arXiv subjects

C. S. Ong

Publications and source records attributed to C. S. Ong.

3 recordsLinked to original sources

arXiv2026

Structural and magnetic phases of topological kagome metal Fe$_3$Sn$_2$ under pressure

We investigate the pressure-induced evolution of crystal structure and magnetism in the kagome ferromagnet Fe$_3$Sn$_2$ by combining X-ray diffraction, X-ray Emission Spectroscopy, X-ray Magnetic Circular Dichroism, and spin-polarized density functional theory calculations. X-ray diffraction reveals a structural phase transition above $\sim$20~GPa, which coincides with a pronounced reduction of the local Fe magnetic moment evidenced by X-ray emission spectroscopy, indicating a high-spin to low-spin transition. While XES probes the amplitude of the local moment, XMCD provides direct information on the orientation of the ordered magnetic moments and uncovers a rich pressure--temperature magnetic phase diagram. At room temperature, a collinear ferromagnetic phase with moments aligned along the $c$ axis persists up to the structural transition. At low temperature, a tilted magnetic configuration remains stable to significantly higher pressures, while at intermediate temperatures pressure stabilizes the low-temperature magnetic phase at the expense of the high-temperature one. Spin-polarized first-principles calculations show that, although isotropic ferromagnetic exchange interactions remain robust under compression, pressure enhances spin--orbit--driven magnetic anisotropy and Dzyaloshinskii--Moriya interactions, favoring non-collinear magnetic configurations. Our results demonstrate that pressure reshapes the magnetic energy landscape of Fe$_3$Sn$_2$ by coupling lattice, spin state, and relativistic magnetic interactions, establishing hydrostatic pressure as an effective control parameter to engineer magnetic anisotropy and potentially topological phases in kagome materials.

cond-mat.str-el↗

arXiv2021

Interpretable Faraday Complexity Classification

Faraday complexity describes whether a spectropolarimetric observation has simple or complex magnetic structure. Quickly determining the Faraday complexity of a spectropolarimetric observation is important for processing large, polarised radio surveys. Finding simple sources lets us build rotation measure grids, and finding complex sources lets us follow these sources up with slower analysis techniques or further observations. We introduce five features that can be used to train simple, interpretable machine learning classifiers for estimating Faraday complexity. We train logistic regression and extreme gradient boosted tree classifiers on simulated polarised spectra using our features, analyse their behaviour, and demonstrate our features are effective for both simulated and real data. This is the first application of machine learning methods to real spectropolarimetry data. With 95 per cent accuracy on simulated ASKAP data and 90 per cent accuracy on simulated ATCA data, our method performs comparably to state-of-the-art convolutional neural networks while being simpler and easier to interpret. Logistic regression trained with our features behaves sensibly on real data and its outputs are useful for sorting polarised sources by apparent Faraday complexity.

astro-ph.IM↗

arXiv2018

Radio Galaxy Zoo: Machine learning for radio source host galaxy cross-identification

We consider the problem of determining the host galaxies of radio sources by cross-identification. This has traditionally been done manually, which will be intractable for wide-area radio surveys like the Evolutionary Map of the Universe (EMU). Automated cross-identification will be critical for these future surveys, and machine learning may provide the tools to develop such methods. We apply a standard approach from computer vision to cross-identification, introducing one possible way of automating this problem, and explore the pros and cons of this approach. We apply our method to the 1.4 GHz Australian Telescope Large Area Survey (ATLAS) observations of the Chandra Deep Field South (CDFS) and the ESO Large Area ISO Survey South 1 (ELAIS-S1) fields by cross-identifying them with the Spitzer Wide-area Infrared Extragalactic (SWIRE) survey. We train our method with two sets of data: expert cross-identifications of CDFS from the initial ATLAS data release and crowdsourced cross-identifications of CDFS from Radio Galaxy Zoo. We found that a simple strategy of cross-identifying a radio component with the nearest galaxy performs comparably to our more complex methods, though our estimated best-case performance is near 100 per cent. ATLAS contains 87 complex radio sources that have been cross-identified by experts, so there are not enough complex examples to learn how to cross-identify them accurately. Much larger datasets are therefore required for training methods like ours. We also show that training our method on Radio Galaxy Zoo cross-identifications gives comparable results to training on expert cross-identifications, demonstrating the value of crowdsourced training data.

astro-ph.IM↗