Examinando por Autor "Sluse, Dominique"
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Ítem New Constraints on Quasar Broad Absorption and Emission Line Regions from Gravitational Microlensing(Journal, 2017-09) Hutsemékers, Damien; Braibant, Lorraine; Sluse, Dominique; Anguita, Timo; Goosmann, RenéGravitational microlensing is a powerful tool allowing one to probe the structure of quasars on sub-parsec scale. We report recent results, focusing on the broad absorption and emission line regions. In particular microlensing reveals the intrinsic absorption hidden in the P Cygni-type line profiles observed in the broad absorption line quasar H1413+117, as well as the existence of an extended continuum source. In addition, polarization microlensing provides constraints on the scattering region. In the quasar Q2237+030, microlensing differently distorts the Hα and CIV broad emission line profiles, indicating that the low- and high-ionization broad emission lines must originate from regions with distinct kinematical properties. We also present simulations of the effect of microlensing on line profiles considering simple but representative models of the broad emission line region. Comparison of observations to simulations allows us to conclude that the Hα emitting region in Q2237+030 is best represented by a Keplerian disk. © Copyright © 2017 Hutsemékers, Braibant, Sluse, Anguita and Goosmann.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.Ítem PS J2107-1611: A new wide-separation, quadruply imaged lensed quasar with flux ratio anomalies(EDP Sciences, 2023-11) Dux, Frédéric; Lemon, Cameron; Courbin, Frédéric; Sluse, Dominique; Smette, Alain; Anguita, Timo; Neira, FavioWe report the discovery of PS J2107-1611, a fold-configuration 4.3′′-separation quadruply lensed quasar with a bright lensed arc. It was discovered using a convolutional neural network on Pan-STARRS gri images of pre-selected quasar candidates with multiple nearby Pan-STARRS detections. Spectroscopic follow-up with EFOSC2 on the ESO 3.58 m New Technology Telescope reveals the source to be a quasar at z = 2.673, with the blended fold image pair showing deformed broad lines relative to the other images. The flux ratios measured from optical to near-infrared imaging in the Canada-France-Hawaii Telescope Legacy Survey, Pan-STARRS, the Legacy Surveys, and the Vista Hemisphere Survey are inconsistent with a smooth mass model as the fold pair images are ∼15 times too faint. Variability, time delay effects, and reddening are ruled out through multiple-epoch imaging and color information. The system is marginally resolved in the radio in the Very Large Array Sky Survey S-band, where it has a 10 mJy detection. The radio flux ratios are compatible with the smooth mass macromodel. This system offers a unique tool for future studies of quasar structure with strong and microlensing. A more detailed analysis of follow-up with JWST/MIRI, VLT/MUSE, VLT/ERIS, and data from the European Very Long Baseline Interferometer will be presented in a forthcoming paper. © 2023 EDP Sciences. All rights reserved.