Beecroft Institute for Particle Astrophysics and Cosmology

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.

Abstract:

Observational searches for large-scale vorticity modes in the late time Universe are underexplored. Within the standard ΛCDM model, this is well motivated given the observed properties of the cosmic microwave background (CMB). However, this means that searches for cosmic vorticity modes can serve as a powerful consistency test of our cosmological model. We show that through combining CMB measurements of the kinetic Sunyaev-Zel'dovich and the moving lens effects with galaxy survey data we can constrain vorticity fields independently from the large scale cosmic velocity field. This approach can provide stringent constraints on the largest scale modes and can be achieved by a simple change in the standard estimators. Alternatively, if one assumes there are no cosmic vorticity modes, this estimator can be used to test for systematic biases in existing analyses of kinetic Sunyaev-Zel'dovich effect in a manner analogous to curl-lensing.