102 citations · 399 across the 15 of their papers we have counts for
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Efficient Search for Extremely Metal Poor Galaxies in the Local Universe using Convolutional Neural Networks
Ting-Yun Cheng, Ryan J. Cooke
Nearby extremely metal-poor galaxies (XMPs) allow us to study primitive galaxy formation and evolution in greater detail than is possible at high redshift. This work, for the first…
Towards ultra metal-poor DLAs: linking the chemistry of the most metal-poor DLA to the first stars
Louise Welsh, Ryan Cooke, Michele Fumagalli +1
We present new Keck/HIRES data of the most metal-poor damped Lyman-alpha (DLA) system currently known. By targeting the strongest accessible Fe II features, we have improved the up…
MUSE Analysis of Gas around Galaxies (MAGG) -- IV: The gaseous environment of 3-4 Lyman-alpha emitting galaxies
Emma K. Lofthouse, Michele Fumagalli, Matteo Fossati +9
We study the link between galaxies and HI-selected absorption systems at z~3-4 in the MUSE Analysis of Gas around Galaxies (MAGG) survey, an ESO large programme consisting of integ…
Lessons Learned from the Two Largest Galaxy Morphological Classification Catalogues built by Convolutional Neural Networks
Ting-Yun Cheng, H. Domínguez Sánchez, J. Vega-Ferrero +54
We compare the two largest galaxy morphology catalogues, which separate early and late type galaxies at intermediate redshift. The two catalogues were built by applying supervised…
Harvesting the Lyα forest with convolutional neural networks
Ting-Yun Cheng, Ryan Cooke, Gwen Rudie
We develop a machine learning based algorithm using a convolutional neural network (CNN) to identify low HI column density Ly absorption systems ($\log{N_{\mathrm{HI}}}/{\rm cm}…
Oxygen-enhanced extremely metal-poor DLAs: A signpost of the first stars?
Louise Welsh, Ryan Cooke, Michele Fumagalli +1
We present precise abundance determinations of two near-pristine damped Ly systems (DLAs) to assess the nature of the [O/Fe] ratio at [Fe/H] < -3 (i.e. <1/1000 of the solar meta…