Deep learning unresolved lensed light curves
Denissenya M. Linder E.V.
1 September 2022Oxford University Press
Monthly Notices of the Royal Astronomical Society
2022#515Issue 1977 - 983 pp.
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.
cosmology: observations , gravitational lensing: strong , methods: data analysis , methods: numerical , transients: supernovae
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Energetic Cosmos Laboratory, Nazarbayev University, Nur-Sultan, 010000, Kazakhstan
Berkeley Center for Cosmological Physics, Berkeley Lab, University of California, Berkeley, 94720, CA, United States
Energetic Cosmos Laboratory
Berkeley Center for Cosmological Physics
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