发现发光事件的神经引擎（NEEDLE）：从宿主星系图像中实时识别罕见瞬变候选体
NEural Engine for Discovering Luminous Events (NEEDLE): identifying rare transient candidates in real time from host galaxy images
AI总结：
本文提出混合分类器 NEEDLE，融合瞬变图像、警报测光和宿主星系信息，实时筛选 SLSNe 与 TDEs，并将部署于 Lasair。
AI中文摘要：
机器学习分类器以分析大型数据集的效率而闻名，被广泛应用于大视场天空巡天。即将开展的 Vera C. Rubin 天文台时空遗产巡天（LSST）每晚将产生数百万条警报，从而能够发现大量罕见事件样本。在爆炸后不久识别这些天体对于研究其演化至关重要。这需要一个利用所有可用瞬变和上下文信息的机器学习框架。我们使用来自 ZTF 明亮瞬变巡天的约5400个瞬变体作为输入数据，开发了 NEEDLE，这是一种新型混合分类器，用于筛选两类具有强烈环境偏好的罕见天体：偏好矮星系的超亮超新星（SLSNe），以及发生在有核星系中心的潮汐瓦解事件（TDEs）。输入数据包括检测图像和参考图像、来自警报数据包的测光信息，以及来自 Pan-STARRS 的宿主星系星等。尽管这些罕见类别只有几十个样本，但我们在未见过的测试集上对 SLSNe 的平均（最佳）完备度达到77%（93%），对 TDEs 达到72%（87%）。考虑到真实巡天中严重的类别不平衡，这仍可能导致罕见瞬变体出现很大比例的误报。然而，NEEDLE 的目标是找到适合光谱分类的良好候选体，而不是筛选纯净的测光样本。我们的网络在设计时考虑了 LSST，并且预计随着 Rubin 将提供的更高分辨率图像以及更准确的瞬变体和宿主测光数据，其性能会进一步提升。我们的系统将作为标注器部署在英国警报代理 Lasair 上，以实时向社区提供预测。
英文摘要：
Known for their efficiency in analyzing large data sets, machine learning classifiers are widely used in wide-field sky surveys. The upcoming Vera C. Rubin Observatory Legacy of Time and Space Survey (LSST) will generate millions of alerts every night, enabling the discovery of large samples of rare events. Identifying such objects soon after explosion will be essential to study their evolution. This requires a machine learning framework that makes use of all available transient and contextual information. Using $\sim5400$ transients from the ZTF Bright Transient Survey as input data, we develop NEEDLE, a novel hybrid classifier to select for two rare classes with strong environmental preferences: superluminous supernovae (SLSNe) preferring dwarf galaxies, and tidal disruption events (TDEs) occurring in the centres of nucleated galaxies. The input data includes detection and reference images, photometric information from the alert packets, and host galaxy magnitudes from Pan-STARRS. Despite having only a few tens of examples of the rare classes, our average (best) completeness on an unseen test set reaches 77% (93%) for SLSNe and 72% (87%) for TDEs. This may still result in a large fraction of false positives for the rare transients, given the large class imbalance in real surveys. However, the goal of NEEDLE is to find good candidates for spectroscopic classification, rather than to select pure photometric samples. Our network is designed with LSST in mind and we expect performance to improve further with the higher resolution images and more accurate transient and host photometry that will be available from Rubin. Our system will be deployed as an annotator on the UK alert broker, Lasair, to provide predictions to the community in real time.
