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New Transformer-based ML model identifies microlensing events in TESS data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Microlensify, a novel machine learning classifier based on the Transformer architecture, designed to identify microlensing events using data from the Transiting Exoplanet Survey Satellite (TESS). This model, trained on simulated and real TESS data, can classify events, reconstruct light curves, and estimate event durations with high accuracy. When applied to millions of TESS light curves, Microlensify identified a small percentage as potential microlensing candidates, distinguishing them from various false positives like variable stars and asteroid crossings. The model's effectiveness was further validated on events from ground-based surveys, confirming its broad applicability. AI

IMPACT This model could improve the efficiency and accuracy of detecting faint celestial objects in astronomical surveys.

RANK_REASON The cluster describes a new machine learning model presented in an arXiv paper for a specific scientific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Transformer-based ML model identifies microlensing events in TESS data

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The cluster describes a new machine learning model presented in an arXiv paper for a specific scientific application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Atousa Kalantari, Somayeh Khakpash, Sedighe Sajadian, Hosein Haghi, Willow Fox Fortino, Rosanne Di Stefano ·

    Microlensify: a Transformer Based Machine Learning Classifier for Microlensing Events Trained on TESS Light Curves

    arXiv:2608.19419v1 Announce Type: cross Abstract: Microlensing can reveal populations of faint compact objects that are otherwise difficult to detect. Depending on their design, all-sky surveys have the potential to search for these objects across the sky. The Transiting Exoplane…

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