DEEP LEARNING UNRESOLVED LENSED LIGHT CURVES

dc.contributor.authorDenissenya, Mikhail
dc.contributor.authorLinder, Eric V
dc.date.accessioned2023-03-29T06:13:04Z
dc.date.available2023-03-29T06:13:04Z
dc.date.issued2022
dc.description.abstractGravitationally 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.en_US
dc.identifier.citationDenissenya, 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/stac1726en_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/6999
dc.language.isoenen_US
dc.publisherMonthly Notices of the Royal Astronomical Societyen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectType of access: Open Accessen_US
dc.subjectgravitational lensing: strongen_US
dc.subjectmethods: data analysisen_US
dc.subjectmethods: numericalen_US
dc.subjectcosmology: observationsen_US
dc.subjecttransients: supernovaeen_US
dc.titleDEEP LEARNING UNRESOLVED LENSED LIGHT CURVESen_US
dc.typeArticleen_US
workflow.import.sourcescience

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