Tidal tails in open clusters: Morphology, binary fraction, dynamics, and rotation

Recent studies using Gaia data have reported tidal tail detections for tens to hundreds of open clusters. However, a comprehensive assessment of the reliability and completeness of these detections is lacking. This work aims to summarise the expected properties of tidal tails based on N-body simulations, review the reliability of tidal tail detections in the literature, and grade them according to a set of diagnostic tests. We also provide an overview of the general characteristics of tidal tails available in the literature. We used a grid of 68--20000 simulated clusters and analysed the formation and evolution of the tidal tails. We compiled 122 catalogues from the recent literature, encompassing 58 unique clusters within 500 pc of the Sun. We employed various tests based on photometric, morphological, and dynamical signatures and comparisons with simulated clusters to grade the tidal tails as gold, silver, and bronze. One of the primary tests was to measure apparent torsion in the Galactocentric XY plane. Based on the simulations, we analysed the complex morphology of the tidal tails and their properties (such as their size, span, stellar types, number density, and mass function) at various cluster masses and ages. During the first 100--200 Myr of evolution, the tails typically form a characteristic S shape, with an amplitude that scales with cluster mass. The tail span increases at a rate of ≈4 times the initial velocity dispersion, and the near-tail (within 100 pc of the cluster) is predominantly populated by recent escapees. In evaluating 122 published tidal tail catalogues, we found that 15 gold-quality catalogues and 55 silver-quality catalogues passed the majority of the tests. The remaining 51 catalogues were graded as bronze; care should be taken before using these catalogues for further analysis. The age, metallicity, binary fraction, and mass function of stars in the tails were generally consistent with those of their parent clusters. The simulations presented here provide first-order approximations of the structure and evolution of the tidal tails. The gold and silver-grade catalogues (69 catalogues of 40 clusters) represent reliable samples for detailed analyses of tidal tails. Future spectroscopic and astrometric data from large-scale surveys will be essential for further validation and for leveraging tidal tails as tracers of cluster dissolution and the Galactic potential.

Context. Tidal tails of open clusters are the result of stellar evaporation from the cluster through the Galactic potential and internal dynamics. With the recent availability of high-precision data, tidal tails are being detected for most of the nearby open clusters. Aims. We identify the tidal tail members for all open clusters within a distance of 400 pc that are older than 100 Myr and have >100 members. To do this, we use model-independent methods. Methods. We used the convergent-point (CP) method to identify the co-moving stars near the open clusters using Gaia DR3 data. A new method called the self-compact convergent-point method was proposed and applied to some of the clusters. It performed better overall in tracing the tails. We also analysed the colour-magnitude diagrams and orbital energy to diagnose possible contamination. Results. Nineteen out of 21 clusters have tidal tails. Five of them were discovered for the first time through this work. The typical span of the tidal tails is 20–200 pc, and 30–700 member stars lie in the region inside the tidal radius and the tidal tails. Four out of 19 tidal tails are tilted away from direction of the Galactic centre. This contradicts the known theory of the tidal-tail formation. The luminosity functions of the tails and clusters are consistent with each other and with the canonical stellar interstellar mass function, but systematically higher radial velocities for the trailing tail than for the leading tail were observed for the first time. Conclusions. The CP method is useful for detecting tidal tails on a scale of ≈100 pc for clusters closer than 400 pc. A further analysis of theoretical N-body models is required to understand the incompleteness and biases in the current sample of tidal tails.

Morphology of open clusters NGC 1857 and Czernik 20 using clustering algorithms

Open clusters (OCs) are common in the Milky Way, but most of them remain undiscovered. There are numerous techniques, including some machine-learning algorithms, available for the exploration of OCs. However, each method has its limitations and therefore, different approaches to discovering OCs hold significant values. We develop a comprehensive approach method to automatically explore the data space and identify potential OC candidates with relatively reliable membership determination. This approach combines the techniques of Hierarchical Density-Based Spatial Clustering of Applications with Noise, Gaussian mixture model, and a novel cluster member identification technique, color excess constraint. The new method exhibits efficiency in detecting OCs while ensuring precise determination of cluster memberships. Because the main feature of this technique is to add an extra constraint (EC) for the members of cluster candidates using the homogeneity of color excess, compared to typical blind search codes, it is called Blind Search-Extra Constraint (BSEC) method. It is successfully applied to the Gaia Data Release 3, and 83 new OCs are found, whose color–magnitude diagrams (CMDs) are fitted well to the isochrones. In addition, this study reports 621 new OC candidates with discernible main sequence or red giant branch. It is shown that BSEC technique can discard some false negatives of previous works, which takes about three percentage of known clusters. It shows that as an EC, the color excess (or two-color) constraint is useful for removing fake cluster member stars from the clusters that are identified from the positions and proper motions of stars, and getting more precise CMDs, when differential reddening of member stars of a cluster is not large (e.g., ΔE(G BP − G RP) < 0.5 mag). It makes the CMDs of 15% clusters clearer (in particular for the region near turnoff) and therefore is helpful for CMD and stellar population studies. Our result suggests that the color excess constraint is more appropriate for clusters with small differential reddening, such as globular clusters or older OCs, and clusters that the distances of member stars cannot be determined accurately.

In our previous work, we introduced a method that combines two unsupervised algorithms: Density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM). We applied this method to 12 open clusters based on Gaia Early Data Release 3 (EDR3) data, demonstrating its effectiveness in identifying reliable cluster members within the tidal radius. However, for studying cluster morphology, we need a method capable of detecting members both inside and outside the tidal radius. By incorporating a supervised algorithm into our approach, we successfully identified members beyond the tidal radius. In our current work, we initially applied DBSCAN and GMM to identify reliable members of cluster stars. Subsequently, we trained the random forest algorithm using DBSCAN- and GMM-selected data. Leveraging the random forest, we can identify cluster members outside the tidal radius and observe cluster morphology across a wide field of view. Our method was then applied to 15 open clusters based on Gaia DR3, which exhibit a wide range of metallicity, distances, members, and ages. Additionally, we calculated the tidal radius for each of the 15 clusters using the King profile and detected stars both inside and outside this radius. Finally, we investigated mass segregation and luminosity distribution within the clusters. Overall, our approach significantly improved the estimation of the tidal radius and detection of mass segregation compared to the previous work. We found that in Collinder 463, low-mass stars do not segregate in comparison to high-mass and intermediate-mass stars. Additionally, we detected a peak of luminosity in the clusters, some of which were located far from the centre, beyond the tidal radius.

After their birth a significant fraction of all stars pass through the tidal threshold (práh) of their cluster of origin into the classical tidal tails. The asymmetry between the number of stars in the leading and trailing tails tests gravitational theory. All five open clusters with tail data (Hyades, Praesepe, Coma Berenices, COIN-Gaia 13, NGC 752) have visibly more stars within $d_{\rm cl}\approx 50\,{\rm pc}$ of their centre in their leading than their trailing tail. Using the Jerabkova-compact-convergent-point (CCP) method, the extended tails have been mapped out for four nearby 600–2000 Myr old open clusters to $d_{\rm cl} &amp;gt; 50\,{\rm pc}$. These are on near-circular Galactocentric orbits, a formula for estimating the orbital eccentricity of an open cluster being derived. Applying the Phantom of Ramses code to this problem in Newtonian gravitation the tails are near-symmetrical. In Milgromian dynamics (MOND), the asymmetry reaches the observed values for 50 &amp;lt; dcl/pc &amp;lt; 200 being maximal near peri-galacticon, and can slightly invert near apo-galacticon, and the Küpper epicyclic overdensities are asymmetrically spaced. Clusters on circular orbits develop orbital eccentricity due to the asymmetrical spill-out, therewith spinning up opposite to their orbital angular momentum. This positive dynamical feedback suggests Milgromian open clusters to demise rapidly as their orbital eccentricity keeps increasing. Future work is necessary to better delineate the tidal tails around open clusters of different ages and to develop a Milgromian direct n-body code.

This research addresses a critical issue in industrial environments: air quality, specifically regarding PM 1.0 and PM 2.5. High concentrations of these particles pose significant health risks. The study measures temperature, humidity, pressure, altitude, PM 1.0, and PM 2.5 and shows the effectiveness of using AIOT-Particle devices to analyze these features with Principal Component Analysis (PCA). Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to detect anomalies during the observation period. Anomalies occur when the altitude ranges from 65 to 70 units, according to PM 1.0 and PM 2.5 values. The positions where anomalies occur are illustrated based on altitude, temperature, pressure, and concentration. The results demonstrate that altitude dominates as the first feature. Finally, the research concludes that altitude, PM 1.0, and PM 2.5 are the dominant features. The study confirms the effectiveness of PCA and recommends using these three features for anomaly detection in DBSCAN. Overall, the research highlights the novelty and success of AIOT-Particle in industrial environments.

Context.Data from theGaiasatellite are revolutionising our understanding of the Milky Way. With every new data release, there is a need to update the census of open clusters.Aims.We aim to conduct a blind, all-sky search for open clusters using 729 million sources fromGaiaDR3 down to magnitudeG ∼ 20, creating a homogeneous catalogue of clusters including many new objects.Methods.We used the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm to recover clusters. We validated our clusters using a statistical density test and a Bayesian convolutional neural network for colour-magnitude diagram classification. We inferred basic astrometric parameters, ages, extinctions, and distances for the clusters in the catalogue.Results.We recovered 7167 clusters, 2387 of which are candidate new objects and 4782 of which crossmatch to objects in the literature, including 134 globular clusters. A more stringent cut of our catalogue contains 4105 highly reliable clusters, 739 of which are new. Owing to the scope of our methodology, we are able to tentatively suggest that many of the clusters we are unable to detect may not be real, including 1152 clusters from the Milky Way Star Cluster (MWSC) catalogue that should have been detectable inGaiadata. Our cluster membership lists include many new members and often include tidal tails. Our catalogue’s distribution traces the galactic warp, the spiral arm structure, and the dust distribution of the Milky Way. While much of the content of our catalogue contains bound open and globular clusters, as many as a few thousand of our clusters are more compatible with unbound moving groups, which we will classify in an upcoming work.Conclusions.We have conducted the largest search for open clusters to date, producing a single homogeneous star cluster catalogue which we make available with this paper.

Neuroblastoma is a common pediatric cancer that affects thousands of infants worldwide, especially children under five years of age. Although recovery for patients with neuroblastoma is possible in 80% of cases, only 40% of those with high-risk stage four neuroblastoma survive. Electronic health records of patients with this disease contain valuable data on patients that can be analyzed using computational intelligence and statistical software by biomedical informatics researchers. Unsupervised machine learning methods, in particular, can identify clinically significant subgroups of patients, which can lead to new therapies or medical treatments for future patients belonging to the same subgroups. However, access to these datasets is often restricted, making it difficult to obtain them for independent research projects. In this study, we retrieved three open datasets containing data from patients diagnosed with neuroblastoma: the Genoa dataset and the Shanghai dataset from the Neuroblastoma Electronic Health Records Open Data Repository, and a dataset from the TARGET-NBL renowned program. We analyzed these datasets using several clustering techniques and measured the results with the DBCV (Density-Based Clustering Validation) index. Among these algorithms, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) was the only one that produced meaningful results. We scrutinized the two clusters of patients’ profiles identified by DBSCAN in the three datasets and recognized several relevant clinical variables that clearly partitioned the patients into the two clusters that have clinical meaning in the neuroblastoma literature. Our results can have a significant impact on health informatics, because any computational analyst wishing to cluster small data of patients of a rare disease can choose to use DBSCAN and DBCV rather than utilizing more common methods such as k-Means and Silhouette coefficient.

We propose that 14 co-moving groups of stars uncovered by Kounkel &amp; Covey may be related to known nearby moving groups and bridge those and nearby open clusters with similar ages and space velocities. This indicates that known nearby moving groups may be spatially much more extended than previously thought, and some of them might be parts of tidal tails around the cores of known open clusters, reminiscent of those recently found around the Hyades and a handful of other nearby clusters. For example, we find that both the nearby Carina and Columba associations may be linked to Theia 208 from Kounkel &amp; Covey and together form parts of a large tidal tail around the Platais 8 open cluster. The AB Doradus moving group and Theia 301 may form a trailing tidal tail behind the Pleiades open cluster, with hints of a possible leading tidal tail in Theia 369. Similarly, we find that IC 2391 and its tidal tails identified by Meingast et al. may be extended by the nearby Argus association and are possibly further extended by Theia 115. The nearby Octans and Octans-Near associations, as well as Theia 94 and 95, may form a large tidal tail leading the poorly studied Platais 5 open cluster candidate. While a preliminary analysis of Gaia color–magnitude sequences hint that these structures are plausibly related, more observational evidence is still required to corroborate their consistent ages and space velocities. These observations may change our current understanding of nearby moving groups and the different pathways through which they can form. While some moving groups may have formed loosely in extended star formation events with rich spatial structure, others may in fact correspond to the tidal tails of nearby open clusters.

DBSCAN Clustering Algorithm for the Detection of Nearby Open Clusters Based on Gaia-DR2two

The study of binary stars in different astronomical environments offers insights into the dynamical state of the hosting stellar systems. The binary fraction in fact plays a crucial role in the dynamical evolution of stellar systems, regulating processes like mass segregation and dynamical heating, and in some cases leading to the formation of exotic objects, like, for instance, blue straggler stars. We used two methodologies to estimate the binary fraction in three different-age open star clusters: FSR 866, NGC 1960 (M36), and Stock 2. The first is a photometric approach based on color–magnitude diagram analysis, and the second is a spectroscopic technique that employs radial velocity measurements. We used Gaia DR3 data in tandem with new spectroscopic observations, and employed the Density-Based Spatial Clustering of Applications with Noise algorithm to identify probable cluster members based on proper motion and parallax in 3D space. The new sample of cluster members allows us to provide new estimates of the cluster fundamental parameters. As a by-product, we found two previously undetected, small physical groups of stars in the background of NGC 1960. The resulting binary fractions lie in the range 0.3–0.5 and are in good agreement with those expected theoretically for open clusters.

We propose a mixture model of open clusters (OCs) in color–magnitude diagrams (CMDs) to measure the OC properties, including isochrone parameters (age, distance, metallicity, and dust extinction), stellar mass function (MF), and binary parameters (binary fraction and mass-ratio distribution), with high precision and reliability. The model treats an OC in the CMD as a mixture of single and binary member stars and field stars in the same region. The cluster members are modeled using a theoretical stellar model, mass function, and binary properties. The field component is modeled nonparametrically using a separate field-star sample in the vicinity of the cluster. Unlike conventional methods that rely on stringent member selection, ours allows us to use a sample of more complete cluster members and attendant field stars. The larger star sample reduces the statistical error and diminishes the potential bias by retaining more stars that are crucial for age estimation and MF measurement. After validating the method with 1000 mock clusters, we measured the parameters of 10 real OCs using Gaia EDR3 data. The best-fit isochrones are consistent with previous measurements in general but with more precise age estimates for several OCs. The inferred MF slope is −2.7 to −1.6 for clusters younger than 2 Gyr, while older clusters appear to have significantly flatter MFs. The binary fraction is 30%–50%. The photometric and astrometric distances agree well.

Based on the homogeneous low-resolution spectra data observed by Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) and the Gaia Data Release 2 (Gaia DR2) photometric data, we chose 12 open clusters (OCs) to study their fundamental parameters and binary fractions (BFs). For each OC, more than 20 cluster members were observed by LAMOST, and some of them were observed multiple times. We used these data to derive precise metallicities of OCs. Combining the metallicities and the Gaia DR2 photometric data, we used the isochrone fitting method to obtain fundamental parameters of these OCs. By fixing metallicity during the fitting, we avoided the effect of degeneracy between the metallicity and reddening. Based on the fundamental parameters, we utilized the synthetic color–magnitude diagram (CMD) method to derive the BFs of main-sequence (MS) stars, the mass functions (MFs) with correction of binaries, and the companion mass ratio distributions. The BFs of observed MS stars in OCs in this paper vary from 29% to 55%. Extrapolating the synthetic CMD to the hydrogen-burning limit, the BFs vary from 13% to 47%. Combining MFs published by previous literature with our results, we found that MFs with correction of binaries were steeper than those without correction by Δα = 0.6 ± 0.16 in the mass range of m ∈ [1M ⊙, 2.67M ⊙]. We found that the companion mass ratio distribution in OCs of our sample was flat. We also found a significant positive correlation between the BF and density.

Purpose. To develop an automated method based on machine learning for accurate detection of features of a damaged building on digital imagery. Methodology. This article presents an approach that employs a combination of unsupervised machine learning techniques, specifically Principal Component Analysis (PCA), K-means clustering, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), to identify building damage resulting from military conflicts. The PCA method is utilized to identify principal vectors representing the directions of maximum variance in the data. Subsequently, the K-means method is applied to cluster the feature vector space, with the predefined number of clusters reflecting the number of principal vectors. Each cluster represents a group of similar blocks of image differences, which helps to identify significant features associated with fractures. Finally, the DBSCAN method is employed to identify areas where points with similar characteristics are located. Subsequently, a binary fracture mask is generated, with pixels exceeding the threshold being identified as fractures. Findings. The introduced methodology attains an accuracy rate of 98.13 %, surpassing the performance of conventional methods such as DBSCAN, PCA, and K-means. Furthermore, the method exhibits a recall of 82.38 %, signifying its ability to effectively detect a substantial proportion of positive examples. Precision of 58.54 % underscores the methodology’s capability to minimize false positives. The F1 Score of 70.90 % demonstrates a well-balanced performance between precision and recall. Originality. DBSCAN, PCA and K-means methods have been further developed in the context of automated detection of building destruction in aerospace images. This allows us to significantly increase the accuracy and efficiency of monitoring territories, including those affected by the consequences of military aggression. Practical value. The results obtained can be used to improve automated monitoring systems for urban development and can also serve as the basis for the development of effective strategies for the restoration and reconstruction of damaged infrastructure.