arXiv subjects

Jianling Tang

Publications and source records attributed to Jianling Tang.

3 recordsLinked to original sources

arXiv2026

The Environmental Dependence of Star Cluster Demographics

Both the star cluster mass function and the lifetimes of clusters may vary with galactic environment, but measuring this variation is challenging because in observational surveys real features of cluster demographics are invariably entangled with catalogue incompleteness. Here we analyse $\approx$ 8300 star clusters in 12 galaxies drawn from the LEGUS survey using the slug Bayesian forward modelling framework coupled to our new c-4 neural network-based completeness estimator, which allows us to incorporate realistic catalogue-inclusion probabilities directly into the likelihood and compensate for these biases. We show that this approach allows us to fit observed cluster luminosity functions with excellent fidelity at both galactic and sub-galactic scales. We find that mass function slopes are relatively universal and broadly consistent with a power law $M^{-2}$ form, but high mass truncations vary by orders of magnitude both between and within galaxies. Our fits also strongly favour models where cluster disruption is mass-independent, but the time at which disruption begins again shows wide environmental variations. Our results demonstrate that young cluster demographics are environmentally dependent, with the clearest signal appearing at the upper end of the cluster mass function, but that these variations are not well-explained by any of the models currently in the literature, and do not correlate straightforwardly with properties such as star formation rate per unit area or strength of shear.

astro-ph.GA↗

arXiv2026

The Cluster Completeness Correction Calculator (C-4): A Neural-Network framework and pilot application to the LEGUS Survey of NGC 628

Integrated-light star cluster catalogues in external galaxies are subject to complex, often poorly-characterised selection effects that can bias inferred cluster demographics and introduce significant uncertainties, limiting the physical parameter space accessible to analysis. To mitigate this problem, here we introduce the Cluster Completeness Correction Calculator (C-4): a new software tool to quantify and predict these effects in both physical and photometric parameter spaces. C-4 adds artificial star clusters to observed galaxy images, processes these images through the same detection and filtering steps used to construct the original cluster catalogue, and then trains multilayer perceptron neural networks to learn the resulting selection function. The trained neural networks provide continuous, differentiable completeness functions that can be used for direct completeness corrections or incorporated into forward models. We present a pilot application of C-4 to NGC~628, demonstrating that the learned selection operator is highly accurate and successfully captures the strongly non-separable dependence of completeness on mass, age, and extinction. Applying the completeness correction to NGC 628 extends the range of cluster demographic analyses by roughly an order of magnitude in both mass and age, and removes artificial flattening in the observed cluster mass and age distributions. These results establish neural-network-based completeness modelling as a powerful and general approach for recovering intrinsic cluster populations, and provide a scalable framework for modelling high-dimensional selection functions in resolved stellar population studies.

astro-ph.IM↗

arXiv2023

Cluster Population Demographics in NGC 628 Derived from Stochastic Population Synthesis Models

The physical properties of star cluster populations offer valuable insights into their birth, evolution, and disruption. However, individual stars in clusters beyond the nearest neighbours of the Milky Way are unresolved, forcing analyses of star cluster demographics to rely on integrated light, a process fraught with uncertainty. Here we infer the demographics of the cluster population in the benchmark galaxy NGC 628 using data from the Legacy Extra-galactic UV Survey (LEGUS) coupled to a novel Bayesian forward-modelling technique. Our method analyzes all 1178 clusters in the LEGUS catalogue, $\sim4$ times more than prior studies severely affected by completeness cuts. Our results indicate that the cluster mass function is either significantly steeper than the commonly-observed slope of $-2$ or is truncated at $\approx 10^{4.5}$ M$_\odot$; the latter possibility is consistent with proposed relations between truncation mass and star formation surface density. We find that cluster disruption is relatively mild for the first $\approx 200$ Myr of cluster evolution; no evidence for mass-dependent disruption is found. We find suggestive but not incontrovertible evidence that inner galaxy clusters may be more prone to disruption and outer galaxy clusters have a more truncated mass function, but confirming or refuting these findings will require larger samples from future observations of outer galaxy clusters. Finally, we find that current stellar track and atmosphere models, along with common forms for cluster mass and age distributions, cannot fully capture all features in the multidimensional photometric distribution of star clusters. While our forward-modelling approach outperforms earlier backward-modeling approaches, some systematic differences persist between observed and modelled photometric distributions.

astro-ph.GA↗