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Lens Model Accuracy in the Expected LSST Lensed AGN Sample

  • Authors: Padmavathi Venkatraman, Sydney Erickson, Phil Marshall, Martin Millon, Philip Holloway, Simon Birrer, Steven Dillmann, Xiangyu Huang, Sreevani Jaragula, Ralf Kaehler, Narayan Khadka, Grzegorz Madejski, Ayan Mitra, Kevil Reil, Aaron Roodman

Padmavathi Venkatraman et al 2026 The Astronomical Journal 172 .

  • Provider: AAS Journals

Caption: Figure 4.

Five simulated LSST lensed AGN systems in the i band. We test our inference pipeline on three different preparations of the data. Subtraction of different light components has been shown to improve the performance of the modeling pipeline in lens parameter recovery. We discuss this further in Section 2.3. In the first column (to the left of the black line), we show the deconvolved version of the second column of images (to the right of the black line). Deconvolution also helps in the lens modeling, detailed also in Section 2.3. Top row: include lens light, AGN light, and host galaxy light. Middle row: lens light subtracted. Poisson noise due to lens light remains. Bottom row: lens light and AGN light subtracted. Poisson noise due to all light remains. The background Gaussian noise also remains constant across three different preparations.

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