{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/transientvit-a-novel-cnn-vision-transformer","title":"TransientViT: A novel CNN - Vision Transformer hybrid real/bogus transient classifier for the Kilodegree Automatic Transient Survey","arxiv_id":"2309.09937","date":"2023-09-18","proceeding":null,"authors":["Zhuoyang Chen","Wenjie Zhou","Guoyou Sun","Mi Zhang","Jiangao Ruan","Jingyuan Zhao"],"abstract":"The detection and analysis of transient astronomical sources is of great importance to understand their time evolution. Traditional pipelines identify transient sources from difference (D) images derived by subtracting prior-observed reference images (R) from new science images (N), a process that involves extensive manual inspection. In this study, we present TransientViT, a hybrid convolutional neural network (CNN) - vision transformer (ViT) model to differentiate between transients and image artifacts for the Kilodegree Automatic Transient Survey (KATS). TransientViT utilizes CNNs to reduce the image resolution and a hierarchical attention mechanism to model features globally. We propose a novel KATS-T 200K dataset that combines the difference images with both long- and short-term images, providing a temporally continuous, multidimensional dataset. Using this dataset as the input, TransientViT achieved a superior performance in comparison to other transformer- and CNN-based models, with an overall area under the curve (AUC) of 0.97 and an accuracy of 99.44%. Ablation studies demonstrated the impact of different input channels, multi-input fusion methods, and cross-inference strategies on the model performance. As a final step, a voting-based ensemble to combine the inference results of three NRD images further improved the model's prediction reliability and robustness. This hybrid model will act as a crucial reference for future studies on real/bogus transient classification.","url_abs":"https://arxiv.org/abs/2309.09937v1","url_pdf":"https://arxiv.org/pdf/2309.09937v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"transientvit-a-novel-cnn-vision-transformer","repo_url":"https://github.com/timedevblocker/transientvit","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}