发表机构
University of Cape Town; South African Astronomical Observatory; The Inter-University Institute for Data Intensive Astronomy, University of Cape Town; Department of Astrophysics/IMAPP, Radboud University; Universitat de Barcelona (UB); Institut de Ciéncies del Cosmos (ICCUB), Universitat de Barcelona (UB); Institut d’Estudis Espacials de Catalunya (IEEC)（开普敦大学; 南非天文台; 开普敦大学数据密集型天文学跨大学研究所; 拉德堡德大学天体物理学/IMAPP系; 巴塞罗那大学; 巴塞罗那大学宇宙科学研究所; 加泰罗尼亚空间研究学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究结合多巡天公开数据，用膨胀火球模型表征125个超新星，成功恢复关键参数，为最大化当前及未来巡天的科学回报提供了重要方法。
AI 中文摘要
广域光学暂现源巡天每晚共发现约50个新暂现源，这使得除了最特殊的事件外，对所有暂现源进行快速多波段和/或光谱后续观测都不可行。因此，大多数超新星（SNe），尤其是低光度事件的（早期）光变曲线性质往往表征不佳。本文展示了通过结合多个同期巡天的公开数据来恢复关键参数的方法：我们通过将MeerLICHT（ML）暂现源巡天与2017年8月至2022年10月间光谱分类SNe的暂现源名称服务器列表交叉匹配，构建了包含125个SNe的星表。对于其中14个具有良好多巡天光变覆盖的SNe，我们结合ML-q、ZTF-g,r和ATLAS-c,o数据构建了复合早期光变曲线。随后我们应用膨胀火球（t²）模型来约束它们的爆发日期和静止帧上升时间。研究发现，该简单模型能可靠描述样本中大多数SNe的早期辐射，并成功约束其上升时间——这是表征SNe类别和探测前身星起源的关键参数。这种仅使用标准巡天产品的多巡天方法，为表征大量否则会被遗漏的SNe提供了强大手段，该技术对于最大化当前及未来巡天（如GOTO、BlackGEM、Vera C. Rubin天文台的LSST以及Global Open Transient Telescope Array（GOTTA））的科学回报至关重要。
英文摘要
Wide-field optical transient surveys collectively discover $\sim$50 new transients per night, making rapid, multi-filter and/or spectroscopic follow-up of all but the most exceptional events unfeasible. Consequently, the (early) light-curve properties of most supernovae (SNe), particularly sub-luminous events, are often poorly characterised. Here, we showcase recovery of key parameters by combining publicly available data from multiple, contemporaneous surveys. We built a catalogue of $125$ SNe by cross-matching the MeerLICHT (ML) transient survey with the Transient Name Server list of spectroscopically classified SNe detected between August 2017 and October 2022. For a subset of $14$ SNe with good multi-survey photometric coverage, we combined ML-$q$, ZTF-$g,r$, and ATLAS-$c,o$ data to construct composite early-time light curves. We then applied an expanding fireball ($t^2$) model to constrain their explosion dates and rest-frame rise times. We find that this simple model provides a reliable description for the early emission of most SNe in our sample and successfully constrains their rise time, a key parameter for characterising SN categories and probing progenitor origins. This multi-survey approach, only using standard survey products, provides a powerful method to characterise large numbers of SNe that would otherwise be lost, a technique that will be essential for maximising the scientific return from current and future surveys such as GOTO, BlackGEM, the Vera C. Rubin Observatory's LSST, and Global Open Transient Telescope Array (GOTTA).
