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Abstract:

The black hole transient GRS 1915+105 entered a new phase of activity in 2018, generally characterised by low X-ray and radio fluxes. This phase has only been interrupted by episodes of strong and variable radio emission, where high levels of X-ray absorption local to the source were measured. We present 18 epochs of near-infrared spectroscopy (2018–2023) obtained with GTC/EMIR and VLT/X-shooter, spanning both radio-loud and radio-quiet periods. We demonstrate that radio-loud phases are characterised by strong P-Cygni line profiles, indicative of accretion disc winds with velocities of up to ∼3000 km s−1. This velocity is consistent with those measured in other black hole transients. It is also comparable to the velocity of the X-ray winds detected during the peak outburst phases in GRS 1915+105, reinforcing the idea that massive, multi-phase outflows are characteristic features of the largest and most powerful black hole accretion discs. Conversely, the evolution of the Brγ line profile during the radio-quiet phases follows the expected trend for accretion disc lines in a system that is gradually decreasing its intrinsic luminosity, exhibiting weaker intensities and more pronounced double-peaks

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.