Predicting High-magnification Events in Microlensed Quasars in the Era of LSST Using Recurrent Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F25%3A00144416" target="_blank" >RIV/00216224:14310/25:00144416 - isvavai.cz</a>
Result on the web
<a href="https://iopscience.iop.org/article/10.3847/1538-4357/adaebb" target="_blank" >https://iopscience.iop.org/article/10.3847/1538-4357/adaebb</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3847/1538-4357/adaebb" target="_blank" >10.3847/1538-4357/adaebb</a>
Alternative languages
Result language
angličtina
Original language name
Predicting High-magnification Events in Microlensed Quasars in the Era of LSST Using Recurrent Neural Networks
Original language description
Upcoming 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10308 - Astronomy (including astrophysics,space science)
Result continuities
Project
<a href="/en/project/GM24-10599M" target="_blank" >GM24-10599M: Stars in galactic nuclei: interrelation with massive black holes</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Astrophysical Journal
ISSN
0004-637X
e-ISSN
1538-4357
Volume of the periodical
981
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
12
Pages from-to
1-12
UT code for WoS article
001432863400001
EID of the result in the Scopus database
2-s2.0-85218858574