All Outputs (2)

The VMC Survey – LI. Classifying extragalactic sources using a probabilistic random forest supervised machine learning algorithm (2025)
Journal Article
Pennock, C. M., van Loon, J. T., Cioni, M.-R. L., Maitra, C., Oliveira, J. M., Craig, J. E. M., …Groenewegen, M. A. T. (2025). The VMC Survey – LI. Classifying extragalactic sources using a probabilistic random forest supervised machine learning algorithm. Monthly Notices of the Royal Astronomical Society, 537(2), 1028-1055. https://doi.org/10.1093/mnras/staf080

We used a supervised machine learning algorithm (probabilistic random forest) to classify ∼130 million sources in the VISTA Survey of the Magellanic Clouds (VMC). We used multi-wavelength photometry from optical to far-infrared as features to be trai... Read More about The VMC Survey – LI. Classifying extragalactic sources using a probabilistic random forest supervised machine learning algorithm.

Considerations with stacking absorption spectra: cold H i gas in cirrus region of the Milky Way (2025)
Journal Article
Lynn, C., Marchal, A., McClure-Griffiths, N. M., Miville-Deschênes, M.-A., Murray, C. E., Nguyen, H., …Dénes, H. (2025). Considerations with stacking absorption spectra: cold H i gas in cirrus region of the Milky Way. Monthly Notices of the Royal Astronomical Society, 536(4), 3538-3553. https://doi.org/10.1093/mnras/stae2818

We use the Milky Way neutral hydrogen (H i) absorption and emission spectra from the Galactic Australian Square Kilometre Array Pathfinder (GASKAP) Phase II Pilot survey along with toy models to investigate the effects of stacking multicomponent spec... Read More about Considerations with stacking absorption spectra: cold H i gas in cirrus region of the Milky Way.