DEEP LEARNING UNRESOLVED LENSED LIGHT CURVES
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Date
2022
Authors
Denissenya, Mikhail
Linder, Eric V
Journal Title
Journal ISSN
Volume Title
Publisher
Monthly Notices of the Royal Astronomical Society
Abstract
Gravitationally lensed sources may have unresolved or blended multiple images, and for time varying sources, the light curves from individual images can overlap. We use convolutional neural nets to both classify the light curves as due to unlensed, double, or quad lensed sources and fit for the time delays. Focusing on lensed supernova systems with time delays Δt ≳ 6 d, we achieve 100 per cent precision and recall in identifying the number of images and then estimating the time delays to σΔt ≈ 1 d, with a 1000× speedup relative to our previous Monte Carlo technique. This also succeeds for flux noise levels ∼10 per cent
. For Δt ∈ [2, 6] d, we obtain 94–98 per cent accuracy, depending on image configuration. We also explore using partial light curves where observations only start near maximum light, without the rise time data, and quantify the success.
Description
Keywords
Type of access: Open Access, gravitational lensing: strong, methods: data analysis, methods: numerical, cosmology: observations, transients: supernovae
Citation
Denissenya, M., & Linder, E. V. (2022a). Deep learning unresolved lensed light curves. Monthly Notices of the Royal Astronomical Society, 515(1), 977–983. https://doi.org/10.1093/mnras/stac1726