Examinando por Autor "Vernardos, Georgios"
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Ítem A quasar microlensing light-curve generator for LSST(Oxford University Press, 2020-06-01) Neira, Favio; Anguita, Timo; Vernardos, GeorgiosWe present a tool to generate mock quasar microlensing light curves and sample them according to any observing strategy. An updated treatment of the fixed and random velocity components of observer, lens, and source is used, together with a proper alignment with the external shear defining the magnification map caustic orientation. Our tool produces quantitative results on high magnification events and caustic crossings, which we use to study three lensed quasars known to display microlensing, viz. RX J1131–1231, HE 0230–2130, and Q 2237+0305, as they would be monitored by The Rubin Observatory Legacy Survey of Space and Time (LSST). We conclude that depending on the location on the sky, the lens and source redshift, and the caustic network density, the microlensing variability may deviate significantly than the expected ∼20-yr average time-scale (Mosquera & Kochanek 2011). We estimate that ∼300 high magnification events with Δmag>1 mag could potentially be observed by LSST each year. The duration of the majority of high magnification events is between 10 and 100 d, requiring a very high cadence to capture and resolve them. Uniform LSST observing strategies perform the best in recovering microlensing high magnification events. Our web tool can be extended to any instrument and observing strategy, and is freely available as a service at http://gerlumph.swin.edu.au/tools/lsst generator/, along with all the related code.Ítem Predicting High-magnification Events in Microlensed Quasars in the Era of LSST Using Recurrent Neural Networks(Elsevier Ltd, 0025-03) Fagin, Joshua; Paic, Eric; Neira, Favio; Best, Henry; Anguita, Timo; Millon, Martin; O’Dowd, Matthew; Sluse, Dominique; Vernardos, GeorgiosUpcoming 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.Ítem Predicting High-magnification Events in Microlensed Quasars in the Era of LSST Using Recurrent Neural Networks(Institute of Physics, 2025-03) Fagin, Joshua; Paic, Eric; Neira, Favio d; Best, Henry; Anguita, Timo; Millon, Martin; O’Dowd, Matthew; Sluse, Dominique; Vernardos, GeorgiosUpcoming 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 resourcesÍtem Predicting High-magnification Events in Microlensed Quasars in the Era of LSST Using Recurrent Neural Networks(Institute of Physics, 0025-03) Fagin. Joshua; Paic, Eric; Neira, Favio; Best, Henry; Anguita, Timo; Millon, Martin; O’Dowd, Matthew; Sluse, Dominique; Vernardos, GeorgiosUpcoming 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.