Breakthrough Listen

Abstract:

A programme to search for radio emission from dwarf-novae-type cataclysmic variables was conducted with the South African MeerKAT radio telescope. The dwarf novae RU Pegasi, V426 Ophiuchi, and IP Pegasi were detected during outburst at L band (1284 MHz central frequency). Previously, only one cataclysmic variable was radio-detected at a frequency this low. We now bring the number to four. With these three newly found radio-emitters, the population of dwarf novae confirmed to be radio-emitting at any frequency reaches 10 systems. We found that the radio luminosity is correlated with the optical luminosity. For V426 Ophiuchi and RU Pegasi we found a radio decline contemporary with the outburst’s optical decline. The peak radio luminosity of dwarf novae in outburst is very similar to that of novalike Cataclysmic Variables and no correlation with orbital period is seen.

Abstract:

The Breakthrough Listen program is, to date, the most extensive search for technological life beyond Earth. Over the past 9 yr, it has surveyed thousands of nearby stars and close to 100 nearby galaxies with telescopes around the world, including the Robert C. Byrd Green Bank Telescope (GBT) in West Virginia. The goal is to find evidence of technosignatures of other civilizations, such as narrowband Doppler-drifting radio signals. Despite the GBT’s location in a radio-quiet zone, the primary challenge of this search continues to be the ability to pick out genuine candidates from the high quantities of human-generated radio-frequency interference (RFI). Here we present a novel search method aimed at finding these “needle-in-a-haystack”-type signals, applied to 9684 observation cadences of 3077 stars (each observed with one or more of the L-, S-, C-, and X-band receivers) from the GBT archive. We implement a low-complexity statistical process to vet out RFI and highlight signals that, upon visual inspection, are less evidently RFI than those from previous analyses. Our work returns candidate signals found previously using both traditional and machine learning algorithms, as well as many not previously identified. This analysis represents the largest data set searched for technosignatures to date, and highlights the efficacy that traditional algorithms continue to have in these types of technosignature searches. We find that less than 1% of stars host transmitters brighter than ∼0.3 Arecibo radar equivalents broadcasting in our direction over the frequency band covered.

Abstract:

The search for radio technosignatures is an anomaly detection problem: Candidate signals represent needles of interest in the proverbial haystack of radio-frequency interference (RFI). Current search frameworks find an enormity of false-positive signals, especially in large surveys, requiring manual follow-up to a sometimes prohibitive degree. Unsupervised learning provides an algorithmic way to winnow the most anomalous signals from the chaff, as well as group together RFI signals that bear morphological similarities. We present Grouping Low-frequency Observations By Unsupervised Learning After Reduction (GLOBULAR) clustering, a signal processing method that uses hierarchical density-based spatial clustering of applications with noise (or HDBSCAN) to reduce the false-positive rate and isolate outlier signals for further analysis. When combined with a standard narrowband signal detection and spatial filtering pipeline, such as turboSETI, GLOBULAR clustering offers significant improvements in the false-positive rate over the standard pipeline alone, suggesting dramatic potential for the amelioration of manual follow-up requirements for future large surveys. By removing RFI signals in regions of high spectral occupancy, GLOBULAR clustering may also enable the detection of signals missed by the standard pipeline. We benchmark our method against the C. Choza et al. turboSETI-only search of 97 nearby galaxies at the L band, demonstrating a false-positive hit reduction rate of 93.1% and a false-positive event reduction rate of 99.3%.