Abstract
The rapid neutron-capture (r-process) element europium (Eu) is a valuable tracer of neutron star mergers and other rare nucleosynthetic events. The stellar spectroscopic survey GALAH's (GALactic Archeology with HERMES) unique wavelength range and setup include the Eu absorption feature at similar to 6645 & Aring; for almost a million stars in the most recent Data Release 4 (DR4). However, DR4 also saw a decreased precision in reported Eu measurements compared to previous data releases. In this work, we use a convolutional neural network (CNN) to perform label transfer, wherein we use the GALAH DR4 spectra and stellar parameters to infer DR3 [Eu/H] abundances. This CNN is then applied to DR4 spectra without corresponding DR3 Eu abundances to develop a new, publicly available catalogue of [Eu/H] values for high signalto-noise targets. We include [Eu/H] predictions for 118 946 stars, out of which 54 068 giants constitute our 'golden sample' of high-confidence predictions, which pass stricter quality cuts and have a reported precision <= 0 . 1 . To overcome the scarcity of training data in the low-metallicity regime, we provide an additional catalogue of [Eu/H] abundances for metal-poor ([Fe/H] < -1) stars derived from synthesis of the Eu feature. Our ' golden sample ' can be combined with [Eu/H] values from GALAH DR3 to create a catalogue of over 100 000 vetted, high-quality abundances on a homogeneous scale. Moreover, we are able to reproduce known science results, including the elevated Eu abundances of accreted stars and previously observed Galactic chemical evolution trends. This catalogue represents one of the largest available samples of [Eu/H] abundances for high signal-to-noise targets.