Kinematic differences between multiple populations in Galactic globular clusters

Martens, Sven; Kamann, Sebastian; Göttgens, Fabian; Husser, Tim-Oliver; Latour, M.; Balakina, Elena; Krajnović, Davor; Pechetti, Renuka; Weilbacher, Peter M.

doi:10.1051/0004-6361/202244787

Cited by 9 publications

(4 citation statements)

References 74 publications

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“…The modeling of the PSF allows to PampelMuse to differentiate the contribution of different sources. This tool has proven to be successful for deblending close stellar objects, even in denser and more crowded stellar systems as globular and nuclear star clusters (e.g., Alfaro-Cuello et al 2019;Kamann et al 2020;Martens et al 2023;Nitschai et al 2023).…”

Section: Observations and Stellar Spectra Extractionmentioning

confidence: 99%

NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data

Asa’d,

Hernandez,

John

et al. 2024

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We present the first application of the novel approach based on data-driven machine learning methods applied to Multi-Unit Spectroscopic Explorer (MUSE) field data to derive stellar abundances of star clusters. MUSE has been used to target more than 10,000 fields, and it is unique in its ability to study dense stellar fields such as stellar clusters providing spectra for each individual star. We use MUSE data of the extragalactic young stellar cluster NGC 1856, located in the Large Magellanic Cloud (LMC). We present the individual stellar [Fe/H] abundance of 327 cluster members in addition to [Mg/Fe], [Si/Fe], [Ti/Fe], [C/Fe], [Ni/Fe], and [Cr/Fe] abundances of subsample sets. Our results match the LMC abundances obtained in the literature for [Mg/Fe], [Ti/Fe], [Ni/Fe], and [Cr/Fe]. This study is the first to derive [Si/Fe] and [C/Fe] abundances for this cluster. The revolutionary combination of integral-field spectroscopy and data-driven modeling will allow us to understand the chemical enrichment of star clusters and their host galaxies in greater detail expanding our understanding of galaxy evolution.

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Section: Observations and Stellar Spectra Extractionmentioning

confidence: 99%

NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data

Asa’d,

Hernandez,

John

et al. 2024

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“…The expected depth of the multi-epoch survey (well bellow the turn-off in most clusters) will also allow for a view of MSPs from the main sequence to the red giant and asymptotic giant branches. The wide field nature of the survey will be important in understanding how the populations behave far away from the cluster center and synergies with Gaia can help expand our understanding of the dynamics of the different stellar populations (see e.g., Martens et al 2023).…”

Section: Multiple Stellar Populationsmentioning

confidence: 99%

Rubin Observatory LSST Stars Milky Way and Local Volume Star Clusters Roadmap

Usher,

Dage,

Girardi

et al. 2023

PASP

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The Vera C. Rubin Observatory will undertake the Legacy Survey of Space and Time, providing an unprecedented, volume-limited catalog of star clusters in the Southern Sky, including Galactic and extragalactic star clusters. The Star Clusters subgroup of the Stars, Milky Way and Local Volume Working Group has identified key areas where Rubin Observatory will enable significant progress in star cluster research. This roadmap represents our science cases and preparation for studies of all kinds of star clusters from the Milky Way out to distances of tens of megaparsecs.

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“…In some cases, the sample comes from two or more populations (Martens, et al, 2023). It is often an interesting question whether the effect of mediation is the same between different groups or under different conditions.…”

Section: A Introductionmentioning

confidence: 99%

Comparison of Mediation Effects on Interaction and Multigroup Approach in Structural Equation Modeling PLS in Case of Bank Mortgage

Maisaroh,

Fernandes,

Iriany

et al. 2024

JTAM

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“Structural Equation Modeling is one of multivariate statistical method that used to explain multiple relationships between latent variables simultaneously to test a mediation model to conduct a formal test on mediation effects. Application PLS-SEM for exploratory research and theory development are increasing. Under certain conditions, the effect of exogenous variables on endogenous variable is also strengthened or weakened by moderating variable. In SEM, there are two approaches in analyzing moderation variables, namely the interaction method and the multigroup method. This article aims to compare the mediation effect on interaction approaches and multigroup approaches in Structural Equation Modeling. The data used is the case of timeliness of Bank X mortgage payments. In this article, statistical methods are evaluated to compare indirect effect between groups and examine indirect effect on each group. It was concluded that Collectability Status moderates the indirect relationship between Capital and the Timeliness of Payment through Willingness to Pay. Debtors with current collectability status more strongly effect the Timeliness of Payment than debtors with incurrect collectability status. Theresults of testing indirect effects on moderation with interaction and multigroup approaches are not much different. In the multigroup approach, the bootstrap interval bias is smaller than the bootstrap interval bias in the interaction approach. The Q-square Predictive Relevance value in both methods is quite high, indicating that the model is good. On the Current Collectibility Status group Q^2 is 89.3%, in the incurrect Collectibility Status Q^2 is 84.2%. While in the interaction approach, Q^2 is 70.4%. Researcher recommend a multigroup approach to data that has categorical moderation variables because differences between groups can be directly observed without adding interaction variables in the model.”

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Kinematic differences between multiple populations in Galactic globular clusters

Cited by 9 publications

References 74 publications

NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data

NGC 1856: Using Machine Learning Techniques to Uncover Detailed Stellar Abundances from MUSE Data

Rubin Observatory LSST Stars Milky Way and Local Volume Star Clusters Roadmap

Comparison of Mediation Effects on Interaction and Multigroup Approach in Structural Equation Modeling PLS in Case of Bank Mortgage

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