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    Connecting the gas in H ii regions to the underlying source of the ionizing radiation can help us constrain the physical processes of stellar feedback and how H ii regions evolve over time. With PHANGS–MUSE, we detect nearly 24 000 H ii regions across 19 galaxies and measure the physical properties of the ionized gas (e.g. metallicity, ionization parameter, and density). We use catalogues of multiscale stellar associations from PHANGS–HST to obtain constraints on the age of the ionizing sources. We construct a matched catalogue of 4177 H ii regions that are clearly linked to a single ionizing association. A weak anticorrelation is observed between the association ages and the $\mathrm{H}\, \alpha$ equivalent width $\mathrm{EW}(\mathrm{H}\, \alpha)$, the $\mathrm{H}\, \alpha/\mathrm{FUV}$ flux ratio, and the ionization parameter, log q. As all three are expected to decrease as the stellar population ages, this could indicate that we observe an evolutionary sequence. This interpretation is further supported by correlations between all three properties. Interpreting these as evolutionary tracers, we find younger nebulae to be more attenuated by dust and closer to giant molecular clouds, in line with recent models of feedback-regulated star formation. We also observe strong correlations with the local metallicity variations and all three proposed age tracers, suggestive of star formation preferentially occurring in locations of locally enhanced metallicity. Overall, $\mathrm{EW}(\mathrm{H}\, \alpha)$ and log q show the most consistent trends and appear to be most reliable tracers for the age of an H ii region.

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  2. Abstract

    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 (Rgal∼ 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;ScousePydecomposition reveals multiple components with line widths of 〈σCO,scouse〉 ≈ 19 km s−1and surface densities ofΣH2,scouse800Mpc2, 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.

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  3. null (Ed.)
    ABSTRACT 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. 
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  4. ABSTRACT We present the results of a proof-of-concept experiment that demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in Hubble Space Telescope(HST) ultraviolet-optical imaging of nearby spiral galaxies ($D\lesssim 20\, \textrm{Mpc}$) in the Physics at High Angular Resolution in Nearby GalaxieS (PHANGS)–HST survey. Given the relatively small nature of existing, human-labelled star cluster samples, 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 perform a series of experiments to determine the dependence of classification performance on neural network architecture (ResNet18 and VGG19-BN), training data sets curated by either a single expert or three astronomers, and the size of the images used for training. We find that the overall classification accuracies are not significantly affected by these choices. The networks are used to classify star cluster candidates in the PHANGS–HST galaxy NGC 1559, which was not included in the training samples. The resulting prediction accuracies are 70 per cent, 40 per cent, 40–50 per cent, and 50–70 per cent for class 1, 2, 3 star clusters, and class 4 non-clusters, respectively. This performance is competitive with consistency achieved in previously published human and automated quantitative classification of star cluster candidate samples (70–80 per cent, 40–50 per cent, 40–50 per cent, and 60–70 per cent). The methods introduced herein lay the foundations to automate classification for star clusters at scale, and exhibit the need to prepare a standardized data set of human-labelled star cluster classifications, agreed upon by a full range of experts in the field, to further improve the performance of the networks introduced in this study. 
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