Using Machine Learning to Estimate Near-ultraviolet Magnitudes and Probe Quenching Mechanisms of $z$=0 Nuggets in the RESOLVE and ECO Surveys
D. S. Carr, S. J. Kannappan, Z. L. Hutchens, M. S. Polimera, M. A. Norris, K. D. Eckert, A. J. Moffett

TL;DR
This study uses machine learning to estimate ultraviolet magnitudes and analyze quenching mechanisms in compact galaxies called nuggets at z=0, revealing links between AGN activity, star formation, and halo mass.
Contribution
The paper introduces a random forest model to predict NUV magnitudes, enabling a detailed analysis of quenching and AGN activity in local nuggets, a novel approach in this context.
Findings
Green nuggets are more similar to red nuggets in halo mass distribution.
Higher AGN frequency in green nuggets at halo masses ≥10^{11.4} M_.
AGN presence correlates with reduced star formation and gas content.
Abstract
We present a census of nuggets -- compact galaxies that form via gas-rich violent disk instability -- within the luminosity- and volume-limited REsolved Spectroscopy Of a Local VolumE (RESOLVE) and Environmental COntext (ECO) surveys. We use random forest (RF) models to predict near-ultraviolet (NUV) magnitudes for ECO galaxies that lack high-quality NUV magnitudes, thereby doubling the number of ECO galaxies with reliable extinction-corrected star formation rates (SFRs) and red/green/blue classifications based on specific SFRs (sSFRs). The resulting RF-enhanced RESOLVE+ECO nugget sample allows us to analyze rare subpopulations -- green nuggets and nuggets with active galactic nuclei (AGN) -- likely associated with quenching. Green nuggets are more similar to red nuggets than to blue nuggets in halo mass () distribution, with both red and green nuggets being found…
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Taxonomy
TopicsAtmospheric Ozone and Climate
