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Microlensing of strongly lensed quasars

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arxiv 2312.00931 v1 pith:ZSOFJIGC submitted 2023-12-01 astro-ph.GA

classification astro-ph.GA
keywords lensingmicrolensingmasspotentialquasardiscussdistributionfield
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Strong gravitational lensing of quasars has the potential to unlock the poorly understood physics of these fascinating objects, as well as serve as a probe of the lensing mass distribution and of cosmological parameters. In particular, gravitational microlensing by compact bodies in the lensing galaxy can enable mapping of quasar structure to $\lt 10^{-6}$ arcsec scales. Some of this potential has been realized over the past few decades, however the upcoming era of large sky surveys promises to bring this to full fruition. Here we review the theoretical framework of this field, describe the prominent current methods for parameter inference from quasar microlensing data across different observing modalities, and discuss the constraints so far derived on the geometry and physics of quasar inner structure. We also review the application of strong lensing and microlensing to constraining the granularity of the lens potential, i.e. the contribution of the baryonic and dark matter components, and the local mass distribution in the lens, i.e. the stellar mass function. Finally, we discuss the future of the field, including the new possibilities that will be opened by the next generation of large surveys and by new analysis methods now being developed.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AGN Reverberation Mapping with LITMUS: Fundamental Limits on lag Recovery Rates

    astro-ph.CO 2026-08 conditional novelty 7.0 of 10

    A Bayesian re-analysis of 906 OzDES AGN finds that previous reverberation lags, especially for MgII and CIV, are often false positives, with only 3-28% of MgII sources showing simple lags.

  2. Microlensing Detection and Inference via Learned Bayes Factors

    astro-ph.IM 2026-07 conditional novelty 6.0 of 10

    A unified transformer-based pipeline detects 99.9% of recoverable simulated microlensing events and outperforms literature hard cuts in the short-duration finite-source regime with amortized neural posterior inference.

  3. Microlensing of Microlensing: Effects of Random Stars on the Double-Source-Plane Gravitational Lens

    astro-ph.CO 2026-07 conditional novelty 6.0 of 10

    First numerical ray-shooting simulation of compound (two-plane) quasar microlensing in the double-source-plane lens J1721+8842 shows new caustic morphologies — convexity violations and lip caustics — that do not occur...

  4. Speeding up Gravitational Lens Mass Models with Machine Learning: Applications in X-ray Astronomy

    astro-ph.GA 2026-07 conditional novelty 5.0 of 10

    A fully connected network trained on millions of simulated quads predicts SIE lens mass and ellipticity from four image positions, cutting optimisation time for real and simulated quadruply lensed quasars to minutes.

  5. High-cadence observations of galactic nuclei by the future two-band UV-photometry mission QUVIK

    astro-ph.GA 2025-01 conditional novelty 3.0 of 10

    The QUVIK mission concept is shown, via simulated AGN light curves, to be able to recover ultraviolet continuum reverberation lags at daily to subdaily cadence for nearby bright AGN.

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