arXiv subjects

Alexander J. Gordon

Publications and source records attributed to Alexander J. Gordon.

4 recordsLinked to original sources

arXiv2026

Linking enhanced star formation and quenching to faint tidal features in galaxies

Galaxy mergers and interactions have long been suggested as a significant driver of galaxy evolution. However, the exact extent to which mergers enhance star formation and AGN activity has been challenging to establish observationally. In previous work, we visually classified a sample of galaxies with various types of faint tidal features in DECaLS images. In this paper, we cross-correlate this sample with SDSS-derived data to investigate how the presence and specific nature of these features correlates with intense star formation and AGN activity. Averaged over all tidal classes, we find that our 688 tidal feature galaxies are 6.6$\pm$0.9 times more likely to be in a starburst phase and 19.6$\pm$5.0 times more likely to have rapidly quenched (post-starbursts) than a sample of 4073 controls matched in both stellar mass and redshift. Examining differences between tidal classes, galaxies with \textit{arm} features were $\sim$1.3-4.0 times more likely to be starbursting than the other categories, while those with \textit{shell} features were $\sim$2.3-5.3 times more likely to be in a quiescent state. In a similar analysis, we identify which galaxies show evidence of AGN activity (from a sample of $\sim$2100) and find no significant difference between those with or without tidal features. Overall, our results reinforce the notion that mergers play an important role in driving star formation and rapid quenching in galaxies, and provide some of the first empirical evidence that the strength of this effect has a dependence on the detailed nature of the interaction, as traced by the tidal feature morphology.

astro-ph.GA↗

arXiv2025

Galaxy Zoo Evo: 1 million human-annotated images of galaxies

We introduce Galaxy Zoo Evo, a labeled dataset for building and evaluating foundation models on images of galaxies. GZ Evo includes 104M crowdsourced labels for 823k images from four telescopes. Each image is labeled with a series of fine-grained questions and answers (e.g. "featured galaxy, two spiral arms, tightly wound, merging with another galaxy"). These detailed labels are useful for pretraining or finetuning. We also include four smaller sets of labels (167k galaxies in total) for downstream tasks of specific interest to astronomers, including finding strong lenses and describing galaxies from the new space telescope Euclid. We hope GZ Evo will serve as a real-world benchmark for computer vision topics such as domain adaption (from terrestrial to astronomical, or between telescopes) or learning under uncertainty from crowdsourced labels. We also hope it will support a new generation of foundation models for astronomy; such models will be critical to future astronomers seeking to better understand our universe.

astro-ph.IM↗

arXiv2024

Uncovering Tidal Treasures: Automated Classification of Faint Tidal Features in DECaLS Data

Tidal features are a key observable prediction of the hierarchical model of galaxy formation and contain a wealth of information about the properties and history of a galaxy. Modern wide-field surveys such as LSST and Euclid will revolutionise the study of tidal features. However, the volume of data will prohibit visual inspection to identify features, thereby motivating a need to develop automated detection methods. This paper presents a visual classification of $\sim2,000$ galaxies from the DECaLS survey into different tidal feature categories: arms, streams, shells, and diffuse. We trained a Convolutional Neural Network (CNN) to reproduce the assigned visual classifications using these labels. Evaluated on a testing set where galaxies with tidal features were outnumbered $\sim1:10$, our network performed very well and retrieved a median $98.7\pm0.3$, $99.1\pm0.5$, $97.0\pm0.8$, and $99.4^{+0.2}_{-0.6}$ per cent of the actual instances of arm, stream, shell, and diffuse features respectively for just 20 per cent contamination. A modified version that identified galaxies with any feature against those without achieved scores of $0.981^{+0.001}_{-0.003}$, $0.834^{+0.014}_{-0.026}$, $0.974^{+0.008}_{-0.004}$, and $0.900^{+0.073}_{-0.015}$ for the accuracy, precision, recall, and F1 metrics, respectively. We used a Gradient-weighted Class Activation Mapping analysis to highlight important regions on images for a given classification to verify the network was classifying the galaxies correctly. This is the first demonstration of using CNNs to classify tidal features into sub-categories, and it will pave the way for the identification of different categories of tidal features in the vast samples of galaxies that forthcoming wide-field surveys will deliver.

astro-ph.GA↗

arXiv2024

Scaling Laws for Galaxy Images

We present the first systematic investigation of supervised scaling laws outside of an ImageNet-like context - on images of galaxies. We use 840k galaxy images and over 100M annotations by Galaxy Zoo volunteers, comparable in scale to Imagenet-1K. We find that adding annotated galaxy images provides a power law improvement in performance across all architectures and all tasks, while adding trainable parameters is effective only for some (typically more subjectively challenging) tasks. We then compare the downstream performance of finetuned models pretrained on either ImageNet-12k alone vs. additionally pretrained on our galaxy images. We achieve an average relative error rate reduction of 31% across 5 downstream tasks of scientific interest. Our finetuned models are more label-efficient and, unlike their ImageNet-12k-pretrained equivalents, often achieve linear transfer performance equal to that of end-to-end finetuning. We find relatively modest additional downstream benefits from scaling model size, implying that scaling alone is not sufficient to address our domain gap, and suggest that practitioners with qualitatively different images might benefit more from in-domain adaption followed by targeted downstream labelling.

cs.CV↗