标题： 基于ViT的局部体积矮星系识别(VIDA)在CSST巡天中的应用 显示英文标题

标题： ViT-based Local Volume dwarf galaxy Identificationin (VIDA) in the CSST survey

摘要： 识别局部星系晕中的矮星系对于限制附近宇宙中卫星星系的亮度函数至关重要。 我们报告了使用中国空间站望远镜（CSST）探测局部星系晕中矮星系的能力。 基于CSST模拟成像数据，我们开发了一种结合传统基于图像搜索技术和先进机器学习分类模型的检测和分类流水线。 通过“扩展源检测”的预处理方法识别模拟的局部星系晕矮星系，随后使用预训练的ViT-Base模型进行分类。 该流水线实现了超过85%的真正阳性率（TPR），而假阳性率（FPR）仅为0.1%。 我们量化了局部星系晕中矮星系在由绝对星等（$M_V$）、半光半径（$R_h$）和日心距离定义的三维参数空间中的检测完整性，基于模拟的单次曝光CSST宽视场成像巡天数据。 对于未解析或部分解析的矮星系，与基于目录的方法相比，我们的方法达到了显著更深的绝对星等探测极限，在10\Mpc 范围内达到$M_V = -7$。 通过结合这种基于图像的方法与传统的基于恒星目录的“匹配滤波”技术，本研究建立的自动化框架可以为CSST任务识别20\Mpc 范围内的矮星系。 显示英文摘要

摘要： Identifying dwarf galaxies within the Local Volume is crucial for constraining the luminosity function of satellite galaxies in the nearby universe. We report the detection capabilities of dwarf galaxies within the Local Volume using the Chinese Space Station Telescope (CSST). Based on the simulated imaging data of CSST, we develop a detection and classification pipeline that combines traditional image-based search techniques with advanced machine learning classification models. The simulated Local Volume dwarf galaxies can be identified using a pre-processing method for "extended source detection", followed by classification with a pretrained ViT-Base model. This pipeline achieves a true positive rate (TPR) exceeding 85% with a false positive rate (FPR) of only 0.1%. We quantify the detection completeness of Local Volume dwarf galaxies across a three-dimensional parameter space defined by absolute magnitude ($M_V$), half-light radius ($R_h$), and heliocentric distance, based on simulated single-exposure CSST wide-field imaging survey data. For unresolved or semi-resolved dwarf galaxies, our method achieves a significantly deeper absolute magnitude detection limit compared to catalog-based approaches, reaching $M_V = -7$ within 10 \Mpc. By combining this image-based approach with traditional stellar catalog-based "matched filter" techniques, our automated framework established in this work can identify dwarf galaxies within 20 \Mpc for the CSST mission.