Andrew Garner | Searcharxiv

arXiv2025

A value-added catalogue of neural network-based europium abundances for GALAH DR4

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 unique wavelength range and setup include the Eu absorption feature at $\sim6645$ \r{A} 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 signal-to-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 $\lesssim0.1$. To overcome the scarcity of training data in the low metallicity regime, we provide an additional catalogue of [Eu/H] abundances for metal poor ($\mathrm{[Fe/H]}<-1$) 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.

astro-ph.GA↗

arXiv2021

Provable superior accuracy in machine learned quantum models

In modelling complex processes, the potential past data that influence future expectations are immense. Models that track all this data are not only computationally wasteful but also shed little light on what past data most influence the future. There is thus enormous interest in dimensional reduction-finding automated means to reduce the memory dimension of our models while minimizing its impact on its predictive accuracy. Here we construct dimensionally reduced quantum models by machine learning methods that can achieve greater accuracy than provably optimal classical counterparts. We demonstrate this advantage on present-day quantum computing hardware. Our algorithm works directly off classical time-series data and can thus be deployed in real-world settings. These techniques illustrate the immediate relevance of quantum technologies to time-series analysis and offer a rare instance where the resulting quantum advantage can be provably established.

quant-ph↗