Applying multimodal learning to Classify transient Detections Early

(repo under construction circa 5/16)

AppleCiDEr (arXiv ) is a multimodal transient classifer that uses photometry, metadata, images and spectra. Name inspired by University of Minnesota's famous apple program.

AppleCiDEr in ZTF production

---

IP structure:

AppleCider Architecture 
└── core
    ├── dataset.py             # DataGenerator
    ├── model.py               # implement multimodal models. contains: AppleCider (for all modalities), ZwickyCoder (for photo, image, metadata)
    └── trainer.py             
└── models                               # collection of models used in AppleCiDEr and baseline models    
    ├── BaselineCLS.py      # photometry model   
    ├── AstroMiNN.py        # image, metadata model
    └── other models           # old models for comparison
        ├── Informer.py        # photometry model (from AstroM3)
        ├── BTSModel.py        # image model      (adapted from BTSbot)
        ├── MetaModel.py       # metadata model   (from AstroM3)
        └── GalSpecNet.py      # spectra model

└── preprocess
    ├── process.py                   # preprocess script
    ├── alert_processor.py           # for ZTF alerts
    ├── photometry_processor.py      # for aux ZTF alerts
    ├── data_preprocessor.py         # combined ZTF, aux
    ├── transient_dataset.py         # preprocess dataset, save as "new" object alerts
└── notebooks
└── files
    ├── ZTF_IDs.txt    # all ZTF IDs used in AppleCiDEr's dataset
    └── cider_BTS.csv  # objects (+ classification) used to train AppleCiDEr that are in the public Bright Transient Survey

└── logo

citation

@article{junell2025AppleCiDEr,
      title={Applying multimodal learning to Classify transient Detections Early (AppleCiDEr) I: Data set, methods, and infrastructure}, 
      author={Alexandra Junell and Argyro Sasli and Felipe Fontinele Nunes and Maojie Xu and Benny Border and Nabeel Rehemtulla and Mariia Rizhko and Yu-Jing Qin and Theophile Jegou Du Laz and Antoine Le Calloch and Sushant Sharma Chaudhary and Shaowei Wu and Jesper Sollerman and Niharika Sravan and Steven L. Groom and David Hale and Mansi M. Kasliwal and Josiah Purdum and Avery Wold and Matthew J. Graham and Michael W. Coughlin},
      year={2025},
      eprint={2507.16088},
      archivePrefix={arXiv},
      primaryClass={astro-ph.IM},
      url={https://arxiv.org/abs/2507.16088}, 
}