Kavli Affiliate: Andrew Vanderburg
| First 5 Authors: Javier Viaña, Kyu-Ha Hwang, Zoë de Beurs, Jennifer C. Yee, Andrew Vanderburg
| Summary:
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 multi-observatory setup of the Korea
Microlensing Telescope Network (KMTNet) and trained on a rich dataset 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
multi-branch Recurrent Neural Network (RNN) 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, PSF 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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