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  1. Book ; Online: A Deep Neural Network Based Reverse Radio Spectrogram Search Algorithm

    Ma, Peter Xiangyuan / Croft, Steve / Siemion, Andrew P. V.

    2023  

    Abstract: We developed a fast and modular deep learning algorithm to search for lookalike signals of interest in radio spectrogram data. First, we trained an autoencoder on filtered data returned by an energy detection algorithm. We then adapted a positional ... ...

    Abstract We developed a fast and modular deep learning algorithm to search for lookalike signals of interest in radio spectrogram data. First, we trained an autoencoder on filtered data returned by an energy detection algorithm. We then adapted a positional embedding layer from classical Transformer architecture to a frequency-based embedding. Next we used the encoder component of the autoencoder to extract features from small (~ 715,Hz with a resolution of 2.79Hz per frequency bin) windows in the radio spectrogram. We used our algorithm to conduct a search for a given query (encoded signal of interest) on a set of signals (encoded features of searched items) to produce the top candidates with similar features. We successfully demonstrate that the algorithm retrieves signals with similar appearance, given only the original radio spectrogram data.

    Comment: 8 pages, 8 figures
    Keywords Electrical Engineering and Systems Science - Signal Processing ; Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Machine Learning ; Computer Science - Sound ; Electrical Engineering and Systems Science - Audio and Speech Processing
    Subject code 006
    Publishing date 2023-02-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    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.

  2. Book ; Online: The weird and the wonderful in our Solar System

    Rogers, Brian / Lintott, Chris J. / Croft, Steve / Schwamb, Megan E. / Davenport, James R. A.

    Searching for serendipity in the Legacy Survey of Space and Time

    2024  

    Abstract: We present a novel method for anomaly detection in Solar System object data, in preparation for the Legacy Survey of Space and Time. We train a deep autoencoder for anomaly detection and use the learned latent space to search for other interesting ... ...

    Abstract We present a novel method for anomaly detection in Solar System object data, in preparation for the Legacy Survey of Space and Time. We train a deep autoencoder for anomaly detection and use the learned latent space to search for other interesting objects. We demonstrate the efficacy of the autoencoder approach by finding interesting examples, such as interstellar objects, and show that using the autoencoder, further examples of interesting classes can be found. We also investigate the limits of classic unsupervised approaches to anomaly detection through the generation of synthetic anomalies and evaluate the feasibility of using a supervised learning approach. Future work should consider expanding the feature space to increase the variety of anomalies that can be uncovered during the survey using an autoencoder.

    Comment: Accepted by AJ
    Keywords Astrophysics - Earth and Planetary Astrophysics ; Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Machine Learning
    Publishing date 2024-01-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    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.

  3. 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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    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.

  4. Book ; Online: Commensal, Multi-user Observations with an Ethernet-based Jansky Very Large Array

    Hickish, Jack / Beasley, Tony / Bower, Geoff / Burke-Spolaor, Sarah / Croft, Steve / DeBoer, Dave / Demorest, Paul / Diamond, Bill / Gajjar, Vishal / Law, Casey / Lazio, Joseph / Manley, Jason / Paragi, Zsolt / Ransom, Scott / Siemion, Andrew

    2019  

    Abstract: Over the last decade, the continuing decline in the cost of digital computing technology has brought about a dramatic transformation in how digital instrumentation for radio astronomy is developed and operated. In most cases, it is now possible to ... ...

    Abstract Over the last decade, the continuing decline in the cost of digital computing technology has brought about a dramatic transformation in how digital instrumentation for radio astronomy is developed and operated. In most cases, it is now possible to interface consumer computing hardware, e.g. inexpensive graphics processing units and storage devices, directly to the raw data streams produced by radio telescopes. Such systems bring with them myriad benefits: straightforward upgrade paths, cost savings through leveraging an economy of scale, and a lowered barrier to entry for scientists and engineers seeking to add new instrument capabilities. Additionally, the typical data-interconnect technology used with general-purpose computing hardware -- Ethernet -- naturally permits multiple subscribers to a single raw data stream. This allows multiple science programs to be conducted in parallel. When combined with broad bandwidths and wide primary fields of view, radio telescopes become capable of achieving many science goals simultaneously. Moreover, because many science programs are not strongly dependent on observing cadence and direction (e.g. searches for extraterrestrial intelligence and radio transient surveys), these so-called "commensal" observing programs can dramatically increase the scientific productivity and discovery potential of an observatory. In this whitepaper, we detail a project to add an Ethernet-based commensal observing mode to the Jansky Very Large Array (VLA), and discuss how this mode could be leveraged to conduct a powerful program to constrain the distribution of advanced life in the universe through a search for radio emission indicative of technology. We also discuss other potential science use-cases for the system, and how the system could be used for technology development towards next-generation processing systems for the Next Generation VLA.

    Comment: Astro2020 APC White Paper
    Keywords Astrophysics - Instrumentation and Methods for Astrophysics
    Subject code 303
    Publishing date 2019-07-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    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.

  5. 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)

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    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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