Rubin-LSST

Abstract:

The search for radio technosignatures is an anomaly detection problem: Candidate signals represent needles of interest in the proverbial haystack of radio-frequency interference (RFI). Current search frameworks find an enormity of false-positive signals, especially in large surveys, requiring manual follow-up to a sometimes prohibitive degree. Unsupervised learning provides an algorithmic way to winnow the most anomalous signals from the chaff, as well as group together RFI signals that bear morphological similarities. We present Grouping Low-frequency Observations By Unsupervised Learning After Reduction (GLOBULAR) clustering, a signal processing method that uses hierarchical density-based spatial clustering of applications with noise (or HDBSCAN) to reduce the false-positive rate and isolate outlier signals for further analysis. When combined with a standard narrowband signal detection and spatial filtering pipeline, such as turboSETI, GLOBULAR clustering offers significant improvements in the false-positive rate over the standard pipeline alone, suggesting dramatic potential for the amelioration of manual follow-up requirements for future large surveys. By removing RFI signals in regions of high spectral occupancy, GLOBULAR clustering may also enable the detection of signals missed by the standard pipeline. We benchmark our method against the C. Choza et al. turboSETI-only search of 97 nearby galaxies at the L band, demonstrating a false-positive hit reduction rate of 93.1% and a false-positive event reduction rate of 99.3%.

Abstract:

We present measurements of the neutral atomic hydrogen (H i) mass function (HiMF) and cosmic H i density (ΩH I) at 0 ≤ z ≤ 0.088 from the Looking at the Distant Universe with MeerKAT Array (LADUMA) survey. Using LADUMA Data Release 1 (DR1), we analyze the HiMF via a new “recovery matrix” method that we benchmark against a more traditional modified maximum likelihood (MML) method. Our analysis, which implements a forward modeling approach, corrects for survey incompleteness and uses extensive synthetic source injections to ensure robust estimates of the HiMF parameters and their associated uncertainties. This new method tracks the recovery of sources in mass bins different from those in which they were injected and incorporates a Poisson likelihood in the forward modeling process, allowing it to correctly handle uncertainties in bins with few or no detections. The application of our analysis to a high-purity subsample of the LADUMA DR1 spectral line catalog in turn mitigates any possible biases that could result from the inconsistent treatment of synthetic and real sources. For the surveyed redshift range, the recovered Schechter function normalization, low-mass slope, and “knee” mass are ϕ*=3.56−1.92+0.97×10−3 Mpc−3 dex−1, α=−1.18−0.19+0.08 , and log(M*/M⊙)=10.01−0.12+0.31 , respectively, which together imply a comoving cosmic H i density of ΩHI=3.09−0.47+0.65×10−4 . Our results show consistency between recovery matrix and MML methods and with previous low-redshift studies, giving confidence that the cosmic volume probed by LADUMA, even at low redshifts, is not an outlier in terms of its H i content.