Breakthrough Listen

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%.

Abstract:

<jats:title>ABSTRACT</jats:title> <jats:p>In this work, we explore the applicability of unsupervised machine learning algorithms to finding radio transients. Facilities such as the Square Kilometre Array (SKA) will provide huge volumes of data in which to detect rare transients; the challenge for astronomers is how to find them. We demonstrate the effectiveness of anomaly detection algorithms using 1.3 GHz light curves from the SKA precursor MeerKAT. We make use of three sets of descriptive parameters (‘feature sets’) as applied to two anomaly detection techniques in the astronomaly package and analyse our performance by comparison with citizen science labels on the same data set. Using transients found by volunteers as our ground truth, we demonstrate that anomaly detection techniques can recall over half of the radio transients in the 10 per cent of the data with the highest anomaly scores. We find that the choice of anomaly detection algorithm makes a minor difference, but that feature set choice is crucial, especially when considering available resources for human inspection and/or follow-up. Active learning, where human labels are given for just 2 per cent of the data, improves recall by up to 20 percentage points, depending on the combination of features and model used. The best-performing results produce a factor of 5 times fewer sources requiring vetting by experts. This is the first effort to apply anomaly detection techniques to finding radio transients and shows great promise for application to other data sets, and as a real-time transient detection system for upcoming large surveys.</jats:p>

Abstract:

We report radio monitoring of Cygnus X-3 at 15.5 GHz during 2024 with the Arcminute Microkelvin Imager. Observations were made on 296 days throughout the year, and reveal five radio outbursts to multi-jansky levels, peaking in February, April, June, July and August. The brightest peak, with ≈16 Jy, was on June 27th.

Abstract:

In this work, we study the circumstellar material (CSM) around massive stars, and the mass-loss rates depositing this CSM, using a large sample of radio observations of 325 core-collapse supernovae (CCSNe; only ~22% of them being detected). This sample comprises both archival data and our new observations of 99 CCSNe conducted with the AMI-LA radio array in a systematic approach devised to constrain the mass loss at different stages of stellar evolution. In the supernova (SN)–CSM interaction model, observing the peak of the radio emission of an SN provides the CSM density at a given radius (and therefore the mass-loss rate that deposited this CSM). On the other hand, limits on the radio emission, and/or on the peak of the radio emission provide a region in the CSM phase space that can be ruled out. Our analysis shows a discrepancy between the values of mass-loss rates derived from radio-detected and radio-nondetected SNe. Furthermore, we rule out mass-loss rates in the range of 2 × 10−6–10−4 M⊙ yr−1 for different epochs during the last 1000 yr before the explosion (assuming wind velocity of 10 km s−1) for the progenitors of ~80% of the Type II supernovae (SNe II) in our sample. In addition, we rule out the ranges of mass-loss rates suggested for red supergiants for ~50% of the progenitors of SNe II in our sample. We emphasize here that these results take a step forward in constraining mass loss in winds from a statistical point of view.