Star cluster formation and feedback in different environments of a Milky Way-like galaxy

This is the author accepted manuscriptData availability: The data underlying this paper will be shared on reasonable request to the corresponding author.It remains unclear how galactic environment affects star formation and stellar cluster properties. This is difficult to address in Milky Way-mass galaxy simulations because of limited resolution and less accurate feedback compared to cloud-scale models. We carry out zoom-in simulations to re-simulate 100–300 pc regions of a Milky Way-like galaxy using smoothed particle hydrodynamics, including finer resolution (0.4 M⊙ per particle), cluster-sink particles, ray-traced photoionization from O stars, H2/CO chemistry, and ISM heating/cooling. We select ∼106 M⊙ cloud complexes from a galactic bar, inner spiral arm, outer arm, and inter-arm region (in order of galactocentric radius), retaining the original galactic potentials. The surface densities of star formation rate and neutral gas follow ΣSFR ∝ Σ 1.3 gas, with the bar lying higher up the relation than the other regions. However, the inter-arm region forms stars 2–3x less efficiently than the arm models at the same Σgas. The bar produces the most massive cluster, the inner arm the second, and the inter-arm the third. Almost all clusters in the bar and inner arm are small (radii < 5 pc), while 30-50 per cent of clusters in the outer arm and inter-arm have larger radii more like associations. Bar and inner arm clusters rotate at least twice as fast, on average, than clusters in the outer arm and inter-arm regions. The degree of spatial clustering also decreases from bar to inter-arm. Our results indicate that young massive clusters, potentially progenitors of globular clusters, may preferentially form near the bar/inner arm compared to outer arm/inter-arm regions.European Commissio

High-contrast and resolution near-infrared photometry of the core of R136

We present the sharpest and deepest near-infrared photometric analysis of the core of R136, a newly formed massive star cluster at the centre of the 30 Doradus star-forming region in the Large Magellanic Cloud. We used the extreme adaptive optics of the SPHERE focal instrument implemented on the ESO Very Large Telescope and operated in its IRDIS imaging mode for the second time with longer exposure time in the H and K filters. Our aim was to (i) increase the number of resolved sources in the core of R136, and (ii) to compare with the first epoch to classify the properties of the detected common sources between the two epochs. Within the field of view (FOV) of 10.8″ × 12.1″ (⁠2.7pc×3.0pc⁠), we detected 1499 sources in both H and K filters, for which 76 per cent of these sources have visual companions closer than 0.2″. The larger number of detected sources enabled us to better sample the mass function (MF). The MF slopes are estimated at ages of 1, 1.5, and 2 Myr, at different radii, and for different mass ranges. The MF slopes for the mass range of 10–300 M⊙ are about 0.3 dex steeper than the mass range of 3–300 M⊙, for the whole FOV and different radii. Comparing the JHK colours of 790 sources common in between the two epochs, 67 per cent of detected sources in the outer region (r > 3″) are not consistent with evolutionary models at 1–2 Myr and with extinctions similar to the average cluster value, suggesting an origin from ongoing star formation within 30 Doradus, unrelated to R136

A code to Make Your Own Synthetic ObservaTIonS (MYOSOTIS)

We introduce our new code MYOSOTIS (Make Your Own Synthetic ObservaTIonS) which is designed to produce synthetic observations from simulated clusters. The code can synthesize observations from both ground-and spaced-based observatories, for a range of different filters, observational conditions and angular/spectral resolution. In this paper, we highlight some of the features of MYOSOTIS, creating synthetic observations from young massive star clusters. Our model clusters are simulated using NBODY6 code and have different total masses, halfmass radii, and binary fractions. The synthetic observations are made at the age of 2 Myr with Solar metallicity and under different extinction conditions. For each cluster, we create synthetic images of the Hubble Space Telescope (HST) in the visible (WFPC2/F555W) as well as Very Large Telescopes in the nearIR (SPHERE/IRDIS/Ks). We show how MYOSOTIS can be used to look at mass function (MF) determinations. For this aim we re-estimate stellar masses using a photometric analysis on the synthetic images. The synthetic MF slopes are compared to their actual values. Our photometric analysis demonstrate that depending on the adopted filter, extinction, angular resolution, and pixel sampling of the instruments, the power-law index of the underlying MFs can be shallower than the observed ones by at least ±0.25 dex which is in agreement with the observed discrepancies reported in the literature, specially for young star clusters

The spatial evolution of young massive clusters - I. A new tool to quantitatively trace stellar clustering

Context. There are a number of methods that identify stellar sub-structure in star forming regions, but these do not quantify the degree of association of individual stars – something which is required if we are to better understand the mechanisms and physical processes that dictate structure. Aims. We present the new novel statistical clustering tool “INDICATE” which assesses and quantifies the degree of spatial clustering of each object in a dataset, discuss its applications as a tracer of morphological stellar features in star forming regions, and to look for these features in the Carina Nebula (NGC 3372). Methods. We employ a nearest neighbour approach to quantitatively compare the spatial distribution in the local neighbourhood of an object with that expected in an evenly spaced uniform (i.e. definitively non-clustered) field. Each object is assigned a clustering index (“I”) value, which is a quantitative measure of its clustering tendency. We have calibrated our tool against random distributions to aid interpretation and identification of significant I values. Results. Using INDICATE we successfully recover known stellar structure of the Carina Nebula, including the young Trumpler 14-16, Treasure Chest and Bochum 11 clusters. Four sub-clusters contain no, or very few, stars with a degree of association above random which suggests these sub-clusters may be fluctuations in the field rather than real clusters. In addition we find: (1) Stars in the NW and SE regions have significantly different clustering tendencies, which is reflective of differences in the apparent star formation activity in these regions. Further study is required to ascertain the physical origin of the difference; (2) The different clustering properties between the NW and SE regions are also seen for OB stars and are even more pronounced; (3) There are no signatures of classical mass segregation present in the SE region – massive stars here are not spatially concentrated together above random; (4) Stellar concentrations are more frequent around massive stars than typical for the general population, particularly in the Tr14 cluster; (5) There is a relation between the concentration of OB stars and the concentration of (lower mass) stars around OB stars in the centrally concentrated Tr14 and Tr15, but no such relation exists in Tr16. We conclude this is due to the highly sub-structured nature of Tr16. Conclusions. INDICATE is a powerful new tool employing a novel approach to quantify the clustering tendencies of individual objects in a dataset within a user-defined parameter space. As such it can be used in a wide array of data analysis applications. In this paper we have discussed and demonstrated its application to trace morphological features of young massive clusters

S2D2: Small-scale Significant substructure DBSCAN Detection

Context. The spatial and dynamical structure of star-forming regions can offer insights into stellar formation patterns. The amount of data from current and upcoming surveys calls for robust and objective procedures for detecting structures in order to statistically analyse the various regions and compare them. Aims. We aim to provide the community with a tool capable of detecting, above random expectations, the small-scale significant structure in star-forming regions that could serve as an imprint of the stellar formation process. The tool makes use of the one-point correlation function to determine an appropriate length scale for ϵ and uses nearest-neighbour statistics to determine a minimum number of points Nmin for the DBSCAN algorithm in the neighbourhood of ϵ. Methods. We implemented the procedure and applied it to synthetic star-forming regions of different nature and characteristics to obtain its applicability range. We also applied the method to observed star-forming regions to demonstrate its performance in realistic circumstances and to analyse its results. Results. The procedure successfully detects significant small-scale substructures in heterogeneous regions, fulfilling the goals it was designed for and providing very reliable structures. The analysis of regions close to complete spatial randomness (Q ∈ [0.7, 0.87]) shows that even when some structure is present and recovered, it is hardly distinguishable from spurious detection in homogeneous regions due to projection effects. Thus, any interpretation should be done with care. For concentrated regions, we detect a main structure surrounded by smaller ones, corresponding to the core plus some Poisson fluctuations around it. We argue that these structures do not correspond to the small compact regions we are looking for. In some realistic cases, a more complete hierarchical, multi-scale analysis would be needed to capture the complexity of the region. Conclusions. We carried out implementations of our procedure and devised a catalogue of the Nested Elementary STructures (NESTs) detected as a result in four star-forming regions (Taurus, IC 348, Upper Scorpius, and Carina). This catalogue is being made publicly available to the community. Implementations of the 3D versionsof the procedure, as well as up to 6D versions, including proper movements, are in progress and will be provided in a future work

Observational bias and young massive cluster characterization − II. Can Gaia accurately observe young clusters and associations?

This is the final version. Available on open access from Oxford University Press via the DOI in this recordData availability: The final stage field-of-view files, from which stellar members are selected, at 500, 2500, and 4300 pc for all three clusters are available to download from the Zenodo repository at https://zenodo.org/records/10053996. Additional distances and analysis data underlying this article will be shared on reasonable request to the corresponding author. The simulation data underlying this article were provided by CLD by permission, which will be shared on request to the corresponding author with permission of CLD.Observations of clusters suffer from issues such as completeness, projection effects, resolving individual stars, and extinction. As such, how accurate measurements and conclusions are likely to be? Here, we take cluster simulations (Westerlund2-and Orion-Type), synthetically observe them to obtain luminosities, accounting for extinction, and the inherent limits of Gaia, then place them within the real Gaia DR3 catalogue. We then attempt to rediscover the clusters at distances of between 500 and 4300 pc. We show the spatial and kinematic criteria that are best able to pick out the simulated clusters, maximizing completeness, and minimizing contamination. We then compare the properties of the 'observed' clusters with the original simulations. We looked at the degree of clustering, the identification of clusters and subclusters within the data sets, and whether the clusters are expanding or contracting. Even with a high level of incompleteness (e.g. stellar members identified), similar qualitative conclusions tend to be reached compared to the original data set, but most quantitative conclusions are likely to be inaccurate. Accurate determination of the number, stellar membership, and kinematic properties of subclusters are the most problematic to correctly determine, particularly at larger distances due to the disappearance of cluster substructure as the data become more incomplete, but also at smaller distances where the misidentification of asterisms as true structure can be problematic. Unsurprisingly, we tend to obtain better quantitative agreement of properties for our more massive Westerlund2-Type cluster. We also make optical style images of the clusters over our range of distances.European Union Horizon 202

Observational bias and young massive cluster characterisation I. 2D perspective effects

This is the author accepted manuscript. The final version is available from Oxford University Press via the DOI in this recordDATA AVAILABILITY: The simulation data underlying this article was provided by KYL by permission, which will shared on request to the corresponding author with permission of KYL. The analysis data underlying this article will be shared on reasonable request to the corresponding author.Understanding the formation and evolution of high mass star clusters requires comparisons between theoretical and observational data to be made. Unfortunately, while the full phase space of simulated regions is available, often only partial 2D spatial and kinematic data is available for observed regions. This raises the question as to whether cluster parameters determined from 2D data alone are reliable and representative of clusters real parameters and the impact of line-of-sight orientation. In this paper we derive parameters for a simulated cluster formed from a cloud-cloud collision with the full 6D phase space, and compare them with those derived from three different 2D line-of-sight orientations for the cluster. We show the same qualitative conclusions can be reached when viewing clusters in 2D versus 3D, but that drawing quantitative conclusions when viewing in 2D is likely to be inaccurate. The greatest divergence occurs in the perceived kinematics of the cluster, which in some orientations appears to be expanding when the cluster is actually contracting. Increases in the cluster density compounds pre-existing perspective issues, reducing the relative accuracy and consistency of properties derived from different orientations. This is particularly problematic for determination of the number, and membership, of subclusters present in the cluster. We find the fraction of subclusters correctly identified in 2D decreases as the cluster evolves, reaching less than $3.4{{\ \rm per\ cent}}$ at the evolutionary end point for our cluster.European Research CouncilScience and Technology Facilities CouncilEuropean Research Counci

The spatial evolution of young massive clusters III. Effect of the Gaia filter on 2D spatial distribution studies

Context. With the third release of the high-precision optical-wavelength Gaia survey, we are in a better position than ever before to study young clusters. However, Gaia is limited in the optical down to G ∼ 21 mag, and therefore it is essential to understand the biases introduced by a magnitude-limited sample on spatial distribution studies. Aims. We ascertain how sample incompleteness in Gaia observations of young clusters affects the local spatial analysis tool INDICATE and subsequently the perceived spatial properties of these clusters. Methods. We created a mock Gaia cluster catalogue from a synthetic dataset using the observation generating tool MYOSOTIS. The effect of cluster distance, uniform and variable extinction, binary fraction, population masking by the point spread function wings of high-mass members, and contrast sensitivity limits on the trends identified by INDICATE are explored. A comparison of the typical index values derived by INDICATE for members of the synthetic dataset and their corresponding mock Gaia catalogue observations is made to identify any significant changes. Results. We typically find only small variations in the pre- and post-observation index values of cluster populations, which can increase as a function of incompleteness percentage and binarity. No significant strengthening or false signatures of stellar concentrations are found, but real signatures may be diluted. Conclusions drawn about the spatial behaviour of Gaia-observed cluster populations that are, and are not, associated with their natal nebulosity are reliable for most clusters, but the perceived behaviours of individual members can change, so INDICATE should be used as a measure of spatial behaviours between members as a function of their intrinsic properties (e.g., mass, age, object type), rather than to draw conclusions about any specific observed member. Conclusions. INDICATE is a robust spatial analysis tool to reliably study Gaia-observed young cluster populations within 1 kpc, up to a sample incompleteness of 83.3% and binarity of 50%

Mass segregation and sequential star formation in NGC 2264 revealed by Herschel

Context: The mass segregation of stellar clusters could be primordial rather than dynamical. Despite the abundance of studies of mass segregation for stellar clusters, those for stellar progenitors are still scarce, so the question concerning the origin and evolution of mass segregation is still open. Aims: Our goal is to characterize the structure of the NGC 2264 molecular cloud and compare the populations of clumps and young stellar objects (YSOs) in this region whose rich YSO population has shown evidence of sequential star formation. Methods: We separated the Herschel column density map of NGC 2264 into three subregions and compared their cloud power spectra using a multiscale segmentation technique. We extracted compact cloud fragments from the column density image, measured their basic properties, and studied their spatial and mass distributions. Results: In the whole NGC 2264 cloud, we identified a population of 256 clumps with typical sizes of ~0.1 pc and masses ranging from 0.08 M⊙ to 53 M⊙. Although clumps have been detected all over the cloud, most of the massive, bound clumps are concentrated in the central subregion of NGC 2264. The local surface density and the mass segregation ratio indicate a strong degree of mass segregation for the 15 most massive clumps, with a median Σ6 three times that of the whole clumps population and ΛMSR ≃ 8. We show that this cluster of massive clumps is forming within a high-density cloud ridge, which is formed and probably still fed by the high concentration of gas observed on larger scales in the central subregion. The time sequence obtained from the combined study of the clump and YSO populations in NGC 2264 suggests that the star formation started in the northern subregion, that it is now actively developing at the center, and will soon start in the southern subregion. Conclusions: Taken together, the cloud structure and the clump and YSO populations in NGC 2264 argue for a dynamical scenario of star formation. The cloud could first undergo global collapse, driving most clumps to centrally concentrated ridges. After their main accretion phase, some YSOs, and probably the most massive, would stay clustered while others would be dispersed from their birth sites. We propose that the mass segregation observed in some star clusters is inherited from that of clumps, originating from the mass assembly phase of molecular clouds