Hintze Centre for Astrophysical Surveys

Abstract:

We present the Virtual Research Assistant (VRA) of the ATLAS sky survey, which performs preliminary eyeballing on our clean transient data stream. The VRA uses histogram-based gradient-boosted decision tree classifiers trained on real data to score incoming alerts on two axes: “Real” and “Galactic.” The alerts are then ranked using a geometric distance such that the most “real” and “extragalactic” receive high scores; the scores are updated when new lightcurve data is obtained on subsequent visits. To assess the quality of the training we use the recall at rank K, which is more informative to our science goal than general metrics (e.g., accuracy, F1-scores). We also establish benchmarks for our metric based on the pre-VRA eyeballing strategy, to ensure our models provide notable improvements before being added to the ATLAS pipeline. Then, policies are defined on the ranked list to select the most promising alerts for humans to eyeball and to automatically remove bogus alerts. In production the VRA method has resulted in a reduction in eyeballing workload by 85% with a loss of follow-up opportunity <0.08%. It also allows us to automatically trigger follow-up observations with the Lesedi telescope, paving the way toward automated methods that will be required in the era of LSST. Finally, this is a demonstration that feature-based methods remain extremely relevant in our field, being trainable on only a few thousand samples and highly interpretable; they also offer a direct way to inject expertise into models through feature engineering.

Abstract:

Radio continuum emission provides a unique opportunity to study star formation unbiased by dust obscuration. However, if radio observations are to be used to accurately trace star formation to high redshifts, it is crucial that the physical processes that affect the radio emission from star-forming galaxies are well understood. While inverse Compton (IC) losses from the cosmic microwave background (CMB) are negligible in the local universe, the rapid increase in the strength of the CMB energy density with redshift [] means that this effect becomes increasingly important at . Using a sample of high-redshift () Lyman-break galaxies selected in the rest-frame ultraviolet (UV), we have stacked radio observations from the MIGHTEE survey to estimate their 1.4-GHz flux densities. We find that for a given rest-frame UV magnitude, the 1.4-GHz flux density and luminosity decrease with redshift. We compare these results to the theoretical predicted effect of energy losses due to IC scattering off the CMB, and find that the observed decrease is consistent with this explanation. We discuss other possible causes for the observed decrease in radio flux density with redshift at a given UV magnitude, such as a top-heavy initial mass function at high redshift or an evolution of the dust properties, but suggest that IC scattering is the most compelling explanation.

Abstract:

Stellar bars are key structures in disc galaxies, driving angular momentum redistribution and influencing processes such as bulge growth and star formation. Quantifying the bar fraction as a function of redshift and stellar mass is therefore important for constraining the physical processes that drive disc formation and evolution across the history of the Universe. Leveraging the unprecedented resolution and survey area of the Euclid Q1 data release, combined with the Zoobot deep-learning model trained on citizen science labels, we identified (7711) barred galaxies with (M_* ≳ 10^ M_⊙) in a magnitude-selected sample ((I_E < 20.5)) spanning (63.1 deg ^2). We measured a mean bar fraction of (0.2-0.4), consistent with previous studies. At fixed redshift, massive galaxies exhibit higher bar fractions, while lower-mass systems show a steeper decline with redshift, suggesting earlier disc assembly in massive galaxies. Comparisons with cosmological simulations (e.g. TNG50 Auriga ) reveal a broadly consistent bar fraction, but highlight overpredictions for high-mass systems, pointing to potential over-efficiency in central stellar mass build-up in simulations. A semi-empirical model ( Decode ) in which bar formation is regulated by the gas fraction broadly reproduces the observed bar fractions. These findings demonstrate the transformative potential of Euclid for galaxy morphology studies and underscore the importance of refining theoretical models to better reproduce observed trends. Future work will explore finer mass bins, environmental correlations, and additional morphological indicators.