arXiv subjects

Thomas Williams

Publications and source records attributed to Thomas Williams.

At least 19 recordsLinked to original sources

arXiv2026

A steep mass transition for bar-driven ISM structuring revealed by PHANGS-JWST

Galactic bars play a critical role in the secular evolution of their hosts by reorganising the ISM. We use a sample of 57 star-forming disc galaxies observed with JWST at 3 and 7.7 $\mu$m to probe how the spatial distribution of PAH emission, as a structural marker of the cold ISM, depends on stellar mass and bar presence. We find evidence for a "watershed" at a stellar mass of $10^{10}$ Msun, marking a fundamental transition in the bar-driven distribution of PAH emission. This confirms trends previously predicted by numerical simulations and observed via ionised gas or UV light. While lower-mass galaxies exhibit a disordered and clumpy distribution of PAH emission regardless of bar presence, higher-mass barred hosts display well-structured dynamical features traced by PAH emission with significant gas reservoirs (e.g., discs and rings) within the central 15% of the bar radius (Rb). Furthermore, we observe a systematic depletion of PAH emission within the [0.2-0.8] Rb range in barred systems with stellar masses above $10^{10}$ Msun. Such central discs, rings, and associated radial dips ("bar deserts") appear to be a mass-dependent phenomenon: ubiquitous in massive galaxies but mostly absent in lower-mass counterparts. In contrast to the structured features in massive hosts, the disorganised ISM in lower-mass galaxies masks commonly observed bar-driven signatures. This suggests that tracer selection and dust obscuration may significantly bias observed bar fractions. Our study underlines two regimes of secular evolution, with different impacts and observability of bar-driven processes: it reaffirms bars as primary drivers of rapid secular evolution in galaxies above $10^{10}$ Msun$, while their impact is significantly reduced or delayed below this threshold. It further underscores the need to account for these processes when modelling galaxy evolution in cosmological simulations.

astro-ph.GA↗

arXiv2026

Multi-Thermal CME Detection with ALMANAC

Reliable identification of low-coronal CME origins remains a key limitation in space weather forecasting with coronagraphs not directly resolving low-coronal signatures. We present a re-engineered multi-thermal implementation of the ALMANAC algorithm, designed to detect eruptive signatures in EUV observations. The framework extends the method to a multi-wavelength system, improving robustness against projection effects, instrumental artifacts, and wavelength-dependent ambiguities via complementary temperature responses. A spatiotemporal clustering scheme merges detections across channels, reducing event bifurcation and improving coherence while maintaining NRT performance through parallel computing. Benchmarking against 20 halo CMEs from CDAW shows improved interpretability and operational usability, with clearer separation of eruptions and more consistent onset localisation relative to coronagraph estimates. The main benefits arise from improved event discrimination, reduced fragmentation, and more interpretable source region identification. ALMANAC shows sensitivity to precursor low-coronal activity not always captured in white-light catalogues, highlighting its advantages for early warning detection. When coupled with the ARTop framework, it enables co-analysis of coronal intensity variability and photospheric magnetic evolution. In this context, kurtosis time-series from multi-wavelength EUV data exhibit recurrent pre-eruptive spikes that frequently align with enhancements in magnetic winding and helicity injection. Across multiple regions, these signatures often precede solar activity, including potential discrimination of X-class flares, while remaining suppressed during magnetically quiet intervals. Overall, integrating coronal diagnostics with photospheric topology offers a pathway toward improved eruption forecasting and space weather prediction.

astro-ph.SR↗

arXiv2026

An agent-based model of outer membrane biogenesis in Gram-negative bacteria

The outer membrane is the interface through which Gram-negative bacteria - a broad classification of organisms including \textit{Escherichia coli} and a number of deadly pathogens - interact with the environment. Two decades of work on the process of outer membrane biogenesis have led to the discovery of the components that mediate this process, and the characterisation of structure and function of these component parts of the bacterial cell machinery. However, neither current experimental methods, nor conventional molecular dynamics (MD) simulation approaches are capable of investigating this membrane machinery on the time scale of the cell division cycle. This leaves crucial questions unanswered, such as how this lipid-poor, largely static environment is organised to permit ongoing membrane growth. Here, we introduce a semi-quantitative agent-based model to explore the molecular-scale dynamics of Gram-negative outer membrane as it grows. Model simulations across a broad region of parameter space suggest that protein incorporation into the membrane by the $\beta$-barrel assembly machinery (BAM complex) is a process which is prone to stalling, and may take place only in short bursts. We also find suggestions that BAM complexes work collaboratively with each other, and with the lipopolysaccharide-inserting Lpt complex when in close proximity. The agent-based framework we introduce provides a means to assess and generate hypotheses on outer membrane biogenesis on previously inaccessible time scales.

q-bio.SC↗

arXiv2026

Amortized Phylodynamic Inference with Neural Bayes Estimators and Recursive Neural Networks

Phylodynamics is used to estimate epidemic dynamics from phylogenetic trees or genomic sequences of pathogens, but the likelihood calculations needed can be challenging for complex models. We present a neural Bayes estimator (NBE) for key epidemic quantities: the reproduction number, prevalence, and cumulative infections through time. By performing quantile regression over tree space, the NBE allows us to estimate posterior medians and credible intervals directly from a reconstructed tree. Our approach uses a recursive neural network as a tree embedding network with a prediction network conditioned on time and quantile level to generate the estimates. In simulation studies, the NBE achieves good predictive performance, with conservative uncertainty estimates. Compared with a BEAST2 fixed-tree analysis, the NBE gives less biased estimates of time-varying reproduction numbers in our test setting. Under a misspecified sampling model, the NBE performance degrades (as expected) but remains reasonable, and fine-tuning a pre-trained model yields estimates comparable to those from a model trained from scratch, at substantially lower computational cost.

stat.ME↗

arXiv2026

Stellar-mass black holes on the millimetre fundamental plane of black hole accretion

Recent work revealed the existence of a galaxy "millimetre fundamental plane of black hole accretion", a tight correlation between nuclear $1$mm luminosity, intrinsic $2$ - $10$keV X-ray luminosity and supermassive black hole mass, originally discovered for nearby low- and high-luminosity active galactic nuclei. Here we use mm and X-ray data of $5$ X-ray binaries (XRBs) to demonstrate that these stellar-mass black holes also lie on the mm fundamental plane, as they do at radio wavelengths. One source for which we have multi-epoch observations shows evidence of deviations from the plane after a state change, suggesting that the plane only applies to XRBs in the hard state, as is true again at radio wavelengths. We show that both advection-dominated accretion flows and compact jet models predict the existence of the plane across the entire range of black hole masses, although these models vary in their ability to accurately predict the XRB black hole masses.

astro-ph.GA↗

arXiv2025

Investigating the Efficacy of Topologically Derived Time Series for Flare Forecasting. II. XGBoost Model

Solar flares are a primary driver of space weather, and forecasting their occurrence remains a significant challenge. This paper presents a novel flare prediction model based on topologically derived photospheric magnetic parameters. We employ the \texttt{ARTop} framework to compute the time-dependent input rates of magnetic winding and helicity across more than $10^5$ active region (AR) observations, decomposing them into current-carrying and potential components to reduce sensitivity to optical flow methods. An \texttt{XGBoost} machine learning model is trained on these topological time series, alongside engineered features including rolling statistics, kurtosis, and flare history, to predict the probability of $\geq$M1.0-class flares within the next 24 hours. The model demonstrates strong performance on a validation set, with a True Skill Statistic (TSS) of 0.804 for once daily operational region forecasts. When applied to a fully independent holdout set, the operational forecast achieves a TSS of \tsssa. A SHapley Additive exPlanations (SHAP) analysis confirms the model's physical interpretability, identifying flare history and accumulated current-carrying winding and helicity as the most important features. The main challenges identified are false positives arising from ARs with frequent C-class flaring and systematic errors introduced by projection effects when ARs are near the limb. Excluding limb-affected data yields no improvement in the holdout set TSS (\TSSalert\ versus \tsssa), due to the overall decreased number of flares. However, our per-region analysis indicates that mitigating these projection effects is crucial for future operational deployment. This work establishes magnetic topology, particularly its current-carrying components, as a highly effective and physically meaningful set of predictors for solar flare forecasting.

astro-ph.SR↗

arXiv2025

Spatially-Structured Models of Viral Dynamics: A Scoping Review

There is growing recognition in both the experimental and modelling literature of the importance of spatial structure to the dynamics of viral infections in tissues. Aided by the evolution of computing power and motivated by recent biological insights, there has been an explosion of new, spatially-explicit models for within-host viral dynamics in recent years. This development has only been accelerated in the wake of the COVID-19 pandemic. Spatially-structured models offer improved biological realism and can account for dynamics which cannot be well-described by conventional, mean-field approaches. However, despite their growing popularity, spatially-structured models of viral dynamics are underused in biological applications. One major obstacle to the wider application of such models is the huge variety in approaches taken, with little consensus as to which features should be included and how they should be implemented for a given biological context. Previous reviews of the field have focused on specific modelling frameworks or on models for particular viral species. Here, we instead apply a scoping review approach to the literature of spatially-structured viral dynamics models as a whole to provide an exhaustive update of the state of the field. Our analysis is structured along two axes, methodology and viral species, in order to examine the breadth of techniques used and the requirements of different biological applications. We then discuss the contributions of mathematical and computational modelling to our understanding of key spatially-structured aspects of viral dynamics, and suggest key themes for future model development to improve robustness and biological utility.

q-bio.QM↗

arXiv2025

Linking stellar populations to HII regions across nearby galaxies. II. Infrared Reprocessed and UV Direct Radiation Pressure in HII Regions

Radiation pressure is a key mechanism by which stellar feedback disrupts molecular clouds and drives HII region expansion. This includes direct radiation pressure exerted by UV photons on dust grains, pressure associated with photoionization, and infrared (IR) radiation pressure on grains due to dust-reprocessed IR photons. We present a new method that combines high resolution mid-IR luminosities from JWST-MIRI, optical attenuation and nebular line measurements from VLT-MUSE, and HST H$\alpha$-based region sizes to estimate the strength of radiation pressure in $\approx 18,000$ HII regions across 19 nearby star-forming galaxies. This is the most extensive and direct estimate of these terms beyond the Local Group to date. In the disks of galaxies, we find that the total reprocessed IR pressure is on average 5% of the direct UV radiation pressure. This fraction rises to 10% in galaxy centers. We expect reprocessed IR radiation pressure to dominate over UV radiation pressure in regions where $L_{\rm F2100W}/L_{\rm H\alpha}^{\rm corr} \gtrsim 75$. Radiation pressure due to H ionizations is lower than pressure on dust in our sample, but appears likely to dominate the radiation pressure budget in dwarf galaxies similar to the Small Magellanic Cloud. The contribution from all radiation pressure terms appears to be subdominant compared to thermal pressure from ionized gas, reinforcing the view that radiation pressure is most important in compact, heavily embedded, and young regions.

astro-ph.GA↗

arXiv2024

Investigating the Efficacy of Topologically Derived Time-Series for Flare Forecasting. I. Dataset Preparation

The accurate forecasting of solar flares is considered a key goal within the solar physics and space weather communities. There is significant potential for flare prediction to be improved by incorporating topological fluxes of magnetogram datasets, without the need to invoke three-dimensional magnetic field extrapolations. Topological quantities such as magnetic helicity and magnetic winding have shown significant potential towards this aim, and provide spatio-temporal information about the complexity of active region magnetic fields. This study develops time-series that are derived from the spatial fluxes of helicity and winding that show significant potential for solar flare prediction. It is demonstrated that time-series signals, which correlate with flare onset times, also exhibit clear spatial correlations with eruptive activity; establishing a potential causal relationship. A significant database of helicity and winding fluxes and associated time series across 144 active regions is generated using SHARP data processed with the ARTop code that forms the basis of the time-series and spatial investigations conducted here. We find that a number of time-series in this dataset often exhibit extremal signals that occur 1-8 hours before a flare. This, publicly available, living dataset will allow users to incorporate these data into their own flare prediction algorithms.

astro-ph.SR↗

arXiv2024

PHANGS-ML: the universal relation between PAH band and optical line ratios across nearby star-forming galaxies

The structure and chemistry of the dusty interstellar medium (ISM) are shaped by complex processes that depend on the local radiation field, gas composition, and dust grain properties. Of particular importance are Polycyclic Aromatic Hydrocarbons (PAHs), which emit strong vibrational bands in the mid-infrared, and play a key role in the ISM energy balance. We recently identified global correlations between PAH band and optical line ratios across three nearby galaxies, suggesting a connection between PAH heating and gas ionization throughout the ISM. In this work, we perform a census of the PAH heating -- gas ionization connection using $\sim$700,000 independent pixels that probe scales of 40--150 pc in nineteen nearby star-forming galaxies from the PHANGS survey. We find a universal relation between $\log$PAH(11.3 \mic/7.7 \mic) and $\log$([SII]/H$\alpha$) with a slope of $\sim$0.2 and a scatter of $\sim$0.025 dex. The only exception is a group of anomalous pixels that show unusually high (11.3 \mic/7.7 \mic) PAH ratios in regions with old stellar populations and high starlight-to-dust emission ratios. Their mid-infrared spectra resemble those of elliptical galaxies. AGN hosts show modestly steeper slopes, with a $\sim$10\% increase in PAH(11.3 \mic/7.7 \mic) in the diffuse gas on kpc scales. This universal relation implies an emerging simplicity in the complex ISM, with a sequence that is driven by a single varying property: the spectral shape of the interstellar radiation field. This suggests that other properties, such as gas-phase abundances, gas ionization parameter, and grain charge distribution, are relatively uniform in all but specific cases.

astro-ph.GA↗

arXiv2024

Accounting for the geometry of the respiratory tract in viral infections

Increasingly, experimentalists and modellers alike have come to recognise the important role of spatial structure in infection dynamics. Almost invariably, spatial computational models of viral infections - as with in vitro experimental systems - represent the tissue as wide and flat, which is often assumed to be representative of entire affected tissue within the host. However, this assumption fails to take into account the distinctive geometry of the respiratory tract in the context of viral infections. The respiratory tract is characterised by a tubular, branching structure, and moreover is spatially heterogeneous: deeper regions of the lung are composed of far narrower airways and are associated with more severe infection. Here, we extend a typical multicellular model of viral dynamics to account for two essential features of the geometry of the respiratory tract: the tubular structure of airways, and the branching process between airway generations. We show that, with this more realistic tissue geometry, the dynamics of infection are substantially changed compared to standard computational and experimental approaches, and that the resulting model is equipped to tackle important biological phenomena that do not arise in a flat host tissue, including viral lineage dynamics, and heterogeneity in immune responses to infection in different regions of the respiratory tree. Our findings suggest aspects of viral dynamics which current in vitro systems may be insufficient to describe, and point to several features of respiratory infections which can be experimentally assessed.

q-bio.QM↗

arXiv2024

Automating Code Adaptation for MLOps -- A Benchmarking Study on LLMs

This paper explores the possibilities of the current generation of Large Language Models for incorporating Machine Learning Operations (MLOps) functionalities into ML training code bases. We evaluate the performance of OpenAI (gpt-3.5-turbo) and WizardCoder (open-source, 15B parameters) models on the automated accomplishment of various MLOps functionalities in different settings. We perform a benchmarking study that assesses the ability of these models to: (1) adapt existing code samples (Inlining) with component-specific MLOps functionality such as MLflow and Weights & Biases for experiment tracking, Optuna for hyperparameter optimization etc., and (2) perform the task of Translation from one component of an MLOps functionality to another, e.g., translating existing GitPython library based version control code to Data Version Control library based. We also propose three different approaches that involve teaching LLMs to comprehend the API documentation of the components as a reference while accomplishing the Translation tasks. In our evaluations, the gpt-3.5-turbo model significantly outperforms WizardCoder by achieving impressive Pass@3 accuracy in model optimization (55% compared to 0% by WizardCoder), experiment tracking (100%, compared to 62.5% by WizardCoder), model registration (92% compared to 42% by WizardCoder) and hyperparameter optimization (83% compared to 58% by WizardCoder) on average, in their best possible settings, showcasing its superior code adaptability performance in complex MLOps tasks.

cs.LG↗

arXiv2023

Spatial information allows inference of the prevalence of direct cell-to-cell viral infection

The role of direct cell-to-cell spread in viral infections - where virions spread between host and susceptible cells without needing to be secreted into the extracellular environment - has come to be understood as essential to the dynamics of medically significant viruses like hepatitis C and influenza. Recent work in both the experimental and mathematical modelling literature has attempted to quantify the prevalence of cell-to-cell infection compared to the conventional free virus route using a variety of methods and experimental data. However, estimates are subject to significant uncertainty and moreover rely on data collected by inhibiting one mode of infection by either chemical or physical factors, which may influence the other mode of infection to an extent which is difficult to quantify. In this work, we conduct a simulation-estimation study to probe the practical identifiability of the proportion of cell-to-cell infection, using two standard mathematical models and synthetic data that would likely be realistic to obtain in the laboratory. We show that this quantity cannot be estimated using non-spatial data alone, and that the collection of a data which describes the spatial structure of the infection is necessary to infer the proportion of cell-to-cell infection. Our results provide guidance for the design of relevant experiments and mathematical tools for accurately inferring the prevalence of cell-to-cell infection in $\textit{in vitro}$ and $\textit{in vivo}$ contexts.

q-bio.QM↗

arXiv2023

Star Cluster Classification using Deep Transfer Learning with PHANGS-HST

Currently available star cluster catalogues from HST imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. The time-consuming nature of this process has limited the production of reliable catalogues and thus also post-observation analysis. To address this problem, deep transfer learning has recently been used to create neural network models which accurately classify star cluster morphologies at production scale for nearby spiral galaxies (D < 20 Mpc). Here, we use HST UV-optical imaging of over 20,000 sources in 23 galaxies from the Physics at High Angular Resolution in Nearby GalaxieS (PHANGS) survey to train and evaluate two new sets of models: i) distance-dependent models, based on cluster candidates binned by galaxy distance (9-12 Mpc, 14-18 Mpc, 18-24 Mpc), and ii) distance-independent models, based on the combined sample of candidates from all galaxies. We find that the overall accuracy of both sets of models is comparable to previous automated star cluster classification studies (~60-80 per cent) and show improvement by a factor of two in classifying asymmetric and multi-peaked clusters from PHANGS-HST. Somewhat surprisingly, while we observe a weak negative correlation between model accuracy and galactic distance, we find that training separate models for the three distance bins does not significantly improve classification accuracy. We also evaluate model accuracy as a function of cluster properties such as brightness, colour, and SED-fit age. Based on the success of these experiments, our models will provide classifications for the full set of PHANGS-HST candidate clusters (N ~ 200,000) for public release.

astro-ph.GA↗

arXiv2023

The impact of HII regions on Giant Molecular Cloud properties in nearby galaxies sampled by PHANGS ALMA and MUSE

We identify giant molecular clouds (GMCs) associated with HII regions for a sample of 19 nearby galaxies using catalogs of GMCs and H regions released by the PHANGS-ALMA and PHANGS-MUSE surveys, using the overlap of the CO and H{\alpha} emission as the key criterion for physical association. We compare the distributions of GMC and HII region properties for paired and non-paired objects. We investigate correlations between GMC and HII region properties among galaxies and across different galactic environments to determine whether GMCs that are associated with HII regions have significantly distinct physical properties to the parent GMC population. We identify trends between the H{\alpha} luminosity of an HII region and the CO peak brightness and the molecular mass of GMCs that we tentatively attribute to a direct physical connection between the matched objects, and which arise independently of underlying environmental variations of GMC and HII region properties within galaxies. The study of the full sample nevertheless hides a large variability galaxy by galaxy. Our results suggests that at the ~100 pc scales accessed by the PHANGS-ALMA and PHANGS-MUSE data, pre-supernova feedback mechanisms in HII regions have a subtle but measurable impact on the properties of the surrounding molecular gas, as inferred from CO observations.

astro-ph.GA↗

arXiv2023

Improving Star Cluster Age Estimates in PHANGS-HST Galaxies and the Impact on Cluster Demographics in NGC 628

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 Halpha 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 $\sim$ 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 $\sim$ 300 Myr clusters around the central region of NGC 1365. and discuss how this results naturally from the dynamics in a barred galaxy.

astro-ph.GA↗

arXiv2022

CI and CO in Nearby Spiral Galaxies -- I. Line Ratio and Abundance Variations at ~ 200 pc Scales

We present new neutral atomic carbon [CI](3P1-3P0) mapping observations within the inner ~7 kpc and ~4 kpc of the disks of NGC3627 and NGC4321 at a spatial resolution of 190 pc and 270 pc, respectively, using the ALMA Atacama Compact Array (ACA). We combine these with the CO(2-1) data from PHANGS-ALMA, and literature [CI] and CO data for two other starburst and/or active galactic nucleus (AGN) galaxies (NGC1808, NGC7469), to study: a) the spatial distributions of CI and CO emission; b) the observed line ratio RCICO = I_[CI](1-0)/I_CO(2-1) as a function of various galactic properties; and c) the abundance ratio of [CI/CO]. We find excellent spatial correspondence between CI and CO emission and nearly uniform RCICO ~0.1 across the majority of the star-forming disks of NGC3627 and NGC4321. However, RCICO strongly varies from ~0.05 at the centre of NGC4321 to >0.2-0.5 in NGC1808's starburst centre and NGC7469's centre with an X-ray AGN. Meanwhile, RCICO does not obviously vary with $U$, similar to the prediction of PDR models. We also find a mildly decreasing RCICO with an increasing metallicity over 0.7-0.85 solar metallicity, consistent with the literature. Assuming various typical ISM conditions representing GMCs, active star-forming regions and strong starbursting environments, we calculate the LTE radiative transfer and estimate the [CI/CO] abundance ratio to be ~0.1 across the disks of NGC3627 and NGC4321, similar to previous large-scale findings in Galactic studies. However, this abundance ratio likely has a substantial increase to ~1 and >1-5 in NGC1808's starburst and NGC7469's strong AGN environments, respectively, in line with the expectations for cosmic-ray dominated region (CRDR) and X-ray dominated region (XDR) chemistry. Finally, we do not find a robust evidence for a generally CO-dark, CI-bright gas in the disk areas we probed. (abbreviated)

astro-ph.GA↗

arXiv2022

PHANGS-JWST First Results: Dust embedded star clusters in NGC 7496 selected via 3.3 $\mu$m PAH emission

The earliest stages of star formation occur enshrouded in dust and are not observable in the optical. Here we leverage the extraordinary new high-resolution infrared imaging from JWST to begin the study of dust-embedded star clusters in nearby galaxies throughout the local volume. We present a technique for identifying dust-embedded clusters in NGC 7496 (18.7 Mpc), the first galaxy to be observed by the PHANGS-JWST Cycle 1 Treasury Survey. We select sources that have strong 3.3$\mu$m PAH emission based on a $\rm F300M-F335M$ color excess, and identify 67 candidate embedded clusters. Only eight of these are found in the PHANGS-HST optically-selected cluster catalog and all are young (six have SED-fit ages of $\sim1$ Myr). We find that this sample of embedded cluster candidates may significantly increase the census of young clusters in NGC 7496 from the PHANGS-HST catalog -- the number of clusters younger than $\sim$2 Myr could be increased by a factor of two. Candidates are preferentially located in dust lanes, and are coincident with peaks in PHANGS-ALMA CO (2-1) maps. We take a first look at concentration indices, luminosity functions, SEDs spanning from 2700A to 21$\mu$m, and stellar masses (estimated to be between $\sim10^4-10^5 M_{\odot}$). The methods tested here provide a basis for future work to derive accurate constraints on the physical properties of embedded clusters, characterize the completeness of cluster samples, and expand analysis to all 19 galaxies in the PHANGS-JWST sample, which will enable basic unsolved problems in star formation and cluster evolution to be addressed.

astro-ph.GA↗