(Abridged) A new method is presented to identify transient candidates in large-scale surveys based on the variability pattern in their light curves. The method is based on the Abbe value, that estimates the smoothness of a light curve, and on a newly introduced value called the excess Abbe that estimates the regularity of the light curve variability pattern over the duration of the observations. Based on simulated light curves, transients are shown to occupy a specific region in the Abbe versus Excess Abbe diagram, distinct from sources presenting pulsating-like features in their light curves or having featureless light curves.
The method is tested on real light curves taken from EROS-2 and OGLE-II surveys in a 0.50deg x 0.17deg field of the sky in the LMC. The method identifies 43 EROS-2 transient candidates out of a total of ~1300 variable stars, and 19 more OGLE-II candidates. The efficiency of the method is further tested by comparing the list of transient candidates with known Be stars in the literature. It is shown that all Be stars known in the studied field of view with detectable bursts or outbursts are successfully extracted by the method. In addition, four new transient candidates displaying bursts and/or outbursts are found in the field, of which at least two are good new Be candidates.
The new method proves to be a potentially powerful tool to extract transient candidates from large-scale multi-epoch surveys. The better the photometric measurement uncertainties are, the cleaner the list of detected transient candidates is. In addition, the diagram is shown to be a good diagnostic tool to check the data quality of multi-epoch photometric surveys. A trend of instrumental and/or data reduction origin, for example, will manifest itself by an unexpected distribution of points in the diagram.
The GMRT High Resolution Southern Sky Survey for pulsars and transients -- III: searching for long period pulsars
在理想白噪声背景下，利用全相位相干快速折叠算法（FFA）搜索周期性非加速信号，理论上比非相干谐波求和快速傅里叶变换（FFT）搜索更灵敏。在本文中，我们提出了一个FFA搜索实现使用RIPTIDE和FFT搜索实现使用PRESTO的性能比较，在一个范围内的信号参数与白噪声和真正的望远镜噪声从GHRSS调查与uGMRT。我们发现，FFA搜索与适当的去红的时间序列，表现出更好的比FFT搜索与频谱白化的长周期序列在真正的GHRSS噪声条件。我们描述了一个FFA搜索管道实施的GHRSS调查寻找在0.1秒至100秒的时间范围内，高达500 pc cm $^{-3}$的分散措施的雷达。我们使用该管道处理了覆盖天空$^2$的$\sim$ 1500度的GHRSS测量数据。我们重新检测了43个已知的信号，FFA搜索比FFT搜索具有更好的信噪比。我们还报告发现了两个新的脉冲星，包括一个长周期脉冲星，具有短的占空比与FFA搜索管道。周期为几秒或更长的长周期脉冲星的数量有助于限制脉冲星的死亡线。
Ghosts of eruptions past: Searching for historical Galactic supernovae using variable thermal dust echoes and machine learning
Columbia University（哥伦比亚大学）；Flatiron Institute（平顿研究所）；California Institute of Technology（加州理工学院）；Space Telescope Science Institute（太空望远镜科学研究所）；Johns Hopkins University（约翰斯·霍普金斯大学）；Massachusetts Institute of Technology（麻省理工学院）
The WINTER Observatory: A One-Degree InGaAs Survey Camera to study the Transient Infrared Sky
Harvard & Smithsonian（哈佛-史密森天体物理中心）；Massachusetts Institute of Technology（麻省理工学院）；California Institute of Technology（加州理工学院）；Columbia University（哥伦比亚大学）；University of Maryland, College Park（马里兰大学帕克分校）
Transfer learning for transient search with small-field optical survey telescopes
Aryabhatta Research Institute of Observational Sciences (ARIES)（阿亚帕塔观测科学研究所）；Department of Applied Optics and Photonics University of Calcutta（加尔各答大学应用光学与光子学系）；Institute of Astrophysics and Geophysics University of Liège（列日大学天体物理学与地球物理研究所）；Department of Physics Loyola College（罗耀拉学院物理系）；Department of Physics School of Advanced Sciences Vellore Institute of Technology（维洛尔理工大学高级科学学院物理系）
cWB-space: A time-frequency unmodelled transient-search pipeline for LISA
ETH Zurich（苏黎世联邦理工学院）；Universitat de les Illes Balears（巴利阿里群岛大学）；University of Trento（特伦托大学）；INFN-TIFPA, Trento Institute for Fundamental Physics and Applications（意大利国家核物理研究所-特伦托基础物理与应用研究所）
Searching for changing-state AGNs in massive datasets -- I: applying deep learning and anomaly detection techniques to find AGNs with anomalous variability behaviours
活动星系核（AGN）的经典分类方案最近受到了所谓的变态（变貌）AGN（CSAGNs）的发现的挑战。这一现象背后的物理机制仍然是一个公开辩论的问题，样本太小，而且是偶然发现的，无法提供可靠的答案。为了解决这个问题，我们需要设计能够在改变状态的行为中检测AGN的方法。在这里，我们提出了一个异常检测（AD）技术，旨在识别活动星系核的光变曲线与异常行为在大量的数据集。这项技术的主要目的是在转变的不同阶段识别CSAGN，但它也可以用于更一般的目的，例如清理AGN可变性分析的大规模数据集。我们使用的光变曲线来自Zwicky Transient Facility数据发布5（ZTF DR 5），包含230，451个不同类别的AGN样本。ZTF DR 5光变曲线采用变分递归自动编码器（VRAE）架构建模，这使我们能够从VRAE潜在空间中获得一组属性，这些属性描述了我们样本的一般行为。然后，这些属性被用作隔离森林（IF）算法的特征，该算法是针对“一类”问题的异常检测器。我们使用VRAE重建误差和IF异常评分来选择8，809个异常的样本。这些异常主要是由虚假的候选人，但我们能够确定75个有希望的CSAGN候选人。
