A stellar flare can brighten a planet in orbit around its host star, producing a light curve with a faint echo. This echo, and others from subsequent flares, can lead to the planet's discovery, revealing its orbital configuration and physical characteristics. A challenge is that an echo is faint relative to the flare and measurement noise. Here, we use a method, based on autocorrelation function estimation, to extract faint planetary echoes from stellar flare light curves. A key component of our approach is that we compensate for planetary motion, measures of echo strength are then co-added into a strong signal. Using simple flare models in simulations, we explore the feasibility of this method with current technology for detecting planets around nearby M dwarfs. We also illustrate how our method can tightly constrain a planet's orbital elements and the mass of its host star. This technique is most sensitive to giant planets within 0.1 au of active flare stars and offers new opportunities for planet discovery in orientations and configurations that are inaccessible with other planet search methods.
Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors
Université de Montréal（蒙特利尔大学）；Ciela Institute（谢拉研究所）；Mila（米拉研究所）；ICRAR（国际射电天文学研究中心）；Kavli IPMU（卡弗里数物连携宇宙研究机构）；CCA, Flatiron Institute（熨斗研究所计算天体物理学中心）；Trottier Space Institute（特罗蒂尔空间研究所）；Perimeter Institute（圆周理论物理研究所）
The HIP 54515 b Superjovian Planet as an Early, Critical Look at the Roman Coronagraph's Performance in the Faint Target Star, Small IWA Limit
University of Texas-San Antonio（德克萨斯大学圣安东尼奥分校）；Subaru Telescope, National Astronomical Observatory of Japan（昴星团望远镜，日本国立天文台）；University of Michigan（密歇根大学）；University of California-Santa Cruz（加州大学圣克鲁兹分校）；National Astronomical Observatory of Japan（日本国立天文台）
Toward automated detection of light echoes in synoptic surveys: considerations on the application of the Deep Convolutional Neural Networks
光回波（LE）是星际尘埃对天体物理瞬变的反射。它们是令人着迷的天文现象，使人们能够研究散射的尘埃以及原始的瞬变。然而，LE是罕见的，并且极难检测，因为它们看起来是微弱的、扩散的、随时间变化的特征。LE的检测仍然主要依赖于人类对图像的检查，这是一种在大型天气调查时代不可行的方法。维拉C。Rubin Observatory Legacy Survey of Space and Time，LSST，将以高空间分辨率，精致的图像质量和超过数万平方度的天空生成前所未有的天文成像数据：这是LE的理想调查。然而，Rubin数据处理管道针对点源的检测进行了优化，并且将完全错过LE。在过去的几年里，人工智能（AI）目标检测框架已经实现并超越了实时、人类水平的性能。在这项工作中，我们准备了一个来自ATLAS望远镜的数据集，并测试了一个流行的AI对象检测框架，即计算机视觉社区开发的You Only Look Once或YOLO，以展示AI在天文图像中检测LE的潜力。我们发现，即使是在大小和质量有限的数据集上，AI框架也可以达到人类水平的性能。我们探索并强调了挑战，包括类别不平衡和标签不完整性，并为高通量天文调查中LE的自动检测和研究建立端到端管道所需的工作制定了路线图。
heliostack: A Novel Approach to Minor Planet Discovery
Center for Astrophysics — Harvard & Smithsonian（哈佛-史密森天体物理中心）；Department of Physics, University of Michigan（密歇根大学物理系）；Michigan Institute for Data and AI in Society, University of Michigan（密歇根大学数据与社会人工智能研究所）；Department of Astronomy, University of Michigan（密歇根大学天文系）；Case Western Reserve University（凯斯西储大学）
QLP Data Release Notes 004: TESS-Gaia Light Curve Photometry Implementation
MIT Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology（麻省理工学院卡弗里天体物理与空间研究所）；Department of Physics & Astronomy, The University of California, Irvine（加州大学尔湾分校物理与天文学系）
