AppleCiDEr. II. SpectraNet: A Spectroscopic Neural Network Classifier for Transients Demonstrated on ZTF Follow-up Data
Time-domain surveys such as the Zwicky Transient Facility have opened a new frontier in the discovery and characterization of transients. While photometric light curves provide broad temporal coverage, spectroscopic observations remain crucial for physical interpretation and source classification. However, existing spectral analysis methods, often reliant on template fitting or parametric models, are limited in their ability to capture the complex and evolving spectra characteristic of such sources, which are sometimes only available at low resolution. In this work, we introduce SpectraNet, a deep convolutional neural network designed to learn robust representations of optical spectra from transients. Our model combines multi-scale convolution kernels and pooling operations to extract features from preprocessed spectra in a hierarchical and interpretable manner. We train and validate SpectraNet on low-resolution time-series spectra obtained from the Spectral Energy Distribution Machine and other instruments, demonstrating better performance in classification compared to other known pipelines.