Predicting High-magnification Events in Microlensed Quasars in the Era of LSST Using Recurrent Neural Networks

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dc.contributor.authorFagin. Joshua
dc.contributor.authorPaic, Eric
dc.contributor.authorNeira, Favio
dc.contributor.authorBest, Henry
dc.contributor.authorAnguita, Timo
dc.contributor.authorMillon, Martin
dc.contributor.authorO’Dowd, Matthew
dc.contributor.authorSluse, Dominique
dc.contributor.authorVernardos, Georgios
dc.date.accessioned2025-03-21T17:07:51Z
dc.date.available2025-03-21T17:07:51Z
dc.date.issued0025-03
dc.descriptionINDEXACION SCOPUS
dc.description.abstractUpcoming wide-field surveys, such as the Rubin Observatory’s Legacy Survey of Space and Time (LSST), will monitor thousands of strongly lensed quasars over a 10 yr period. Many of these monitored quasars will undergo high-magnification events (HMEs) through microlensing, as the accretion disk crosses a caustic—places of infinite magnification. Microlensing allows us to map the inner regions of the accretion disk as it crosses a caustic, even at large cosmological distances. The observational cadences of LSST are not ideal for probing the inner regions of the accretion disk, so there is a need to predict HMEs as early as possible, to trigger high-cadence multiband or spectroscopic follow-up observations. Here, we simulate a diverse and realistic sample of 10 yr quasar microlensing light curves to train a recurrent neural network to predict HMEs before they occur, by classifying the locations of the peaks at each time step. This is the first deep-learning approach for predicting HMEs. We give estimates of how well we expect to predict HME peaks during LSST and benchmark how our metrics change with different cadence strategies. With LSST-like observations, we can predict approximately 55% of HME peaks, corresponding to tens to hundreds per year and a false-positive rate of around 20% compared to the total number of HMEs. Our network can be continuously applied throughout the LSST survey, providing crucial alerts for optimizing follow-up resources. © 2025. The Author(s). Published by the American Astronomical Society.
dc.identifier.doi10.3847/1538-4357/adaebb
dc.identifier.issn0004637X
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/63840
dc.language.isoen
dc.publisherInstitute of Physics
dc.rights.licenseCC BY LICENSE
dc.titlePredicting High-magnification Events in Microlensed Quasars in the Era of LSST Using Recurrent Neural Networks
dc.typeArtículo
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