Narrow-Band Signal Localization for SETI on Noisy Synthetic Spectrogram Data
Bryan Brzycki, Andrew P. V. Siemion, Steve Croft, Daniel Czech, David DeBoer, Julia DeMarines, Jamie Drew, Vishal Gajjar, Howard Isaacson, Brian Lacki, Matthew … 展开作者
Bryan Brzycki, Andrew P. V. Siemion, Steve Croft, Daniel Czech, David DeBoer, Julia DeMarines, Jamie Drew, Vishal Gajjar, Howard Isaacson, Brian Lacki, Matthew Lebofsky, David H. E. MacMahon, Imke de Pater, Danny C. Price, S. Pete Worden
As it stands today, the search for extraterrestrial intelligence (SETI) is highly dependent on our ability to detect interesting candidate signals, or technosignatures, in radio telescope observations and distinguish these from human radio frequency interference (RFI). Current signal search pipelines look for signals in spectrograms of intensity as a function of time and frequency (which can be thought of as images), but tend to do poorly in identifying multiple signals in a single data frame. This is especially apparent when there are dim signals in the same frame as bright, high signal-to-noise ratio (SNR) signals. In this work, we approach this problem using convolutional neural networks (CNN) as a computationally efficient method for localizing signals in synthetic observations resembling data collected by Breakthrough Listen using the Green Bank Telescope. We generate two synthetic datasets, the first with exactly one signal at various SNR levels and the second with exactly two signals, one of which represents RFI. We find that a residual CNN with strided convolutions and using multiple image normalizations as input outperforms a more basic CNN with max pooling trained on inputs with only one normalization. Training each model on a smaller subset of the training data at higher SNR levels results in a significant increase in model performance, reducing root mean square errors by at least a factor of 3 at an SNR of 25 dB. Although each model produces outliers with significant error, these results demonstrate that using CNNs to analyze signal location is promising, especially in image frames that are crowded with multiple signals.
Dual-Backend Multibeam Position Switching Targeted SETI Observations toward Nearby Active Planet-Hosting Systems with FAST
Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University（北京师范大学前沿天文与天体物理研究所）；School of Physics and Astronomy, Beijing Normal University（北京师范大学物理与天文学院）；Institute for Astronomical Science, Dezhou University（德州大学天文学研究所）；College of Computer and Information, Dezhou University（德州大学计算机与信息学院）；National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）
A Deep SETI Search for Technosignatures in the TRAPPIST-1 System with FAST
College of Computer and Information Engineering, Dezhou University（德州学院计算机与信息工程学院）；Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University（北京师范大学天文与前沿科学研究院）；School of Mathematics and Big Data, Dezhou University（德州学院数学与大数据学院）；School of Physics and Astronomy, Beijing Normal University（北京师范大学物理与天文学院）；Institute for Astronomical Science, Dezhou University（德州学院天体科学学院）
SETI Observations of Exoplanets with the Allen Telescope Array
我们报告了使用艾伦望远镜阵列（ATA）对大量已知系外行星和其他邻近恒星系统的无线电SETI观测。从2009年5月到2015年12月，进行了大约19000小时的观测。这次搜索的重点是来自总共9293颗恒星的窄带无线电信号，其中包括2015颗系外行星恒星和开普勒感兴趣的天体，以及另外65颗行星可能靠近其宜居带的天体。ATA观测是使用多个合成波束和反符合滤波器进行的，以帮助识别地面无线电干扰。在避开强地面通信频率的多个波段的1- 9千兆赫频率上观测恒星。对于窄带（0.7 ~ 100 Hz）连续信号和脉冲信号，发射器/接收器的相对加速度为-0.3 ~0.3 m/s^2，对数据进行了近实时处理。在超过8 x 10^6星兆赫兹的观测中，总共检测到1.9 x 10^8个需要立即随访的独特信号。我们没有探测到来自地外技术的持续信号超过我们的频率敏感度阈值180 ~ 310 x 10^-26 W / m^2。
A deep-learning search for technosignatures of 820 nearby stars
University of Toronto（多伦多大学）；Dunlap Institute for Astronomy & Astrophysics（邓拉普天文与天体物理研究所）；University of California, Berkeley（加州大学伯克利分校）；SETI Institute（搜寻地外文明研究所）；The University of Manchester（曼彻斯特大学）；University of Malta（马耳他大学）
Sensitivity Limits and Operational Threshold Calibration for DINOv2-based Gravitational-Wave Glitch Characterization: A Strain-Domain Mock Data Challenge on LIGO O4a
