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  1. Book ; Online: A deep-learning search for technosignatures of 820 nearby stars

    Ma, Peter Xiangyuan / Ng, Cherry / Rizk, Leandro / Croft, Steve / Siemion, Andrew P. V. / Brzycki, Bryan / Czech, Daniel / Drew, Jamie / Gajjar, Vishal / Hoang, John / Isaacson, Howard / Lebofsky, Matt / MacMahon, David / de Pater, Imke / Price, Danny C. / Sheikh, Sofia Z. / Worden, S. Pete

    2023  

    Abstract: The goal of the Search for Extraterrestrial Intelligence (SETI) is to quantify the prevalence of technological life beyond Earth via their "technosignatures". One theorized technosignature is narrowband Doppler drifting radio signals. The principal ... ...

    Abstract The goal of the Search for Extraterrestrial Intelligence (SETI) is to quantify the prevalence of technological life beyond Earth via their "technosignatures". One theorized technosignature is narrowband Doppler drifting radio signals. The principal challenge in conducting SETI in the radio domain is developing a generalized technique to reject human radio frequency interference (RFI). Here, we present the most comprehensive deep-learning based technosignature search to date, returning 8 promising ETI signals of interest for re-observation as part of the Breakthrough Listen initiative. The search comprises 820 unique targets observed with the Robert C. Byrd Green Bank Telescope, totaling over 480, hr of on-sky data. We implement a novel beta-Convolutional Variational Autoencoder to identify technosignature candidates in a semi-unsupervised manner while keeping the false positive rate manageably low. This new approach presents itself as a leading solution in accelerating SETI and other transient research into the age of data-driven astronomy.

    Comment: 10 pages of main paper followed by 16 pages of methods; 17 figures total and 7 tables; published in Nature Astronomy
    Keywords Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Machine Learning
    Subject code 020
    Publishing date 2023-01-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: Narrow-Band Signal Localization for SETI on Noisy Synthetic Spectrogram Data

    Brzycki, Bryan / Siemion, Andrew P. V. / Croft, Steve / Czech, Daniel / DeBoer, David / DeMarines, Julia / Drew, Jamie / Gajjar, Vishal / Isaacson, Howard / Lacki, Brian / Lebofsky, Matthew / MacMahon, David H. E. / de Pater, Imke / Price, Danny C. / Worden, S. Pete

    2020  

    Abstract: 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 ... ...

    Abstract 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.

    Comment: 12 pages, 10 figures, 1 table, submitted to PASP
    Keywords Astrophysics - Instrumentation and Methods for Astrophysics
    Subject code 551
    Publishing date 2020-06-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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