ECL_Articles

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Browsing ECL_Articles by Subject "cosmology: observations"

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    DEEP LEARNING UNRESOLVED LENSED LIGHT CURVES
    (Monthly Notices of the Royal Astronomical Society, 2022) Denissenya, Mikhail; Linder, Eric V
    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.
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    OUT OF ONE, MANY: DISTINGUISHING TIME DELAYS FROM LENSED SUPERNOVAE
    (Monthly Notices of the Royal Astronomical Society, 2022) Denissenya, Mikhail; Bag, Satadru; Kim, Alex G; Linder, Eric V; Shafieloo, Arman
    Gravitationally lensed Type Ia supernovae are an emerging probe with great potential for constraining dark energy, spatial curvature, and the Hubble constant. The multiple images and their time delayed and magnified fluxes may be unresolved, however, blended into a single light curve. We demonstrate methods without a fixed source template matching for extracting the individual images, determining whether there are one (no lensing) or two or four (lensed) images, and measuring the time delays between them that are valuable cosmological probes. We find 100 per cent success for determining the number of images for time delays greater than ∼10 d.