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Gaia16aye was a binary microlensing event discovered in the direction towards the northern Galactic disc and was one of the first microlensing events detected and alerted to by the Gaia space mission. Its light curve exhibited five distinct brightening episodes, reaching up to I = 12 mag, and it was covered in great detail with almost 25 000 data points gathered by a network of telescopes. We present the photometric and spectroscopic follow-up covering 500 days of the event evolution. We employed a full Keplerian binary orbit microlensing model combined with the motion of Earth and Gaia around the Sun to reproduce the complex light curve. The photometric data allowed us to solve the microlensing event entirely and to derive the complete and unique set of orbital parameters of the binary lensing system. We also report on the detection of the first-ever microlensing space-parallax between the Earth and Gaia located at L2. The properties of the binary system were derived from microlensing parameters, and we found that the system is composed of two main-sequence stars with masses 0.57 ± 0.05 M ⊙ and 0.36 ± 0.03 M ⊙ at 780 pc, with an orbital period of 2.88 years and an eccentricity of 0.30. We also predict the astrometric microlensing signal for this binary lens as it will be seen by Gaia as well as the radial velocity curve for the binary system. Events such as Gaia16aye indicate the potential for the microlensing method of probing the mass function of dark objects, including black holes, in directions other than that of the Galactic bulge. This case also emphasises the importance of long-term time-domain coordinated observations that can be made with a network of heterogeneous telescopes.
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This content will become publicly available on February 20, 2026
LensNet : Enhancing Real-time Microlensing Event Discovery with Recurrent Neural Networks in the Korea Microlensing Telescope Network
Abstract Traditional microlensing event vetting methods require highly trained human experts, and the process is both complex and time consuming. This reliance on manual inspection often leads to inefficiencies and constrains the ability to scale for widespread exoplanet detection, ultimately hindering discovery rates. To address the limits of traditional microlensing event vetting, we have developed LensNet, a machine learning pipeline specifically designed to distinguish legitimate microlensing events from false positives caused by instrumental artifacts, such as pixel bleed trails and diffraction spikes. Our system operates in conjunction with a preliminary algorithm that detects increasing trends in flux. These flagged instances are then passed to LensNet for further classification, allowing for timely alerts and follow-up observations. Tailored for the multiobservatory setup of the Korea Microlensing Telescope Network and trained on a rich data set of manually classified events, LensNet is optimized for early detection and warning of microlensing occurrences, enabling astronomers to organize follow-up observations promptly. The internal model of the pipeline employs a multibranch Recurrent Neural Network architecture that evaluates time-series flux data with contextual information, including sky background, the full width at half-maximum of the target star, flux errors, point-spread function quality flags, and air mass for each observation. We demonstrate a classification accuracy above 87.5% and anticipate further improvements as we expand our training set and continue to refine the algorithm.
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- Award ID(s):
- 2108414
- PAR ID:
- 10654673
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- AAS Journals
- Date Published:
- Journal Name:
- The Astronomical Journal
- Volume:
- 169
- Issue:
- 3
- ISSN:
- 0004-6256
- Page Range / eLocation ID:
- 159
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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