OAR@UM Collection:

A deep-learning search for technosignatures from 820 nearby stars

2023-01-01T00:00:00Z

Title: A deep-learning search for technosignatures from 820 nearby stars Authors: Ma, Peter Xiangyuan; Ng, Cherry; Rizk, Leandro; Croft, Steve; Siemion, Andrew P. V.; Brzycki, Bryan; Czech, Daniel; Drew, Jamie; Gajjar, Vishal; Hoang, John; Isaacson, Howard; Lebofsky, Matt; MacMahon, David H. E.; de Pater, Imke; Price, Danny C.; Sheikh, Sofia Z.; Worden, S. Pete Abstract: The goal of the search for extraterrestrial intelligence (SETI) is to quantify the prevalence of technological life beyond Earth via their ‘technosignatures’. One theorized technosignature is narrowband Doppler drifting radio signals. The principal challenge in conducting SETI in the radio domain is developing a generalized technique to reject human radiofrequency interference. Here we present a comprehensive deep-learning-based technosignature search on 820 stellar targets from the Hipparcos catalogue, totalling over 480 h of on-sky data taken with the Robert C. Byrd Green Bank Telescope as part of the Breakthrough Listen initiative. We implement a novel β-convolutional variational autoencoder to identify technosignature candidates in a semi-unsupervised manner while keeping the false-positive rate manageably low, reducing the number of candidate signals by approximately two orders of magnitude compared with previous analyses on the same dataset. Our work also returned eight promising extraterrestrial intelligence signals of interest not previously identified. Re-observations on these targets have so far not resulted in re-detections of signals with similar morphology. This machine-learning approach presents itself as a leading solution in accelerating SETI and other transient research into the age of data-driven astronomy.

Spectral templates optimal for selecting galaxies at z> 8 with the JWST

2023-01-01T00:00:00Z

Title: Spectral templates optimal for selecting galaxies at z> 8 with the JWST Authors: Larson, Rebecca L.; Hutchison, Taylor A.; Bagley, Micaela; Finkelstein, Steven L.; Yung, L. Y. Aaron; Somerville, Rachel S.; Hirschmann, Michaela; Brammer, Gabriel; Holwerda, Benne W.; Papovich, Casey; Morales, Alexa M.; Wilkins, Stephen M. Abstract: The selection of high-redshift galaxies often involves spectral energy distribution (SED) fitting to photometric data, an expectation for contamination levels, and measurement of sample completeness—all vetted through comparison to spectroscopic redshift measurements of a sub-sample. The first JWST data are now being taken over several extragalactic fields to different depths and across various areas, which will be ideal for the discovery and classification of galaxies out to distances previously uncharted. As spectroscopic redshift measurements for sources in this epoch will not be initially available to compare with the first photometric measurements of z > 8 galaxies, robust photometric redshifts are of the utmost importance. Galaxies at z > 8 are expected to have bluer rest-frame ultraviolet (UV) colors than typically used model SED templates, which could lead to catastrophic photometric redshift failures. We use a combination of BPASS and Cloudy models to create a supporting set of templates that match the predicted rest-UV colors of z > 8 simulated galaxies. We test these new templates by fitting simulated galaxies in a mock catalog, Yung et al., which mimic expected field depths and areas of the JWST Cosmic Evolution Early Release Science Survey (m5σ ∼ 28.6 over ∼100 arcmin2). We use EAZY to highlight the improvements in redshift recovery with the inclusion of our new template set and suggest criteria for selecting galaxies at 8 < z < 10 with the JWST, providing an important test case for observers venturing into this new era of astronomy.

A deep neural network based reverse radio spectrogram search algorithm

2024-01-01T00:00:00Z

Title: A deep neural network based reverse radio spectrogram search algorithm Authors: Ma, Peter Xiangyuan; Croft, Steve; Lintott, Chris; Siemion, Andrew P. V. 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.

Propagating gravitational waves in teleparallel Gauss-Bonnet gravity

2025-01-01T00:00:00Z

Title: Propagating gravitational waves in teleparallel Gauss-Bonnet gravity Authors: Mishra, Shivam Kumar; Said, Jackson; Mishra, Bivudutta Abstract: Gravitational waves offer a key insight into the viability of classes of gravitational theories beyond general relativity. The observational constraints on their speed of propagation can provide strong constraints on generalized classes of broader gravitational frameworks. In this work, we reconsider the general class of Gauss-Bonnet theories in the context of teleparallel gravity, where the background geometry is expressed through torsion. We perform tensor perturbations on a flat Friedmann–Lemaître–Robertson–Walker background, and derive the gravitational wave propagation equation. We find that gravitational waves propagate at the speed of light in these classes of theories. We also derive the distance-duality relationship for radiation propagating in the gravitational wave and electromagnetic domains.