Preprint
Applying multimodal learning to Classify transient Detections Early (AppleCiDEr) I: Data set, methods, and infrastructure
24 Jul 2025
Abstract
Modern time-domain surveys like the Zwicky Transient Facility (ZTF) and the Legacy Survey of Space and Time (LSST) generate hundreds of thousands to millions of alerts, demanding automatic, unified classification of transients and variable stars for efficient follow-up. We present AppleCiDEr (Applying Multimodal Learning to Classify Transient Detections Early), a novel framework that integrates four key data modalities (photometry, image cutouts, metadata, and spectra) to overcome limitations of single-modality classification approaches. Our architecture introduces (i) two transformer encoders for photometry, (ii) a multimodal convolutional neural network (CNN) with domain-specialized metadata towers and Mixture-of-Experts fusion for combining metadata and images, and (iii) a CNN for spectra classification. Training on ~ 30,000 real ZTF alerts, AppleCiDEr achieves high accuracy, allowing early identification and suggesting follow-up for rare transient spectra. The system provides the first unified framework for both transient and variable star classification using real observational data, with seamless integration into brokering pipelines, demonstrating readiness for the LSST era.
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Details
- Title
- Applying multimodal learning to Classify transient Detections Early (AppleCiDEr) I: Data set, methods, and infrastructure
- Creators
- Alexandra JunellArgyro SasliFelipe Fontinele NunesMaojie XuBenny BorderNabeel RehemtullaMariia RizhkoYu-Jing QinTheophile Jegou Du LazAntoine Le CallochSushant Sharma ChaudharyShaowei WuJesper SollermanNiharika SravanSteven L GroomDavid HaleMansi M KasliwalJosiah PurdumAvery WoldMatthew J GrahamMichael W Coughlin
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Physics
- Other Identifier
- 991022065122604721