A versatile framework for analyzing galaxy image data by incorporating Human-in-the-loop in a large vision model

Abstract：

The exponential growth of astronomical datasets provides an unprecedented opportunity for humans to gain insight into the Universe. However, effectively analyzing this vast amount of data poses a significant challenge. In response, astronomers are turning to deep learning techniques, but these methods are limited by their specific training sets, leading to considerable duplicate workloads. To overcome this issue, we built a framework for the general analysis of galaxy images based on a large vision model (LVM) plus downstream tasks (DST), including galaxy morphological classification, image restoration, object detection, parameter extraction, and more. Considering the low signal-to-noise ratios of galaxy images and the imbalanced distribution of galaxy categories, we designed our LVM to incorporate a Human-in-the-loop (HITL) module, which leverages human knowledge to enhance the reliability and interpretability of processing galaxy images interactively. The proposed framework exhibits notable few-shot learning capabilities and versatile adaptability for all the abovementioned tasks on galaxy images in the DESI Legacy Imaging Surveys. In particular, for the object detection task, which was trained using 1000 data points, our DST in the LVM achieved an accuracy of 96.7%, while ResNet50 plus Mask R-CNN reached an accuracy of 93.1%. For morphological classification, to obtain an area under the curve (AUC) of ~0.9, LVM plus DST and HITL only requested 1/50 of the training sets that ResNet18 requested. In addition, multimodal data can be integrated, which creates possibilities for conducting joint analyses with datasets spanning diverse domains in the era of multi-messenger astronomy.