arXiv subjects

D. O'Ryan

Publications and source records attributed to D. O'Ryan.

8 recordsLinked to original sources

arXiv2026

Euclid: Quick Data Release (Q1) -- Exploring the detailed visual morphology of galaxies in clusters

Galaxy clusters provide unique laboratories for studying environmental effects on galaxy evolution. The morphology--density ($T$--$\Sigma$) and morphology--cluster-centric radius ($T$--$R$) relations trace how galaxy morphology is influenced by the environment, but previous studies at intermediate redshifts have been limited in both sample size and radial coverage. We use the Euclid Quick Data Release 1 (Q1) visual morphology catalogue to measure the $T$--$\Sigma$ and $T$--$R$ relations for smooth, featured-or-disc, and barred galaxies in known clusters at $0.2\leq z\leq 0.5$, extending this analysis to large cluster-centric distances ($3 R_{500c}$) and studying their dependence on stellar mass. Using photometric redshifts and stellar masses provided by Euclid, we identify 1754 cluster members within $1.5 R_{500c}$, distributed across 71 clusters. We classify the identified galaxies as smooth, featured-or-disc, or barred using the predicted vote fractions provided by the Zoobot deep learning foundation model in the Q1 visual morphology catalogue. We confirm the $T$--$\Sigma$ relation in all the stellar mass ranges studied, with a stronger influence of the cluster environment on galaxy morphology in the densest parts of the cluster and closer to the cluster centre. Beyond $1.5 R_{500c}$, the morphological segregation weakens, with featured-or-disc galaxies overtaking smooth galaxies at the lowest densities, consistent with the growing contribution of field interlopers in the cluster outskirts. For barred galaxies, we find a tentative decline of the bar fraction toward lower densities that is most pronounced for the most massive galaxies. In conclusion, our results show that the cluster environment drives a progressive transformation of galaxy morphology, with the loss of disc structure becoming more pronounced towards the cluster core, where environmental processes act most efficiently.

astro-ph.GA↗

arXiv2025

Euclid Quick Data Release (Q1). First Euclid statistical study of galaxy mergers and their connection to active galactic nuclei

Galaxy major mergers are a key pathway to trigger AGN. We present the first detection of major mergers in the Euclid Deep Fields and analyse their connection with AGN. We constructed a stellar-mass-complete ($M_*>10^{9.8}\,M_{\odot}$) sample of galaxies from the first quick data release (Q1), in the redshift range z=0.5-2. We selected AGN using X-ray data, optical spectroscopy, mid-infrared colours, and processing \IE observations with an image decomposition algorithm. We used CNNs trained on cosmological simulations to classify galaxies as mergers and non-mergers. We found a larger fraction of AGN in mergers compared to the non-merger controls for all AGN selections, with AGN excess factors ranging from 2 to 6. Likewise, a generally larger merger fraction ($f_{merg}$) is seen in active galaxies than in the non-active controls. We analysed $f_{merg}$ as a function of the AGN bolometric luminosity ($L_{bol}$) and the contribution of the point-source to the total galaxy light in the \IE-band ($f_{PSF}$) as a proxy for the relative AGN contribution fraction. We uncovered a rising $f_{merg}$, with increasing $f_{PSF}$ up to $f_{PSF}=0.55$, after which we observed a decreasing trend. We then derived the point-source luminosity ($L_{PSF}$) and showed that $f_{merg}$ monotonically increases as a function of $L_{PSF}$ at z<0.9, with $f_{merg}>$50% for $L_{PSF}>2\,10^{43}$ erg/s. At z>0.9, $f_{merg}$ rises as a function of $L_{PSF}$, though mergers do not dominate until $L_{PSF}=10^{45}$ erg/s. For X-ray and spectroscopic AGN, we computed $L_{bol}$, which has a positive correlation with $f_{merg}$ for X-ray AGN, while shows a less pronounced trend for spectroscopic AGN due to the smaller sample size. At $L_{bol}>10^{45}$ erg/s, AGN mostly reside in mergers. We concluded that mergers are strongly linked to the most powerful, dust-obscured AGN, associated with rapid supermassive black hole growth.

astro-ph.GA↗

arXiv2025

Timescales for the Effects of Interactions on Galaxy Properties and SMBH Growth

Galaxy interaction and merging have clear effects on the systems involved. We find an increase in the star formation rate (SFR), potential ignition of active galactic nuclei (AGN) and significant morphology changes. However, at what stage during interactions or mergers these changes begin to occur remains an open question. With a combination of machine learning and visual classification, we select a sample of 3,162 interacting and merging galaxies in the Cosmic Evolutionary Survey (COSMOS) field across a redshift range of 0.0 - 1.2. We divide this sample into four distinct stages of interaction based on their morphology, each stage representing a different phase of the dynamical timescale. We use the rich ancillary data available in COSMOS to probe the relation between interaction stage, stellar mass, SFR, and AGN fraction. We find that the distribution of SFRs rapidly change with stage for mass distributions consistent with being drawn from the same parent sample. This is driven by a decrease in the fraction of red sequence galaxies (from 17% as close pairs to 1.4% during merging) and an increase in the fraction of starburst galaxies (from 7% to 32%). We find the AGN fraction increases by a factor of 1.2 only at coalescence. We find the effects of interaction peak at the point of closest approach and coalescence of the two systems. We show that the point in time of the underlying dynamical timescale - and its related morphology - is as important to consider as its projected separation.

astro-ph.GA↗

arXiv2025

Galaxy Zoo JWST: Up to 75% of discs are featureless at $3<z<7$

We have not yet observed the epoch at which disc galaxies emerge in the Universe. While high-$z$ measurements of large-scale features such as bars and spiral arms trace the evolution of disc galaxies, such methods cannot directly quantify featureless discs in the early Universe. Here we identify a substantial population of apparently featureless disc galaxies in the Cosmic Evolution Early Release Science (CEERS) survey by combining quantitative visual morphologies of $\sim 7,000$ galaxies from the Galaxy Zoo JWST CEERS project with a public catalogue of expert visual and parametric morphologies. While the highest-redshift featured disc we identify is at $z_{\rm{phot}}=5.5$, the highest-redshift featureless disc we identify is at $z_{\rm{phot}}=7.4$. The distribution of Sérsic indices for these featureless systems suggests that they truly are dynamically cold: disc-dominated systems have existed since at least $z\sim 7.4$. We place upper limits on the featureless disc fraction as a function of redshift, and show that up to $75\%$ of discs are featureless at $3.0<z<7.4$. This is a conservative limit assuming all galaxies in the sample truly lack features. With further consideration of redshift effects and observational constraints, we find the featureless disc fraction in CEERS imaging at these redshifts is more likely $\sim29-38\%$. We hypothesise that the apparent lack of features in a third of high-redshift discs is due to a higher gas fraction in the early Universe, which allows the discs to be resistant to buckling and instabilities.

astro-ph.GA↗

arXiv2025

Euclid Quick Data Release (Q1): First visual morphology catalogue

We present a detailed visual morphology catalogue for Euclid's Quick Release 1 (Q1). Our catalogue includes galaxy features such as bars, spiral arms, and ongoing mergers, for the 378000 bright ($I_E < 20.5$) or extended (area $\geq 700\,$pixels) galaxies in Q1. The catalogue was created by finetuning the Zoobot galaxy foundation models on annotations from an intensive one month campaign by Galaxy Zoo volunteers. Our measurements are fully automated and hence fully scaleable. This catalogue is the first 0.4% of the approximately 100 million galaxies where Euclid will ultimately resolve detailed morphology.

astro-ph.GA↗

arXiv2025

Euclid Quick Data Release (Q1), A first look at the fraction of bars in massive galaxies at $z<1$

Stellar bars are key structures in disc galaxies, driving angular momentum redistribution and influencing processes such as bulge growth and star formation. Quantifying the bar fraction as a function of redshift and stellar mass is therefore important for constraining the physical processes that drive disc formation and evolution across the history of the Universe. Leveraging the unprecedented resolution and survey area of the Euclid Q1 data release combined with the Zoobot deep-learning model trained on citizen-science labels, we identify 7711 barred galaxies with $M_* \gtrsim 10^{10}M_\odot$ in a magnitude-selected sample $I_E < 20.5$ spanning $63.1 deg^2$. We measure a mean bar fraction of $0.2-0.4$, consistent with prior studies. At fixed redshift, massive galaxies exhibit higher bar fractions, while lower-mass systems show a steeper decline with redshift, suggesting earlier disc assembly in massive galaxies. Comparisons with cosmological simulations (e.g., TNG50, Auriga) reveal a broadly consistent bar fraction, but highlight overpredictions for high-mass systems, pointing to potential over-efficiency in central stellar mass build-up in simulations. These findings demonstrate Euclid's transformative potential for galaxy morphology studies and underscore the importance of refining theoretical models to better reproduce observed trends. Future work will explore finer mass bins, environmental correlations, and additional morphological indicators.

astro-ph.GA↗

arXiv2024

Galaxy merger challenge: A comparison study between machine learning-based detection methods

Various galaxy merger detection methods have been applied to diverse datasets. However, it is difficult to understand how they compare. We aim to benchmark the relative performance of machine learning (ML) merger detection methods. We explore six leading ML methods using three main datasets. The first one (the training data) consists of mock observations from the IllustrisTNG simulations and allows us to quantify the performance metrics of the detection methods. The second one consists of mock observations from the Horizon-AGN simulations, introduced to evaluate the performance of classifiers trained on different, but comparable data. The third one consists of real observations from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) survey. For the binary classification task (mergers vs. non-mergers), all methods perform reasonably well in the domain of the training data. At $0.1<z<0.3$, precision and recall range between $\sim$70\% and 80\%, both of which decrease with increasing $z$ as expected (by $\sim$5\% for precision and $\sim$10\% for recall at $0.76<z<1.0$). When transferred to a different domain, the precision of all classifiers is only slightly reduced, but the recall is significantly worse (by $\sim$20-40\% depending on the method). Zoobot offers the best overall performance in terms of precision and F1 score. When applied to real HSC observations, all methods agree well with visual labels of clear mergers but can differ by more than an order of magnitude in predicting the overall fraction of major mergers. For the multi-class classification task to distinguish pre-, post- and non-mergers, none of the methods offer a good performance, which could be partly due to limitations in resolution and depth of the data. With the advent of better quality data (e.g. JWST and Euclid), it is important to improve our ability to detect mergers and distinguish between merger stages.

astro-ph.GA↗

arXiv2022

Preparing for low surface brightness science with the Vera C. Rubin Observatory: characterisation of tidal features from mock images

Tidal features in the outskirts of galaxies yield unique information about their past interactions and are a key prediction of the hierarchical structure formation paradigm. The Vera C. Rubin Observatory is poised to deliver deep observations for potentially of millions of objects with visible tidal features, but the inference of galaxy interaction histories from such features is not straightforward. Utilising automated techniques and human visual classification in conjunction with realistic mock images produced using the NEWHORIZON cosmological simulation, we investigate the nature, frequency and visibility of tidal features and debris across a range of environments and stellar masses. In our simulated sample, around 80 per cent of the flux in the tidal features around Milky Way or greater mass galaxies is detected at the 10-year depth of the Legacy Survey of Space and Time (30-31 mag / sq. arcsec), falling to 60 per cent assuming a shallower final depth of 29.5 mag / sq. arcsec. The fraction of total flux found in tidal features increases towards higher masses, rising to 10 per cent for the most massive objects in our sample (M*~10^{11.5} Msun). When observed at sufficient depth, such objects frequently exhibit many distinct tidal features with complex shapes. The interpretation and characterisation of such features varies significantly with image depth and object orientation, introducing significant biases in their classification. Assuming the data reduction pipeline is properly optimised, we expect the Rubin Observatory to be capable of recovering much of the flux found in the outskirts of Milky Way mass galaxies, even at intermediate redshifts (z<0.2).

astro-ph.GA↗