Search

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

The search for extraterrestrial intelligence at radio frequencies has largely been focused on continuous-wave narrowband signals. We demonstrate that broadband pulsed beacons are energetically efficient compared to narrowband beacons over longer operational timescales. Here, we report the first extensive survey searching for such broadband pulsed beacons towards 1883 stars as a part of the Breakthrough Listen's search for advanced intelligent life. We conducted 233 hours of deep observations across 4 to 8 GHz using the Robert C. Byrd Green Bank Telescope and searched for three different classes of signals with artificial (or negative) dispersion. We report a detailed search -- leveraging a convolutional neural network classifier on high-performance GPUs -- deployed for the very first time in a large-scale search for signals from extraterrestrial intelligence. Due to the absence of any signal-of-interest from our survey, we place a constraint on the existence of broadband pulsed beacons in our solar neighborhood: $\lesssim$1 in 1000 stars have transmitter power-densities $\gtrsim$10$^5$ W/Hz repeating $\leq$500 seconds at these frequencies., Accepted for publication in ApJ

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 are 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) that dominate the features across the band in searches for technosignatures. Here, we present the first 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 β−Convolutional Variational Autoencoder with an embedded discriminator combined with Random Forest Decision Trees to classify technosignature candidates in a semiunsupervised manner. We compare our results with prior classical techniques on the same dataset and conclude that our algorithm returns more convincing and novel signals-of-interest with a manageable false positive rate. This new approach presents itself as a leading solution in accelerating SETI and other transient research into the age of data-driven astronomy.

Stars provide an enormous gain for interstellar communications at their gravitational focus, perhaps as part of an interstellar network. If the Sun is part of such a network, there should be probes at the gravitational foci of nearby stars. If there are probes within the solar system connected to such a network, we might detect them by intercepting transmissions from relays at these foci. Here, we demonstrate a search across a wide bandwidth for interstellar communication relays beyond the Sun's innermost gravitational focus at 550 AU using the Green Bank Telescope (GBT) and Breakthrough Listen (BL) backend. As a first target, we searched for a relay at the focus of the Alpha Centauri AB system while correcting for the parallax due to Earth's orbit around the Sun. We searched for radio signals directed at the inner solar system from such a source in the L and S bands. Our analysis, utilizing the turboSETI software developed by BL, did not detect any signal indicative of a non-human-made artificial origin. Further analysis excluded false negatives and signals from the nearby target HD 13908. Assuming a conservative gain of 10^3 in L-band and roughly 4 times that in S-band, a ~1 meter directed transmitter would be detectable by our search above 7 W at 550 AU or 23 W at 1000 AU in L-band, and above 2 W at 550 AU or 7 W at 1000 AU in S-band. Finally, we discuss the application of this method to other frequencies and targets., Comment: 20 pages, 9 figures, accepted to AJ

Caballero identified the star 2MASS 19281982-2640123 as a potential Sun-like star from which the WOW! signal could have originated. We conducted a search for artificial narrowband (2.79 Hz/1.91 Hz), drifting (±4 Hz s−1) technosignatures from this source using the turboSETI pipeline, from 1–2 GHz, using simultaneous multi-telescope observations with both the Robert C. Byrd Green Bank Telescope and the newly refurbished Allen Telescope Array on 2022 May 21. Both telescope observations had an overlap of 580 s. While blind searches using radio telescopes have been conducted in the general field of view in which the WOW! signal was first detected, this is the first time a targeted search has been done. No technosignature candidates were detected.

The detection of life beyond Earth is an ongoing scientific endeavour, with profound implications. One approach, known as the search for extraterrestrial intelligence (SETI), seeks to find engineered signals (`technosignatures') that indicate the existence technologically-capable life beyond Earth. Here, we report on the detection of a narrowband signal-of-interest at ~982 MHz, recorded during observations toward Proxima Centauri with the Parkes Murriyang radio telescope. This signal, `BLC1', has characteristics broadly consistent with hypothesized technosignatures and is one of the most compelling candidates to date. Analysis of BLC1 -- which we ultimately attribute to being an unusual but locally-generated form of interference -- is provided in a companion paper (Sheikh et al., 2021). Nevertheless, our observations of Proxima Centauri are the most sensitive search for radio technosignatures ever undertaken on a star target., 14 pages, 4 figures (+3 supplementary figures). Published open-access in Nature Astronomy

Breakthrough Listen (BL) is a ten-year initiative to search for signatures of technologically capable life beyond Earth via radio and optical observations of the local Universe. A core part of the BL program is a comprehensive survey of 1702 nearby stars at radio wavelengths (1-10 GHz). Here, we report on observations with the 64-m CSIRO Parkes radio telescope in New South Wales, Australia, and the 100-m Robert C. Byrd Green Bank radio telescope in West Virginia, USA. Over 2016 January to 2019 March, a sample of 1138 stars was observed at Green Bank using the 1.10-1.90 GHz and 1.80-2.80 GHz receivers, and 189 stars were observed with Parkes over 2.60-3.45 GHz. We searched these data for the presence of engineered signals with Doppler-acceleration drift rates between -4 to 4 Hz/s. Here, we detail our data analysis techniques and provide examples of detected events. After excluding events with characteristics consistent with terrestrial radio interference, we are left with zero candidates. Given the sensitivity of our observations, we can put an upper limit on the power of potential radio transmitters at these frequencies at 2x10^12 W, and 9x10^12 W for GBT and Parkes respectively. These observations constitute the most comprehensive search over 1.10-3.45 GHz for technosignatures to date. All data products, totalling ~219 TB, are available for download as part of the first BL data release (DR1), as described in a companion paper (Lebofsky et. al., 2019), Published 2020-02-05 (AJ)

As radio telescope projects grow larger with more antennas observing wider bandwidths, data rates are rapidly increasing. The Square Kilometer Array and other next generation observatories will usher in an era of exascale data and beyond [1]. This necessitates an equivalent increase in data transfers, processing speeds, and emphasis on real-time analysis. Reducing the performance gap between high-level science algorithm development (frequently in Python) and real-time, production code would allow astronomers to better utilize the hardware available to them. To enable this, we create a high-level array data processing pipeline framework in the Julia programming language, featuring templates for modular data processing algorithms. We demonstrate its performance with a spectral kurtosis algorithm and show that the new interface does not introduce significant processing overhead. In future work, we will explore the signal processing potential of new hardware accelerators present in modern GPUs. Such accelerators promise improved performance along with new programming challenges.

We report polarization properties for eight narrowband bursts from FRB 121102 that have been re-detected in a high-frequency (4-8 GHz) Breakthrough Listen observation with the Green Bank Telescope, originally taken on 2017 August 26. The bursts were found to exhibit nearly 100% linear polarization, Faraday rotation measures (RM) bordering 9.3$\times$10$^4$ rad-m$^{-2}$, and stable polarization position angles (PA), all of which agree with burst properties previously reported for FRB 121102 at the same epoch. We confirm that these detections are indeed physical bursts with limited spectral occupancies and further support the use of sub-banded search techniques in FRB detection., 4 pages, 1 table. Accepted to RNAAS

A line-of-sight towards the Galactic Center (GC) offers the largest number of potentially habitable systems of any direction in the sky. The Breakthrough Listen program is undertaking the most sensitive and deepest targeted SETI surveys towards the GC. Here, we outline our observing strategies with Robert C. Byrd Green Bank Telescope (GBT) and Parkes telescope to conduct 600 hours of deep observations across 0.7--93 GHz. We report preliminary results from our survey for ETI beacons across 1--8 GHz with 7.0 and 11.2 hours of observations with Parkes and GBT, respectively. With our narrowband drifting signal search, we were able to place meaningful constraints on ETI transmitters across 1--4 GHz and 3.9--8 GHz with EIRP limits of $\geq$4$\times$10$^{18}$ W among 60 million stars and $\geq$5$\times$10$^{17}$ W among half a million stars, respectively. For the first time, we were able to constrain the existence of artificially dispersed transient signals across 3.9--8 GHz with EIRP $\geq$1$\times$10$^{14}$ W/Hz with a repetition period $\leq$4.3 hours. We also searched our 11.2 hours of deep observations of the GC and its surrounding region for Fast Radio Burst-like magnetars with the DM up to 5000 pc cm$^{-3}$ with maximum pulse widths up to 90 ms at 6 GHz. We detected several hundred transient bursts from SGR J1745$-$2900, but did not detect any new transient burst with the peak luminosity limit across our observed band of $\geq$10$^{31}$ erg s$^{-1}$ and burst-rate of $\geq$0.23 burst-hr$^{-1}$. These limits are comparable to bright transient emission seen from other Galactic radio-loud magnetars, constraining their presence at the GC., Comment: Accepted for publication in AJ

New radio telescope arrays offer unique opportunities for large-scale commensal SETI surveys. Ethernet-based architectures are allowing multiple users to access telescope data simultaneously by means of multicast Ethernet subscriptions. Breakthrough Listen will take advantage of this by conducting a commensal SETI survey on the MeerKAT radio telescope in South Africa. By subscribing to raw voltage data streams, Breakthrough Listen will be able to beamform commensally anywhere within the field of view during primary science observations. The survey will be conducted with unprecedented speed by forming and processing 64 coherent beams simultaneously, allowing the observation of several million objects within a few years. Both coherent and incoherent observing modes are planned. We present the list of desired sources for observation and explain how these sources were selected from the Gaia DR2 catalog. Given observations planned by MeerKAT's primary telescope users, we discuss their effects on the commensal survey and propose a commensal observing strategy in response. Finally, we outline our proposed approach towards observing one million nearby stars and analyse expected observing progress in the coming years.

The aim of the search for extraterrestrial intelligence (SETI) is to find technologically-capable life beyond Earth through their technosignatures. On 2019 April 29, the Breakthrough Listen SETI project observed Proxima Centauri with the Parkes 'Murriyang' radio telescope. These data contained a narrowband signal with characteristics broadly consistent with a technosignature near 982 MHz ('blc1'). Here we present a procedure for the analysis of potential technosignatures, in the context of the ubiquity of human-generated radio interference, which we apply to blc1. Using this procedure, we find that blc1 is not an extraterrestrial technosignature, but rather an electronically-drifting intermodulation product of local, time-varying interferers aligned with the observing cadence. We find dozens of instances of radio interference with similar morphologies to blc1 at frequencies harmonically related to common clock oscillators. These complex intermodulation products highlight the necessity for detailed follow-up of any signal-of-interest using a procedure such as the one outlined in this work., Comment: 15 pages of main paper followed by 19 pages of supplementary material; 22 figures total; published open-access in Nature Astronomy

We present Breakthrough Listen's "Exotica" Catalog as the centerpiece of our efforts to expand the diversity of targets surveyed in the Search for Extraterrestrial Intelligence (SETI). As motivation, we introduce the concept of survey breadth, the diversity of objects observed during a program. Several reasons for pursuing a broad program are given, including increasing the chance of a positive result in SETI, commensal astrophysics, and characterizing systematics. The Exotica Catalog is a 963 entry collection of 816 distinct targets intended to include "one of everything" in astronomy. It contains four samples: the Prototype sample, with an archetype of every known major type of non-transient celestial object; the Superlative sample of objects with the most extreme properties; the Anomaly sample of enigmatic targets that are in some way unexplained; and the Control sample with sources not expected to produce positive results. As far as we are aware, this is the first object list in recent times with the purpose of spanning the breadth of astrophysics. We share it with the community in hopes that it can guide treasury surveys and as a general reference work. Accompanying the catalog is extensive discussion of classification of objects and a new classification system for anomalies. Extensive notes on the objects in the catalog are available online. We discuss how we intend to proceed with observations in the catalog, contrast it with our extant Exotica efforts, and suggest similar tactics may be applied to other programs., Corresponds to version published in ApJS. 122 pages (29 pages + full appendices and references), 13 tables, 6 figures

Short, transient radio-frequency interference (RFI) events could threaten the quality of astronomical observations made by new and planned radio telescopes such as MeerKAT, the SKA and HERA in the radio astronomy reserve in South Africa. Because they are so short, often of the order of hundreds of nanoseconds long, these events are difficult to detect and identify in the time-frequency data typically produced by RFI monitoring systems. In this paper we record and analyze a dataset of the time domain RFI signals of nine typical transient RFI sources. We statistically characterize these signal types, study their distributions and investigate the effectiveness of analyzing them using components analysis methods such as principal components analysis (PCA) and kernel PCA. Our analysis provides a foundation for possible future transient RFI identification systems.

The ultra-wideband low (UWL) receiver is a new feed for the Parkes telescope, which covers the combined bandwidth of four previously installed receivers. In order to use the UWL for technosignature observations, several updates and upgrades were made on the Breakthrough Listen Parkes Data Recorder (BLPDR). This Research Note summarizes BLPDR hardware upgrades and new capacity for technosignature observations with the UWL receiver.

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., 12 pages, 10 figures, 1 table, submitted to PASP

Breakthrough Listen is the most comprehensive and sensitive search for extraterrestrial intelligence (SETI) to date, employing a collection of international observational facilities including both radio and optical telescopes. During the first three years of the Listen program, thousands of targets have been observed with the Green Bank Telescope (GBT), Parkes Telescope and Automated Planet Finder. At GBT and Parkes, observations have been performed ranging from 700 MHz to 26 GHz, with raw data volumes averaging over 1PB / day. A pseudo-real time software spectroscopy suite is used to produce multi-resolution spectrograms amounting to approximately 400 GB hr^-1 GHz^-1 beam^-1. For certain targets, raw baseband voltage data is also preserved. Observations with the Automated Planet Finder produce both 2-dimensional and 1-dimensional high resolution (R~10^5) echelle spectral data. Although the primary purpose of Listen data acquisition is for SETI, a range of secondary science has also been performed with these data, including studies of fast radio bursts. Other current and potential research topics include spectral line studies, searches for certain kinds of dark matter, probes of interstellar scattering, pulsar searches, radio transient searches and investigations of stellar activity. Listen data are also being used in the development of algorithms, including machine learning approaches to modulation scheme classification and outlier detection, that have wide applicability not just for astronomical research but for a broad range of science and engineering. In this paper, we describe the hardware and software pipeline used for collection, reduction, archival, and public dissemination of Listen data. We describe the data formats and tools, and present Breakthrough Listen Data Release 1.0 (BLDR 1.0), a defined set of publicly-available raw and reduced data totalling 1 PB., Comment: 28 pages, 15 figures, PASP accepted

Transient radio frequency interference (RFI) is detrimental to radio astronomy. It is typically broadband, intermittent and particularly difficult to classify by source. RFI of this type can be generated by devices like mechanical relays, fluorescent lights or AC machines. Such sources may be present in the vicinity of a radio telescope array, especially if other new instruments are under construction nearby. One mitigating approach is to deploy independent RFI monitoring stations at radio telescope arrays. Once the sources of RFI signals are identified, they may be removed or replaced where possible. For the first time in the open literature, we demonstrate an approach to classifying the sources of transient RFI (in time domain data) using deep learning techniques. Our proposed model includes a pre-trained CNN followed by a bidirectional LSTM layer. Applied to a previously obtained dataset of experimentally recorded transient RFI signals, our approach offers good results. It shows potential for development into a tool for identifying the sources of transient RFI signals recorded by RFI monitoring stations.

As radio telescopes become more sensitive, the damaging effects of radio frequency interference (RFI) become more apparent. Near radio telescope arrays, RFI sources are often easily removed or replaced; the challenge lies in identifying them. Transient (impulsive) RFI is particularly difficult to identify. We propose a novel dictionary-based approach to transient RFI identification. RFI events are treated as sequences of sub-events, drawn from particular labelled classes. We demonstrate an automated method of extracting and labelling sub-events using a dataset of transient RFI. A dictionary of labels may be used in conjunction with hidden Markov models to identify the sources of RFI events reliably. We attain improved classification accuracy over traditional approaches such as SVMs or a na\"ive kNN classifier. Finally, we investigate why transient RFI is difficult to classify. We show that cluster separation in the principal components domain is influenced by the mains supply phase for certain sources.

The star EPIC 249706694 (HD 139139) was found to exhibit 28 transit-like events over an 87 day period during the Kepler mission's K2 Campaign 15 (Rappaport et al. 2019). These events did not fall into an identifiable pattern and could not be explained by a multitude of transit scenarios explored by the authors. We conduct follow-up observations at C-band frequencies with the Green Bank Telescope as part of the ongoing Breakthrough Listen search for technosignatures. We search for narrow band signals above a signal-to-noise threshold of 10 and with Doppler drift rates within +-5 Hz/s. We detect no evidence of technosignatures from EPIC 249706694 and derive an upper limit for the EIRP (Equivalent Isotropic Radiated Power) of putative transmissions to be 10 TW.

Radio frequency interference (RFI) is a significant concern for radio astronomy, even in radio-quiet reserves. Whereas frequency-based algorithms are well suited for detecting and classifying intentional RFI (like communication links), the identification of unintentional RFI events (like those from devices such as relays or electric motors) is far more difficult. In this work we describe a novel way of dealing with time-domain unintentional RFI events. We show that irrespective of their sources, the time-domain RFI signals seem to consist of sub-impulses. These sub-impulses are akin to a canonical set of signals which we have named Canonical Interferencelets. We establish this hypothesis through a range of experiments. This method of analysing RFI signals is deemed very useful in the detection and identification of RFI events.

Radio frequency interference (RFI) is a significant concern for radio astronomy. Identifying unintentional RFI signals (for example, from equipment operating in the vicinity of radio telescopes) is a challenging topic due to the highly non-ergodic nature of such signals. Another non-ergodic signal type which has been very well researched is human speech, for which hidden Markov model-based approaches have led to some of the best performing classification algorithms. Inspired by this, in this work, we propose the use of HMMs to identify transient RFI events. We train HMMs to distinguish between the sources of several different types of RFI in a previously recorded dataset. We demonstrate that basic HMMs can be used to classify different RFI events according to their sources in the time-domain, providing useful levels of accuracy.

ObjectiveThe present paper describes the systematic development of an FFQ to assess the intake of fatty acids and antioxidants in school-aged children. In addition, a validation study applying 24 h dietary recalls was performed.DesignUsing the variance-based Max_r method, a list of eighty-two foods was compiled from data obtained by 3 d weighed dietary records. The foods were used to design an FFQ, the comprehensibility of which was evaluated in a feasibility study. In addition, the FFQ was validated in a subset of 101 children from the German Infant Nutritional Intervention Study (GINI PLUS) against one 24 h dietary recall.ResultsThe feasibility study attested a good acceptance of the FFQ. Mean intake of foods compared well between the FFQ and the 24 h dietary recall, although intake data generated from the FFQ tended to be higher. This difference became less apparent at the nutrient level, although the estimated average consumption of arachidonic acid and EPA using the FFQ still exceeded values recorded with the 24 h recall method by 45 % and 29 %, respectively.ConclusionsOn the basis of the systematic selection process of the food list, the established practicability of the FFQ and the overall plausibility of the results, the use of this FFQ is justified in future epidemiological studies.