Machine learning is a promising tool to reconstruct time-series phenomena, such as variability of active galactic nuclei (AGN), from sparsely-sampled data. Here we use three Continuous Auto-Regressive Moving Average (CARMA) representations of AGN variability -- the Damped Random Walk (DRW) and (over/under-)Damped Harmonic Oscillator (DHO) -- to simulate 10-year AGN light curves as they would appear in the upcoming Vera Rubin Observatory Legacy Survey of Space and Time (LSST), and provide a public tool to generate these for any survey cadence. We investigate the impact on AGN science of five proposed cadence strategies for LSST's primary Wide-Fast-Deep (WFD) survey. We apply for the first time in astronomy a novel Stochastic Recurrent Neural Network (SRNN) algorithm to reconstruct input light curves from the simulated LSST data, and provide a metric to evaluate how well SRNN can help recover the underlying CARMA parameters. We find that the light curve reconstruction is most sensitive to the duration of gaps between observing season, and that of the proposed cadences, those that change the balance between filters, or avoid having long gaps in the {g}-band perform better. Overall, SRNN is a promising means to reconstruct densely sampled AGN light curves and recover the long-term Structure Function of the DRW process (SF$_\infty$) reasonably well. However, we find that for all cadences, CARMA/SRNN models struggle to recover the decorrelation timescale ($τ$) due to the long gaps in survey observations. This may indicate a major limitation in using LSST WFD data for AGN variability science.
The LSST era of supermassive black holes accretion-disk reverberation mapping
维拉C。鲁宾天文台的时空遗产巡天（LSST）将探测到一个空前大的活跃吸积超大质量黑洞样本，其典型的吸积盘（AD）大小为几个光日。这给利用带间连续延迟测量活动星系核AD尺寸的混响映射带来了挑战。我们使用我们的度量AGNTimeLagMetric检查LSST节奏策略对AD RM的影响。它解释了红移、节奏、星等极限和尘埃消光的星等修正。在不同的LSST节奏策略上运行我们的度量，我们产生了LSST光度RM测量的性能估计的图谱。我们提供了一个上限的估计数量的类星体，其AD时滞可以计算在0<z <7 using the features of our metric. We forecast that the total counts of such objects will increase as the mean sampling rate of the survey decreases. The AD time lag measurements are expected for >1000源在每个深钻领域（DDF，10平方米）。度），这些源的红移分布在z~1处达到峰值。我们发现LSST观测策略具有良好的节奏（~ 5天）和长的累积季节（~9年），如LSST DDF所建议的，有利于AD大小测量。我们为最合适的DDF节奏创建合成LSST光变曲线，并确定RM时间滞后，以证明基于所提出的度量的…
Nearby Supernova Science in the Era of High Cadence, Deep Time Domain Surveys
University of Arizona（亚利桑那大学）；University of California, San Diego（加州大学圣地亚哥分校）；Caltech（加州理工学院）；Gemini Observatory/NSF’s NOIRLab（吉米尼天文台/美国国家光学红外天文实验室）；Center for Astrophysics | Harvard & Smithsonian（哈佛-史密森天体物理中心）
Exploring LSST's capabilities for early detection of outbursts in low-mass X-ray binaries
International Centre for Radio Astronomy Research, Curtin University（国际射电天文中心，科廷大学）；New York University Abu Dhabi（阿布扎比纽约大学）；University of Michigan-Dearborn（密歇根大学迪尔伯恩分校）
The impact of ultraviolet suppression on the rates and properties of strongly lensed Type IIn supernovae detected by LSST
School of Physics and Astronomy, University of Birmingham（伯明翰大学物理与天文学学院）；Department of Astrophysics, University of Vienna（维也纳大学天体物理学系）；Astrophysics Research Centre, School of Mathematics and Physics, Queen’s University Belfast（贝尔法斯特女王大学数学与物理学院天体物理学研究中心）；Oskar Klein Centre, Department of Physics, Stockholm University（斯德哥尔摩大学物理系奥萨尔·克莱因中心）；Institute of Cosmology and Gravitation, University of Portsmouth（朴茨茅斯大学宇宙学与引力研究所）
Blazar variability with the Vera C. Rubin Legacy Survey of Space and Time (LSST)
耀变体的辐射主要来自指向我们的相对论性喷流，是研究河外喷流及其中心引擎的基本来源，由吸积盘提供的超大质量黑洞（SMBH）组成。它们也是高能中微子和宇宙射线的候选源。由于喷流的方向，非热耀变体的发射是多普勒波束;它的变化是不可预测的，发生的时间尺度从不到一小时到几年。理解产生通量和光谱变化的不同机制需要在长时间段上良好采样的多波段光变曲线。特别是，爆发是揭示潜在物理学的最佳测试平台，特别是在多波长背景下研究时。维拉C。Rubin Legacy Survey of Space and Time（Rubin-LSST）将在六个测光波段对南部天空进行为期十年的监测，为以统计方式研究blazar可变性特征提供了强大的工具。警报系统将使我们能够触发对突出事件的后续观测，特别是在高（keV到GeV）和非常高（TeV）能量下。在这里，我们研究了模拟Rubin-LSST调查策略，目的是了解哪些节奏更适合耀变体科学。我们的指标包括光变曲线和颜色采样。我们还调查了饱和的问题，这将影响最亮的和许多耀斑源，并将对后续观测产生不利影响。
Automatically distinguishing Rubin transients from AGN using variability metrics
Queen’s University Belfast（贝尔法斯特女王大学）；University of Southampton（南安普顿大学）；University of Birmingham（伯明翰大学）；University of Portsmouth（朴茨茅斯大学）；Leiden University（莱顿大学）；University of Manchester（曼彻斯特大学）
Optimizing Optical Searches for Supermassive Black Hole Binaries in AGN Light Curves: Fourier versus Bayesian Periodicity Detection
Vanderbilt University（范德堡大学）；Wake Forest University（维克森林大学）；Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA)（天体物理跨学科探索与研究中心）；Northwestern University（西北大学）
Identifying massive black hole binaries via light curve variability in optical time-domain surveys
Università degli Studi di Milano-Bicocca（米兰比可卡大学）；INFN, Sezione di Milano-Bicocca（意大利国家核物理研究所米兰比可卡分部）；Università degli Studi di Milano（米兰大学）；Università degli Studi dell’Insubria（因苏布里亚大学）
University of Washington（华盛顿大学）；Louisiana State University（路易斯安那州立大学）；University of Southampton（南安普顿大学）；University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）；Carnegie Mellon University（卡内基梅隆大学）
