Leveraging Multimodality for Real-Time Classification of Transients and Variables found by the Zwicky Transient Facility
arXiv:2607.00228v1 Announce Type: cross Abstract: Modern time-domain surveys such as the Zwicky Transient Facility (ZTF) generate hundreds of thousands of alerts each night, making real-time decisions for follow-up observations a central challenge in time-domain astronomy. Robust early classification is crucial for making informed decisions, but is hindered by sparse light curves and degeneracies between classes. In this work, we leverage multimodality to substantially improve real-time classification and demonstrate the practicality of our approach by deploying our model on the ZTF alert stre
The increasing volume of data from modern astronomical surveys necessitates advanced AI techniques for real-time processing and classification, pushing the boundaries of automated discovery.
This development improves the efficiency and accuracy of astronomical discovery, potentially accelerating our understanding of transient celestial events and the universe.
Astronomers can now leverage more sophisticated, multimodal AI models for real-time classification, leading to more informed and rapid follow-up observations of cosmic phenomena.
- · Astronomical observatories
- · Astrophysicists
- · AI/ML researchers
- · Traditional manual classification methods
Real-time AI classification of astronomical transients becomes a standard practice for large surveys.
Increased rates of discovery and characterization of rare or short-lived cosmic events.
New astrophysical theories emerge from the analysis of previously unobservable or rapidly identified phenomena.
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Read at arXiv cs.LG