利用变率指标自动区分Rubin暂现源与活动星系核（AGN）
Automatically distinguishing Rubin transients from AGN using variability metrics
浏览论文内容
中文总结 AI 辅助
本研究提出基于测光变率参数的二维阈值法，区分Rubin暂现源与AGN，可避免AGN对暂现源巡天的污染，效率高且易扩展。
中文摘要 AI 辅助
活动星系核（AGN）的随机变异性会在搜寻河外爆发暂现源（如超新星和潮汐瓦解事件）时产生污染。在Rubin天文台的时空遗产巡天（LSST）新时代，此前未编目的AGN，尤其是那些光度接近巡天探测极限的AGN，预计会产生大量探测结果，可能污染针对其他暂现源的巡天，导致后续光谱跟进时间的低效利用。对于旨在统计表征暂现源人口统计学特征的巡天，使用易于建模和可重复的选择标准来区分AGN与其他暂现源比使用机器学习更具优势。我们测试基于简单的数据驱动测光变率参数设置阈值，以区分非AGN河外暂现源与标准AGN变异性，测试数据来自兹威基暂现设施测光数据和MALLORN数据集的模拟LSST测光数据。我们还研究了光变曲线历史可用性、红移范围和滤光片选择对选择效率的影响。我们发现，结合探测流量与探测前标准偏差的比值、探测流量与探测前平均流量的比值的二维阈值是最有效的。该方法易于扩展，因为这些值已包含在LSST警报数据包中。我们提供了设置该阈值后产生样本的完备性和纯度估计，并评估了避免的AGN污染。该方法使用的参数也可作为特征用于测光分类器中识别AGN。
英文摘要
Stochastic variability of active galactic nuclei (AGN) can produce contaminants in the search for explosive extragalactic transients (such as supernovae and tidal disruption events). In the new era of the Rubin Observatory's Legacy Survey of Space and Time (LSST), previously uncatalogued AGN, especially those with luminosity near the survey detection limits, are expected to produce a flood of detections that have the potential to contaminate surveys targeting other transients, leading to inefficient use of spectroscopic follow-up time. For surveys aiming to statistically characterise transient demographics, it is advantageous to use easily modelled and reproducible selection criteria to distinguish AGN from other transients, rather than machine learning. We test enacting cuts based on simple data-driven photometric variability parameters to distinguish non-AGN extragalactic transients from standard AGN variability on both Zwicky Transient Facility photometry and simulated LSST photometry from the MALLORN data set. We also investigate the impact of light curve history availability, redshift range and filter selection on selection efficiency. We find that a two-dimensional cut incorporating the ratio of detection flux and pre-detection standard deviation and the ratio of detection flux to pre-detection mean flux is the most effective cut. This approach is easily scalable as these values are included in the LSST alert packets. We provide estimates of the completeness and purity of the sample produced by enacting this cut, and gauge the AGN contamination avoided. The parameters utilised in this approach could also be implemented as features for identifying AGN in a photometric classifier.
