A deep neural network based reverse radio spectrogram search algorithm

RAS Techniques and Instruments Oxford University Press 3:1 (2023) 33-43

Authors:

Peter Xiangyuan Ma, Steve Croft, Chris Lintott, Andrew PV Siemion

Abstract:

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 β-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 β-variational autoencoder to extract features from small (∼715 Hz, with a resolution of 2.79 Hz 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 data sets.

The discovery of a z=0.7092 OH megamaser with the MIGHTEE survey

Monthly Notices of the Royal Astronomical Society Oxford University Press 529:4 (2023) 3484-3494

Authors:

Matthew Jarvis, Ian Heywood, Anastasia Ponomareva, Rohan Varadaraj, Imogen Whittam, Hengxing Pan

Abstract:

We present the discovery of the most distant OH megamaser to be observed in the main lines, using data from the MeerKAT International Giga-Hertz Tiered Extragalactic Exploration (MIGHTEE) survey. At a newly measured redshift of 𝑧 = 0.7092, the system has strong emission in both the 1665 MHz (𝐿 ≈ 2500 L⊙) and 1667 MHz (𝐿 ≈ 4.5×104 L⊙) transitions, with both narrow and broad components. We interpret the broad line as a high-velocity-dispersion component of the 1667 MHz transition, with velocity 𝑣 ∼ 330 km s−1 with respect to the systemic velocity. The host galaxy has a stellar mass of 𝑀★ = 2.95 × 1010 M⊙ and a star-formation rate of SFR = 371 M⊙ yr−1 , placing it ∼ 1.5 dex above the main sequence for star-forming galaxies at this redshift, and can be classified as an ultra-luminous infrared galaxy. Alongside the optical imaging data, which exhibits evidence for a tidal tail, this suggests that the OH megamaser arises from a system that is currently undergoing a merger, which is stimulating star formation and providing the necessary conditions for pumping the OH molecule to saturation. The OHM is likely to be lensed, with a magnification factor of ∼ 2.5, and perhaps more if the maser emitting region is compact and suitably offset relative to the centroid of its host galaxy’s optical light. This discovery demonstrates that spectral line mapping with the new generation of radio interferometers may provide important information on the cosmic merger history of galaxies.

The Galactic Interstellar Object Population: A Framework for Prediction and Inference

The Astronomical Journal American Astronomical Society 166:6 (2023) 241

Authors:

Matthew J Hopkins, Chris Lintott, Michele T Bannister, J Ted Mackereth, John C Forbes

Bayesian Imaging for Radio Interferometry with Score-Based Priors

ArXiv 2311.18012 (2023)

Authors:

Noe Dia, MJ Yantovski-Barth, Alexandre Adam, Micah Bowles, Pablo Lemos, Anna MM Scaife, Yashar Hezaveh, Laurence Perreault-Levasseur

Galaxy Zoo DESI: Detailed morphology measurements for 8.7M galaxies in the DESI Legacy Imaging Surveys

Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) 526:3 (2023) 4768-4786

Authors:

Mike Walmsley, Tobias Géron, Sandor Kruk, Anna MM Scaife, Chris Lintott, Karen L Masters, James M Dawson, Hugh Dickinson, Lucy Fortson, Izzy L Garland, Kameswara Mantha, David O’Ryan, Jürgen Popp, Brooke Simmons, Elisabeth M Baeten, Christine Macmillan