arXiv subjects

Ivy Wong

Publications and source records attributed to Ivy Wong.

3 recordsLinked to original sources

arXiv2026

Towards independent event horizon imaging of the supermassive black holes in M87 and the Milky Way

The Event Horizon Telescope (EHT) Collaboration's images of the supermassive black holes in M87 and the Milky Way have provided the first event-horizon-scale views of these objects, opening new avenues for studies of gravitation, accretion physics, and black hole astrophysics. Achieving these results, however, requires imaging under some of the most challenging conditions in radio astronomy, including low signal-to-noise ratios, severe calibration uncertainties, and sparse aperture coverage. With the aim of presenting independent analyses of the public EHT datasets for M87* and Sgr A*, we adopt an approach that is independent in observables, and reconstruction methodology. Our framework is based on closure invariants, a class of interferometric observables that are intrinsically immune to station-based calibration errors and therefore provide robust constraints on source structure. We combine these observables with Generative Deep learning Image Reconstruction with Closure Terms (GenDIReCT), a diffusion-based image reconstruction framework that operates in the latent space of images conditioned on closure invariants. We present independent reconstructions obtained using GenDIReCT on synthetic challenge data sets as well as real EHT data on 3C279 and Centaurus A, and compare them with previously reported results. This work demonstrates the potential of closure-invariant-driven generative imaging as a calibration-resilient framework for Very Long Baseline Interferometry (VLBI) and provides an independent and complementary avenue for interpreting horizon-scale black hole observations.

astro-ph.IM↗

arXiv2023

The HI in Ring Galaxies Survey (HI-RINGS) -- Effects of the bar on the HI gas in ring galaxies

We present a new high-resolution neutral atomic hydrogen (HI) survey of ring galaxies using the Australia Telescope Compact Array (ATCA). We target a sample of 24 ring galaxies from the Buta (1995) Southern Ring Galaxy Survey Catalogue in order to study the origin of resonance-, collisional- and interaction-driven ring galaxies. In this work, we present an overview of the sample and study their global and resolved HI properties. In addition, we also probe their star formation properties by measuring their star formation rates (SFR) and their resolved SFR surface density profiles. We find that a majority of the barred galaxies in our sample are HI deficient, alluding to the effects of the bar in driving their HI deficiency. Furthermore, for the secularly evolving barred ring galaxies in our sample, we apply Lindblad's resonance theory to predict the location of the resonance rings and find very good agreement between predictions and observations. We identify rings of HI gas and/or star formation co-located at one or the other major resonances. Lastly, we measure the bar pattern speed ($Ω_{\textrm{bar}}$) for a sub-sample of our galaxies and find that the values range from 10 -- 90 km s$^{-1}$ kpc$^{-1}$, in good agreement with previous studies.

astro-ph.GA↗

arXiv2022

Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift

In this work we examine the classification accuracy and robustness of a state-of-the-art semi-supervised learning (SSL) algorithm applied to the morphological classification of radio galaxies. We test if SSL with fewer labels can achieve test accuracies comparable to the supervised state-of-the-art and whether this holds when incorporating previously unseen data. We find that for the radio galaxy classification problem considered, SSL provides additional regularisation and outperforms the baseline test accuracy. However, in contrast to model performance metrics reported on computer science benchmarking data-sets, we find that improvement is limited to a narrow range of label volumes, with performance falling off rapidly at low label volumes. Additionally, we show that SSL does not improve model calibration, regardless of whether classification is improved. Moreover, we find that when different underlying catalogues drawn from the same radio survey are used to provide the labelled and unlabelled data-sets required for SSL, a significant drop in classification performance is observered, highlighting the difficulty of applying SSL techniques under dataset shift. We show that a class-imbalanced unlabelled data pool negatively affects performance through prior probability shift, which we suggest may explain this performance drop, and that using the Frechet Distance between labelled and unlabelled data-sets as a measure of data-set shift can provide a prediction of model performance, but that for typical radio galaxy data-sets with labelled sample volumes of O(1000), the sample variance associated with this technique is high and the technique is in general not sufficiently robust to replace a train-test cycle.

astro-ph.GA↗