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:

We use numerical simulations of scalar field dark matter evolving on a moving black hole background to confirm the regime of validity of (semi)analytic expressions derived from first principles for both dynamical friction and momentum accretion in the relativistic regime. We cover both small and large clouds (relative to the de Broglie wavelength of the scalars), and light and heavy particle masses (relative to the black hole size). In the case of a small dark matter cloud, the effect of accretion is a non-negligible contribution to the total force on the black hole, even for small scalar masses. We confirm that this momentum accretion transitions between two regimes (wave and particlelike) and we identify the mass of the scalar at which the transition between regimes occurs.

Abstract:

We measure weak lensing cosmic shear power spectra from the 3-year galaxy shear catalog of the Hyper Suprime-Cam (HSC) Subaru Strategic Program imaging survey. The shear catalog covers 416  deg2 of the northern sky, with a mean i-band seeing of 0.59 arcsec and an effective galaxy number density of 15  arcmin−2 within our adopted redshift range. With an i-band magnitude limit of 24.5 mag, and four tomographic redshift bins spanning 0.3≤zph≤1.5 based on photometric redshifts, we obtain a high-significance measurement of the cosmic shear power spectra, with a signal-to-noise ratio of approximately 26.4 in the multipole range 300<ℓ<1800. The accuracy of our power spectrum measurement is tested against realistic mock shear catalogs, and we use these catalogs to get a reliable measurement of the covariance of the power spectrum measurements. We use a robust blinding procedure to avoid confirmation bias, and model various uncertainties and sources of bias in our analysis, including point spread function systematics, redshift distribution uncertainties, the intrinsic alignment of galaxies and the modeling of the matter power spectrum. For a flat ΛCDM model, we find S8≡σ8(Ωm/0.3)0.5=0.776+0.032−0.033, which is in excellent agreement with the constraints from the other HSC Year 3 cosmology analyses, as well as those from a number of other cosmic shear experiments. This result implies a ∼2σ-level tension with the Planck 2018 cosmology. We study the effect that various systematic errors and modeling choices could have on this value, and find that they can shift the best-fit value of S8 by no more than ∼0.5σ, indicating that our result is robust to such systematics.