian framework for analyzing SN Ia siblings. At its core is the cosmology-independent relative intrinsic scatter parameter, σRel: the dispersion of siblings distance estimates relative to one another within a galaxy. It quantifies the contribution toward the total intrinsic scatter, σ0, from within-galaxy variations about the siblings' common properties. It also affects the combined distance uncertainty. We present analytic formulae for computing a σRel posterior from individual siblings distances (estimated using any SN model). Applying a newly trained BAYESN model, we fit the light curves of each sibling in NGC 3147 individually, to yield consistent distance estimates. However, the wide σRel posterior means σRel ≈ σ0 is not ruled out. We thus combine the distances by marginalizing over σRel with an informative prior: σRel ∼ U(0, σ0). Simultaneously fitting the trio's light curves improves constraints on distance and each sibling's individual dust parameters, compared to individual fits. Higher correlation also tightens dust parameter constraints. Therefore, σRel marginalization yields robust estimates of siblings distances for cosmology, as well as dust parameters for sibling–host correlation studies. Incorporating NGC 3147's Cepheid distance yields H0 = 78.4 ± 6.5 km s−1 Mpc−1. Our work motivates analyses of homogeneous siblings samples, to constrain σRel and its SN-model dependence.
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