Dr Alex Andersson

Abstract:

We present the detection of the binary system 2MASS J02132062+3648506 AB using the Karl G. Jansky Very Large Array archive data observed at 4–8 GHz. The system is a triple consisting of a tight binary () of two M dwarfs of spectral types M4.5 and M6.5 and a wide T3 brown dwarf companion (16.4 arcsec). The binary displays coronal and chromospheric activity as traced by previously measured X-ray flux and H emission. We detect the unresolved binary at a peak flux density of at a signal-to-noise ratio (SNR) of and determine a radio luminosity of . The radio emission is quiescent, polarized at a mean circular polarization fraction % and exhibits a spectral index . We probe the binary using the Enhanced Multi-Element Remotely Linked Interferometer Network (e-MERLIN) with an angular resolution of mas at 5 GHz and detect a component at a peak flux density of Jy at a SNR . We propose a gyrosynchrotron origin for the radio emission and estimate a magnetic field strength G, an emitting region of size times the radius of the M4.5 primary and a plasma number density . The brown dwarf companion is not detected. Additionally, we have analysed observations of 2MASS J04183483+213127, a chromospherically active L5 brown dwarf which is also not detected and can only place flux density upper limits at Jy and Jy for Stokes I and V, respectively.

Abstract:

The sensitivity and field of view of the MeerKAT radio telescope provide excellent opportunities for commensal transient searches. We carry out a commensal transient search in supernova and short gamma-ray burst fields using methodologies established by S. I. Chastain et al. We search for transients in MeerKAT L-band images with integration times of 30 minutes, finding 13 variable sources. We compare these sources to the VLASS and RACS survey data, and examine possible explanations for the variability. Additionally, for one of these sources we examine archival Chandra ACIS data. We find that 12 of these sources are consistent with variability due to interstellar scintillation. The remaining source could possibly have some intrinsic variability. We also split the MeerKAT L band into upper and lower halves, and search for transients in images with an integration time of 8 s. We find a source with a duration of 8–16 s that is highly polarized at the lowest frequencies. This source is spatially coincident with a star detected by the Transiting Exoplanet Survey Satellite. We conclude that this source may be consistent with a stellar flare. Finally, we calculate accurate upper and lower limits on the transient rate using transient simulations.

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%.