Deep learning is rapidly becoming a ubiquitous signal-processing tool in big-data experiments. Here, we present the results of a proof-of-concept experiment which demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in HST UV-optical imaging of nearby spiral galaxies (D 20 Mpc) in the PHANGS-HST survey. Given the relatively small and unbalanced nature of existing, human-labelled star cluster datasets, we transfer the knowledge of state-of-the-art neural network models for real-object recognition to classify star clusters candidates into four morphological classes. We show that human classification is at the 66% : 37% : 40% : 61% agreement level for the four classes considered. On the other hand, our findings indicate that deep learning algorithms achieve 76% : 63% : 59% : 70% for a star cluster sample within 4Mpc ≤ D ≤ 10Mpc. We further tested the robustness of our deep learning algorithms to generalize to different cluster images. For this experiment we used the first data obtained by PHANGS-HST of NGC1559, which is more distant at D = 19Mpc, and found that deep learning produces classification accuracies 73% : 42% : 52% : 67%. We furnish evidence for the robustness of these analyses by using two different state-of-the-art neural network models for image classification, which were trained multiple times from the ground up to assess the variance and stability of our results. Through ablation studies, we quantified the importance of the NUV, U, B, V and I images for morphological classification with our deep learning models, and find that, as expected, the V-band is the key contributor as human classifications are based on images taken in that filter. The methods introduced in this article lay the foundations to automate classification for these objects at scale, and motivate the creation of a standardized star cluster classification dataset, developed and agreed upon by a range of experts in the field.

The morphology of HII regions around young star clusters provides insight into the timescales and physical processes that clear a cluster's natal gas. We study ∼700 young clusters (≤10Myr) in three nearby spiral galaxies (NGC 7793, NGC 4395, and NGC 1313) using Hubble Space Telescope (HST ) imaging from LEGUS (Legacy Extra-Galactic Ultraviolet Survey). Clusters are classified by their Hα morphology (concentrated, partially exposed, no-emission) and whether they have neighboring clusters (which could affect the clearing timescales). Through visual inspection of the HST images, and analysis of ages, reddenings, and stellar masses from spectral energy distributions fitting, together with the (U-B), (V-I) colors, we find: 1) the median ages indicate a progression from concentrated (∼3 Myr), to partially exposed (∼4 Myr), to no Hα emission (>5Myr), consistent with the expected temporal evolution of HII regions and previous results. However, 2) similarities in the age distributions for clusters with concentrated and partially exposed Hα morphologies imply a short timescale for gas clearing ( 1Myr). Also, 3) our cluster sample's median mass is ∼1000 M , and a significant fraction (∼20%) contain one or more bright red sources (presumably supergiants), which can mimic reddening effects. Finally, 4) the median E(B-V) values for clusters with concentrated Hα and those without Hα emission appear to be more similar than expected (∼0.18 vs. ∼0.14, respectively), but when accounting for stochastic effects, clusters without Hα emission are less reddened. To mitigate stochastic effects, we experiment with synthesizing more massive clusters by stacking fluxes of clusters within each Hα morphological class. Composite isolated clusters also reveal a color and age progression for Hα morphological classes, consistent with analysis of the individual clusters.

We present an innovative and widely applicable approach for the detection and classification of stellar clusters, developed for the PHANGS-HST Treasury Program, an NUV-to-I band imaging campaign of 38 spiral galaxies. Our pipeline first generates a unified master source list for stars and candidate clusters, to enable a self-consistent inventory of all star formation products. To distinguish cluster candidates from stars, we introduce the Multiple Concentration Index (MCI) parameter, and measure inner and outer MCIs to probe morphology in more detail than with a single, standard concentration index (CI). We improve upon cluster candidate selection, jointly basing our criteria on expectations for MCI derived from synthetic cluster populations and existing cluster catalogues, yielding model and semi-empirical selection regions (respectively). Selection purity (confirmed clusters versus candidates, assessed via human-based classification) is high (up to 70 per cent) for moderately luminous sources in the semi-empirical selection region, and somewhat lower overall (outside the region or fainter). The number of candidates rises steeply with decreasing luminosity, but pipeline-integrated Machine Learning (ML) classification prevents this from being problematic. We quantify the performance of our PHANGS-HST methods in comparison to LEGUS for a sample of four galaxies in common to both surveys, finding overall agreement with 50–75 per cent of human verified star clusters appearing in both catalogues, but also subtle differences attributable to specific choices adopted by each project. The PHANGS-HST ML-classified Class 1 or 2 catalogues reach ∼1 mag fainter, ∼2 × lower stellar mass, and are 2−5 × larger in number, than attained in the human classified samples.

When completed, the PHANGS–HST project will provide a census of roughly 50 000 compact star clusters and associations, as well as human morphological classifications for roughly 20 000 of those objects. These large numbers motivated the development of a more objective and repeatable method to help perform source classifications. In this paper, we consider the results for five PHANGS–HST galaxies (NGC 628, NGC 1433, NGC 1566, NGC 3351, NGC 3627) using classifications from two convolutional neural network architectures (RESNET and VGG) trained using deep transfer learning techniques. The results are compared to classifications performed by humans. The primary result is that the neural network classifications are comparable in quality to the human classifications with typical agreement around 70 to 80 per cent for Class 1 clusters (symmetric, centrally concentrated) and 40 to 70 per cent for Class 2 clusters (asymmetric, centrally concentrated). If Class 1 and 2 are considered together the agreement is 82 ± 3 per cent. Dependencies on magnitudes, crowding, and background surface brightness are examined. A detailed description of the criteria and methodology used for the human classifications is included along with an examination of systematic differences between PHANGS–HST and LEGUS. The distribution of data points in a colour–colour diagram is used as a ‘figure of merit’ to further test the relative performances of the different methods. The effects on science results (e.g. determinations of mass and age functions) of using different cluster classification methods are examined and found to be minimal.

A primary new capability of JWST is the ability to penetrate the dust in star-forming galaxies to identify and study the properties of young star clusters that remain embedded in dust and gas. In this Letter we combine new infrared images taken with JWST with our optical Hubble Space Telescope (HST) images of the starbursting barred (Seyfert2) spiral galaxy NGC 1365. We find that this galaxy has the richest population of massive young clusters of any known galaxy within 30 Mpc, with ∼30 star clusters that are more massive than 106 M ⊙ and younger than 10 Myr. Sixteen of these clusters are newly discovered from our JWST observations. An examination of the optical images reveals that 4 of 30 (∼13%) are so deeply embedded that they cannot be seen in the Hubble I band (A V ≳ 10 mag), and that 11 of 30 (∼37%) are missing in the HST B band, so age and mass estimates from optical measurements alone are challenging. These numbers suggest that massive clusters in NGC 1365 remain completely obscured in the visible for ∼1.3 ± 0.7 Myr and are either completely or partially obscured for ∼3.7 ± 1.1 Myr. We also use the JWST observations to gain new insights into the triggering of star cluster formation by the collision of gas and dust streamers with gas and dust in the bar. The JWST images reveal previously unknown structures (e.g., bridges and overshoot regions from stars that form in the bar) that help us better understand the orbital dynamics of barred galaxies and associated star-forming rings. Finally, we note that the excellent spatial resolution of the NIRCAM F200W filter provides a better way to separate barely resolved compact clusters from individual stars based on their sizes.

The analysis of star cluster ages in tandem with the morphology of their HII regions can provide insight into the processes that clear a cluster’s natal gas, as well as the accuracy of cluster ages and dust reddening derived from Spectral Energy Distribution (SED) fitting. We classify 3757 star clusters in 16 nearby galaxies according to their Hα morphology (concentrated, partially exposed, no emission), using Hubble Space Telescope (HST) imaging from the Legacy ExtraGalactic Ultraviolet Survey (LEGUS). We find: 1) The mean SED ages of clusters with concentrated (1-2 Myr) and partially exposed HII region morphologies (2-3 Myr) indicate a relatively early onset of gas clearing and a short (1-2 Myr) clearing timescale. 2) The reddening of clusters can be overestimated due to the presence of red supergiants, which is a result of stochastic sampling of the IMF in low mass clusters. 3) The age-reddening degeneracy impacts the results of the SED fitting – out of 1408 clusters with M* ≥ 5000 M⊙, we find that at least 46 (3 per cent) have SED ages which appear significantly underestimated or overestimated based on Hα and their environment, while the total percentage of poor age estimates is expected to be several times larger. 4) Lastly, we examine the dependence of the morphological classifications on spatial resolution. At HST resolution, our conclusions are robust to the distance range spanned by the sample (3-10 Mpc). However, analysis of ground-based Hα images shows that compact and partially exposed morphologies frequently cannot be distinguished from each other.

Large-scale bars can fuel galaxy centers with molecular gas, often leading to the development of dense ringlike structures where intense star formation occurs, forming a very different environment compared to galactic disks. We pair ∼0.″3 (30 pc) resolution new JWST/MIRI imaging with archival ALMA CO(2–1) mapping of the central ∼5 kpc of the nearby barred spiral galaxy NGC 1365 to investigate the physical mechanisms responsible for this extreme star formation. The molecular gas morphology is resolved into two well-known bright bar lanes that surround a smooth dynamically cold gas disk (R gal ∼ 475 pc) reminiscent of non-star-forming disks in early-type galaxies and likely fed by gas inflow triggered by stellar feedback in the lanes. The lanes host a large number of JWST-identified massive young star clusters. We find some evidence for temporal star formation evolution along the ring. The complex kinematics in the gas lanes reveal strong streaming motions and may be consistent with convergence of gas streamlines expected there. Indeed, the extreme line widths are found to be the result of inter-“cloud” motion between gas peaks; ScousePy decomposition reveals multiple components with line widths of 〈σ CO,scouse〉 ≈ 19 km s−1 and surface densities of 〈 Σ H 2 , scouse 〉 ≈ 800 M ⊙ pc − 2 , similar to the properties observed throughout the rest of the central molecular gas structure. Tailored hydrodynamical simulations exhibit many of the observed properties and imply that the observed structures are transient and highly time-variable. From our study of NGC 1365, we conclude that it is predominantly the high gas inflow triggered by the bar that is setting the star formation in its CMZ.

A long-standing problem when deriving the physical properties of stellar populations is the degeneracy between age, reddening, and metallicity. When a single metallicity is used for all star clusters in a galaxy, this degeneracy can result in ”catastrophic” errors for old globular clusters. Typically, approximately 10 – 20 % of all clusters detected in spiral galaxies can have ages that are incorrect by a factor of ten or more. In this paper we present a pilot study for four galaxies (NGC 628, NGC 1433, NGC 1365, and NGC 3351) from the PHANGS-HST survey. We describe methods to correct the age-dating for old globular clusters, by first identifying candidates using their colors, and then reassigning ages and reddening based on a lower metallicity solution. We find that young ‘Interlopers’ can be identified from their Hα flux. CO (2-1) intensity or the presence of dust can also be used, but our tests show that they do not work as well. Improvements in the success fraction are possible at the ≈15 % level (reducing the fraction of catastrophic age-estimates from between 13 - 21 % to 3 - 8 %). A large fraction of the incorrectly age-dated globular clusters are systematically given ages around 100 Myr, polluting the younger populations as well. Incorrectly age-dated globular clusters significantly impact the observed cluster age distribution in NGC 628, which affects the physical interpretation of cluster disruption in this galaxy. For NGC 1365, we also demonstrate how to fix a second major age-dating problem, where very dusty young clusters with E(B − V) > 1.5 mag are assigned old, globular-cluster like ages. Finally, we note the discovery of a dense population of ≈300 Myr clusters around the central region of NGC 1365. and discuss how this results naturally from the dynamics in a barred galaxy.