罗马望远镜即将带来海量天文数据，做瞬变天体探测的团队需要可靠的自动分类工具——RuBR模型解决了无真实数据时的训练难题，值得关注其后续在真实观测中的表现。
美国宇航局的南希·格雷斯·罗马太空望远镜计划于2026年9月发射，将进行前所未有的高分辨率红外巡天，预计发现数百万天文瞬变现象。由于缺乏真实数据，开发自动警报管道面临挑战。研究团队提出机器学习模型RuBR，结合本地注入和OpenUniverse2024模拟数据，训练出三种变体（RuBR_comb、RuBR_loc、RuBR_DA），用于区分真实瞬变与虚假检测。实验表明，该方法在图像差分管道中表现有效，为罗马任务早期无真实标签情况下的鲁棒分类铺平了道路。
Identifying Gems from Roman RAPIDly
The Nancy Grace Roman Space Telescope (Roman), set for launch as early as September 2026, will conduct wide-field infrared imaging surveys with unprecedented spatial resolution and cadence, enabling the discovery of millions of astronomical transients. Hence, it is necessary to have automated pipelines for generating alerts in place so that the telescope can begin discovering reliable transients and variable objects soon after it is launched. However, no real Roman data currently exist, making the development of such pipelines difficult. In this work, we present a machine learning model $RuBR$ and a general methodology for distinguishing genuine transient and variable detections from spurious (bogus) detections within the RAPID pipeline. In particular, we present three models using this methodology: $RuBR_{comb}$ trained and tested on combined locally injected and OpenUniverse2024 transients, $RuBR_{loc}$ trained on locally injected transients and tested on OpenUniverse2024 transients, and $RuBR_{DA}$ that combines locally injected transients with a fraction of OpenUniverse2024 transients in domain-adaptation mode for training. This paves the way for strategies to adapt the $RuBR_{comb}$ model to real observations in the absence of any ground-truth labels during the early phases of the Roman mission. While the image differencing pipeline continues to be improved, our experimental results demonstrate the effectiveness of the proposed approach and its promise for robust real-bogus classification in the Roman era.