All-sky Radio SETI

Over the last decade, Aperture Arrays (AA) have successfully replaced parabolic dishes as the technology of choice at low radio frequencies - good examples are the MWA, LWA and LOFAR. Aperture Array based telescopes present several advantages, including sensitivity to the sky over a very wide field-of-view. As digital and data processing systems continue to advance, an all-sky capability is set to emerge, even at GHz frequencies. We argue that assuming SETI events are both rare and transitory in nature, an instrument with a large field-of-view, operating around the so-called water-hole (1-2 GHz), might offer several advantages over contemporary searches. Sir Arthur C. Clarke was the first to recognise the potential importance of an all-sky radio SETI capability, as presented in his book, Imperial Earth. As part of the global SKA (Square Kilometre Array) project, a Mid-Frequency Aperture Array (MFAA) prototype known as MANTIS (Mid- Frequency Aperture Array Transient and Intensity-Mapping System) is now being considered as a precursor for SKA-2. MANTIS can be seen as a first step towards an all-sky radio SETI capability at GHz frequencies. This development has the potential to transform the field of SETI research, in addition to several other scientific programmes.Comment: 7 pages, 4 figures, accepted for publication, Proceedings of Science, workshop on "MeerKAT Science: On the Pathway to the SKA", held in Stellenbosch 25-27 May 2016. Comments welcom

Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach

We report the detection of 72 new pulses from the repeating fast radio burst FRB 121102 in Breakthrough Listen C-band (4-8 GHz) observations at the Green Bank Telescope. The new pulses were found with a convolutional neural network in data taken on August 26, 2017, where 21 bursts have been previously detected. Our technique combines neural network detection with dedispersion verification. For the current application we demonstrate its advantage over a traditional brute-force dedis- persion algorithm in terms of higher sensitivity, lower false positive rates, and faster computational speed. Together with the 21 previously reported pulses, this observa- tion marks the highest number of FRB 121102 pulses from a single observation, total- ing 93 pulses in five hours, including 45 pulses within the first 30 minutes. The number of data points reveal trends in pulse fluence, pulse detection rate, and pulse frequency structure. We introduce a new periodicity search technique, based on the Rayleigh test, to analyze the time of arrivals, with which we exclude with 99% confidence pe- riodicity in time of arrivals with periods larger than 5.1 times the model-dependent time-stamp uncertainty. In particular, we rule out constant periods >10 ms in the barycentric arrival times, though intrinsic periodicity in the time of emission remains plausible.Comment: 32 pages, 10 figure

Background Contamination of the Project Hephaistos Dyson Spheres Candidates

Project Hephaistos recently identified seven M-dwarfs as possible Dyson Spheres (DS) candidates. We have cross-matched three of these candidates (A, B and G) with radio sources detected in various all-sky surveys. The radio sources are offset from the Gaia stellar positions by ∼4.9, ∼0.4 and ∼5.″0 for candidates A, B, and G respectively. We propose that Dust obscured galaxies (DOGs) lying close to the line-of-sight of these M-dwarf stars significantly contribute to the measured WISE mid-IR flux densities in the WISE W3 and W4 wave bands. These three stars have therefore been misidentified as DS candidates. We also note that with an areal sky density of 9 × 10−6 per square arcsecond, Hot DOGs can probably account for the contamination of all 7 DS candidates drawn from an original sample of 5 million stars

A Deep Neural Network Based Reverse Radio Spectrogram Search Algorithm

Modern radio astronomy instruments generate vast amounts of data, and the increasingly challenging radio frequency interference (RFI) environment necessitates ever-more sophisticated RFI rejection algorithms. The "needle in a haystack" nature of searches for transients and technosignatures requires us to develop methods that can determine whether a signal of interest has unique properties, or is a part of some larger set of pernicious RFI. In the past, this vetting has required onerous manual inspection of very large numbers of signals. In this paper we present a fast and modular deep learning algorithm to search for lookalike signals of interest in radio spectrogram data. First, we trained a B-Variational Autoencoder on signals returned by an energy detection algorithm. We then adapted a positional embedding layer from classical Transformer architecture to a embed additional metadata, which we demonstrate using a frequency-based embedding. Next we used the encoder component of the B-Variational 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. This algorithm can be used to improve the efficiency of vetting signals of interest in technosignature searches, but could also be applied to a wider variety of searches for "lookalike" signals in large astronomical datasets.Comment: 8 pages, 8 figure