arXiv subjects

Lucy F. Fortson

Publications and source records attributed to Lucy F. Fortson.

9 recordsLinked to original sources

arXiv2026

Physical properties of star-forming clumps in nearby galaxies from CLAUDS and HSC-SSP

Giant Star-forming Clumps (GSFCs) are kpc-scale regions of enhanced star-formation with stellar masses of $10^6$ to $10^{10}\,M_\odot$ that are commonly observed in high-redshift ($z \gtrsim 1$) galaxies but their formation and role in galaxy evolution remain unclear. We applied a Faster R-CNN object detection framework (FRCNN) to identify star-forming clumps in astrophysical imaging data from a mass-complete sample of $\sim$210,000 low-redshift galaxies ($z\lesssim0.32$), located in the HSC-SSP Wide and Deep fields observed by the Hyper Suprime-Cam Subaru Strategic Survey (HSC-SSP) and CFHT Large Area U-band Deep Survey (CLAUDS). We present a population of $\sim$710,000 detected clumps for which we measured the (u)grizy-photometry and derived the physical properties (stellar mass, age, dust extinction and star-formation rate) for each clump by fitting the photometric data to stellar population models. We estimate that the clump Stellar Mass Function (cSMF) for young clumps ($<100\,\mathrm{Myr}$) follows a power law with an exponent of $α=1.98\pm0.02$ down to a completeness limit of $\sim10^{8}\,M_\odot$ which is consistent with an in situ formation through violent disk instabilities (VDI). The clumps show elevated star-formation rates and negative gradients in their ages and stellar masses as a function of galactocentric distance. These gradients are steeper than those measured for the intra-clump regions of the host galaxies and are robust against uncertainties introduced by different modelling assumptions. For nearby galaxies we argue that the observed low-redshift clumps are likely analogues of high-redshift GSFCs and that the inward migration of star-forming clumps directly contributes to the central bulge formation of the host galaxy.

astro-ph.GA↗

arXiv2026

The fraction of clumpy star-forming galaxies in nearby galaxies from CLAUDS and HSC-SSP

Massive, star-forming clumps are regions of intensive star-formation that are commonly observed in high-redshift ($z > 1$) galaxies. Observations of low-redshift clumpy galaxy analogues are rare but the availability of wide-field galaxy survey data makes the detection of large clumpy galaxy samples much more feasible. We present a population of 12,790 star-forming clumps detected in a mass-complete sample of 5,395 star-forming galaxies (SFGs) at redshifts $z\leq0.32$, located in the XMM-LSS, E-COSMOS and DEEP2-3 fields observed by the Hyper Suprime-Cam Subaru Strategic Survey (HSC-SSP) and CFHT Large Area U-band Deep Survey (CLAUDS). The clumps were detected using an improved version of our Deep Learning (DL)-based object detection framework which uses the ZOOBOT foundation DL-model as a 'backbone' feature extractor. We determined the fraction of star-forming galaxies hosting at least one off-centre clump ($f_{\mathrm{clumpy}}$) based on a clump definition that requires a clump-galaxy flux ratio in the CLAUDS u-band of $\geq8\%$. We estimate $f_{\mathrm{clumpy}}$ to decrease from $\sim$31\% at $z\sim0.3$ to $\sim$23\% at $z \sim 0.1$, which aligns well with a low-redshift extrapolation of the clumpy fraction that is measured using high-redshift observations. At fixed redshift, $f_{\mathrm{clumpy}}$ is negatively correlated with the stellar mass and positively correlated with the specific star-formation rate (sSFR) of the host galaxies. When the clump definition is changed to include only clumps with a stellar mass of $M_{\mathrm{cl}} \geq 10^7 M_\odot$, we observe a highly increased clumpy fraction of $\sim$60\% that tends to increase with the stellar mass of the host galaxies but does not show a dependence on the sSFR of the host galaxies.

astro-ph.GA↗

arXiv2026

Star-forming clump detection in nearby galaxies using Faster R-CNN and $ugrizy$ imaging data from CLAUDS and HSC-SSP

Giant Star-forming Clumps (GSFCs) are kpc-scale regions of enhanced star-formation with stellar masses of $10^7$ to $10^9\,M_\odot$ that are commonly observed in high-redshift galaxies but are rarely detected in low-redshift ($z\lesssim0.5$) galaxy analogues. However, the availability of wide-field galaxy survey data makes it possible to identify potential star-forming clumps in large samples of low-redshift galaxies using object detection models that are based on Deep Learning (DL) techniques. We apply a novel DL-based object detection model to galaxies observed by the Hyper Suprime-Cam Subaru Strategic Survey (HSC-SSP) and CFHT Large Area U-band Deep Survey (CLAUDS). Our model is based on the the Faster Region-Based Convolutional Neural Network (Faster R-CNN or FRCNN) object detection framework but expanded to process the six $ugrizy$ filter band images simultaneously and identify not only clumps and their locations in the host galaxy but also additional contaminants. By adopting the \textsc{Zoobot} foundation DL-model as a feature extraction backbone, we also demonstrate one of the first applications of \textsc{Zoobot} in a downstream task for object detection. Our model achieves a detection completeness of $\gtrsim 0.9$ and purity of $\gtrsim 0.8$ which were validated on a large set of real galaxies into which simulated clumps were injected.

astro-ph.IM↗

arXiv2026

The complex relationships between AGN, bars and bulges

Context. Via scaling relations, it is well-known that active galactic nuclei (AGN) and bulges are linked. This link was thought to be driven by mergers, but recent studies show that secular processes are the dominant mechanism of supermassive black hole growth. One such secular mechanism is gas inflow driven by large-scale bars. Since bulges can also grow via these bars, there is likely some common process between these three features. Aims. We investigate whether the observed correlation between AGN and bars is real or arises as a result of correlations between bars and bulges. Methods. Using a catalogue of AGN identifications and galaxy morphologies in the DESI Legacy Survey at $z\leq0.1$, we control for mass and colour and investigate the AGN fraction variation with bulge prominence and bar strength. Results. We first show that the variation in AGN fraction between strongly barred, weakly barred and unbarred galaxies does not qualitatively change if we additionally control for bulge prominence. Second, we find that in fixed bins of bulge prominence, the AGN fraction increases with increasing bar strength. In subsamples split by bar strength, the AGN fraction increases with bulge prominence, indicating that AGN presence correlates with both bar strength and bulge prominence simultaneously.

astro-ph.GA↗

arXiv2018

Integrating human and machine intelligence in galaxy morphology classification tasks

Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme we increase the classification rate nearly 5-fold, classifying 226,124 galaxies in 92 days of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7% accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of nonparametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine, and demonstrate that the combined system provides at least a factor of 8 increase in the classification rate, classifying 210,803 galaxies in just 32 days of GZ2 project time with 93.1% accuracy. As the Random Forest algorithm requires a minimal amount of computation cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large scale surveys.

astro-ph.IM↗

arXiv2016

Galaxy Zoo: Morphological Classifications for 120,000 Galaxies in HST Legacy Imaging

We present the data release paper for the Galaxy Zoo: Hubble (GZH) project. This is the third phase in a large effort to measure reliable, detailed morphologies of galaxies by using crowdsourced visual classifications of colour composite images. Images in GZH were selected from various publicly-released Hubble Space Telescope Legacy programs conducted with the Advanced Camera for Surveys, with filters that probe the rest-frame optical emission from galaxies out to $z \sim 1$. The bulk of the sample is selected to have $m_{I814W} < 23.5$,but goes as faint as $m_{I814W} < 26.8$ for deep images combined over 5 epochs. The median redshift of the combined samples is $z = 0.9 \pm 0.6$, with a tail extending out to $z \sim 4$. The GZH morphological data include measurements of both bulge- and disk-dominated galaxies, details on spiral disk structure that relate to the Hubble type, bar identification, and numerous measurements of clump identification and geometry. This paper also describes a new method for calibrating morphologies for galaxies of different luminosities and at different redshifts by using artificially-redshifted galaxy images as a baseline. The GZH catalogue contains both raw and calibrated morphological vote fractions for 119,849 galaxies, providing the largest dataset to date suitable for large-scale studies of galaxy evolution out to $z \sim 1$.

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arXiv2015

Galaxy Zoo: the effect of bar-driven fueling on the presence of an active galactic nucleus in disc galaxies

We study the influence of the presence of a strong bar in disc galaxies which host an active galactic nucleus (AGN). Using data from the Sloan Digital Sky Survey and morphological classifications from the Galaxy Zoo 2 project, we create a volume-limited sample of 19,756 disc galaxies at $0.01<z<0.05$ which have been visually examined for the presence of a bar. Within this sample, AGN host galaxies have a higher overall percentage of bars (51.8%) than inactive galaxies exhibiting central star formation (37.1%). This difference is primarily due to known effects; that the presence of both AGN and galactic bars is strongly correlated with both the stellar mass and integrated colour of the host galaxy. We control for this effect by examining the difference in AGN fraction between barred and unbarred galaxies in fixed bins of mass and colour. Once this effect is accounted for, there remains a small but statistically significant increase that represents 16% of the average barred AGN fraction. Using the $L_{\rm{[O III]}}/M_{BH} $ratio as a measure of AGN strength, we show that barred AGN do not exhibit stronger accretion than unbarred AGN at a fixed mass and colour. The data are consistent with a model in which bar-driven fueling does contribute to the probability of an actively growing black hole, but in which other dynamical mechanisms must contribute to the direct AGN fueling via smaller, non-axisymmetric perturbations.

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arXiv2014

Galaxy Zoo: Are Bars Responsible for the Feeding of Active Galactic Nuclei at 0.2 < z < 1.0?

We present a new study investigating whether active galactic nuclei (AGN) beyond the local universe are preferentially fed via large-scale bars. Our investigation combines data from Chandra and Galaxy Zoo: Hubble (GZH) in the AEGIS, COSMOS, and GOODS-S surveys to create samples of face-on, disc galaxies at 0.2 < z < 1.0. We use a novel method to robustly compare a sample of 120 AGN host galaxies, defined to have 10^42 erg/s < L_X < 10^44 erg/s, with inactive control galaxies matched in stellar mass, rest-frame colour, size, Sersic index, and redshift. Using the GZH bar classifications of each sample, we demonstrate that AGN hosts show no statistically significant enhancement in bar fraction or average bar likelihood compared to closely-matched inactive galaxies. In detail, we find that the AGN bar fraction cannot be enhanced above the control bar fraction by more than a factor of two, at 99.7% confidence. We similarly find no significant difference in the AGN fraction among barred and non-barred galaxies. Thus we find no compelling evidence that large-scale bars directly fuel AGN at 0.2 1, our findings suggest that large-scale bars have likely never directly been a dominant fueling mechanism for supermassive black hole growth.

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arXiv2013

Galaxy Zoo 2: detailed morphological classifications for 304,122 galaxies from the Sloan Digital Sky Survey

We present the data release for Galaxy Zoo 2 (GZ2), a citizen science project with more than 16 million morphological classifications of 304,122 galaxies drawn from the Sloan Digital Sky Survey. Morphology is a powerful probe for quantifying a galaxy's dynamical history; however, automatic classifications of morphology (either by computer analysis of images or by using other physical parameters as proxies) still have drawbacks when compared to visual inspection. The large number of images available in current surveys makes visual inspection of each galaxy impractical for individual astronomers. GZ2 uses classifications from volunteer citizen scientists to measure morphologies for all galaxies in the DR7 Legacy survey with m_r>17, in addition to deeper images from SDSS Stripe 82. While the original Galaxy Zoo project identified galaxies as early-types, late-types, or mergers, GZ2 measures finer morphological features. These include bars, bulges, and the shapes of edge-on disks, as well as quantifying the relative strengths of galactic bulges and spiral arms. This paper presents the full public data release for the project, including measures of accuracy and bias. The majority (>90%) of GZ2 classifications agree with those made by professional astronomers, especially for morphological T-types, strong bars, and arm curvature. Both the raw and reduced data products can be obtained in electronic format at http://data.galaxyzoo.org .

astro-ph.CO↗