Text captions of radio galaxy images can classify FR-I vs FR-II morphologies comparably to image embeddings, but LoRA fine-tuning improves local class coherence without improving global image-text alignment.
Title resolution pending
4 Pith papers cite this work, alongside 12 external citations. Polarity classification is still indexing.
4
Pith papers citing it
12
external citations · OpenAlex
fields
astro-ph.IM 4years
2026 4representative citing papers
Astra-CLR is a multi-filter time-series Transformer pre-trained via contrastive learning on 2.1 million ZTF light curves that achieves 0.70 accuracy classifying 12 variability classes, rising to 0.77 with partial fine-tuning.
Hyrax is a GPU-enabled open-source framework for the full ML lifecycle in astronomy, with demonstrations of unsupervised discovery and classification on real survey data from Rubin, ZTF, and other projects.
ASTRAFier is a Transformer-BiLSTM-CNN model that classifies stellar variability from light curves, reporting 94.26% accuracy on Kepler data and 88.22% on TESS, then applied to 2.8 million TESS curves to release a catalog.
citing papers explorer
-
Exploring Image-Text Alignment for Radio Galaxy Morphologies
Text captions of radio galaxy images can classify FR-I vs FR-II morphologies comparably to image embeddings, but LoRA fine-tuning improves local class coherence without improving global image-text alignment.
-
Multi-Scale Contrastive Attention for Light-Curve Representation Learning
Astra-CLR is a multi-filter time-series Transformer pre-trained via contrastive learning on 2.1 million ZTF light curves that achieves 0.70 accuracy classifying 12 variability classes, rising to 0.77 with partial fine-tuning.
-
Hyrax: An Extensible Framework for Rapid ML Experimentation and Unsupervised Discovery in the Era of Rubin, Roman, and Euclid
Hyrax is a GPU-enabled open-source framework for the full ML lifecycle in astronomy, with demonstrations of unsupervised discovery and classification on real survey data from Rubin, ZTF, and other projects.
-
ASTRAFier: A Novel and Scalable Transformer-based Stellar Variability Classifier
ASTRAFier is a Transformer-BiLSTM-CNN model that classifies stellar variability from light curves, reporting 94.26% accuracy on Kepler data and 88.22% on TESS, then applied to 2.8 million TESS curves to release a catalog.