Statistical Classification and an Optimized Red-Sequence Technique for the Determination of Galaxy Clusters
This study presents a novel method for characterizing galaxy clusters by integrating statistical classification techniques with an optimized adaptation of the red sequence approach. The proposed algorithm employs Gaussian mixture models to analyze the distribution of three key variables: <i>r&...
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MDPI AG
2025-05-01
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| author | Dagoberto R. Mares-Rincón Josué J. Trejo-Alonso José A. Guerrero-Díaz-de-León Jorge E. Macías-Díaz |
| author_facet | Dagoberto R. Mares-Rincón Josué J. Trejo-Alonso José A. Guerrero-Díaz-de-León Jorge E. Macías-Díaz |
| author_sort | Dagoberto R. Mares-Rincón |
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| description | This study presents a novel method for characterizing galaxy clusters by integrating statistical classification techniques with an optimized adaptation of the red sequence approach. The proposed algorithm employs Gaussian mixture models to analyze the distribution of three key variables: <i>r</i> magnitude, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>g</mi><mrow><mo>–</mo></mrow><mi>r</mi></mrow></semantics></math></inline-formula> color index, and redshift <i>z</i>. To enhance cluster discrimination, we incorporate Mahalanobis distance metrics and modify the conventional red sequence technique by adopting the principal eigenvector as the slope of the cluster. A sample of 114 galaxy groups and clusters within the redshift range <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.002</mn><mo><</mo><mi>z</mi><mo><</mo><mn>0.45</mn></mrow></semantics></math></inline-formula> was used to validate the method. Comparative analyses demonstrate that the proposed approach achieves comparable or, in certain cases, superior performance in cluster characterization relative to the standard red sequence technique. These results highlight the algorithm’s potential as a robust tool for the exploratory identification and initial parameter determination of galaxy clusters, particularly in large-scale surveys. The methodology bridges statistical rigor with established astrophysical techniques, offering a promising avenue for advancing cluster detection in observational cosmology. |
| format | Article |
| id | doaj-art-eb5f40489a164c71b499f46fba5b055e |
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| issn | 2075-4434 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Galaxies |
| spelling | doaj-art-eb5f40489a164c71b499f46fba5b055e2025-08-20T02:21:11ZengMDPI AGGalaxies2075-44342025-05-011335210.3390/galaxies13030052Statistical Classification and an Optimized Red-Sequence Technique for the Determination of Galaxy ClustersDagoberto R. Mares-Rincón0Josué J. Trejo-Alonso1José A. Guerrero-Díaz-de-León2Jorge E. Macías-Díaz3Faculty of Sciences, Autonomous University of Aguascalientes, Avenida Universidad 940, Ciudad Universitaria, Aguascalientes 20100, MexicoFaculty of Engineering, Autonomous University of Queretaro, Apartado Postal 3-72, Queretaro 58090, MexicoDepartment of Statistics, Autonomous University of Aguascalientes, Aguascalientes 20100, MexicoDepartment of Mathematics and Didactics of Mathematics, School of Digital Sciences and Technologies, Tallinn University, 10120 Tallinn, EstoniaThis study presents a novel method for characterizing galaxy clusters by integrating statistical classification techniques with an optimized adaptation of the red sequence approach. The proposed algorithm employs Gaussian mixture models to analyze the distribution of three key variables: <i>r</i> magnitude, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>g</mi><mrow><mo>–</mo></mrow><mi>r</mi></mrow></semantics></math></inline-formula> color index, and redshift <i>z</i>. To enhance cluster discrimination, we incorporate Mahalanobis distance metrics and modify the conventional red sequence technique by adopting the principal eigenvector as the slope of the cluster. A sample of 114 galaxy groups and clusters within the redshift range <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.002</mn><mo><</mo><mi>z</mi><mo><</mo><mn>0.45</mn></mrow></semantics></math></inline-formula> was used to validate the method. Comparative analyses demonstrate that the proposed approach achieves comparable or, in certain cases, superior performance in cluster characterization relative to the standard red sequence technique. These results highlight the algorithm’s potential as a robust tool for the exploratory identification and initial parameter determination of galaxy clusters, particularly in large-scale surveys. The methodology bridges statistical rigor with established astrophysical techniques, offering a promising avenue for advancing cluster detection in observational cosmology.https://www.mdpi.com/2075-4434/13/3/52galaxy clustersstatistical classificationGaussian mixture modelsMahalanobis distancered sequence techniqueprincipal eigenvector |
| spellingShingle | Dagoberto R. Mares-Rincón Josué J. Trejo-Alonso José A. Guerrero-Díaz-de-León Jorge E. Macías-Díaz Statistical Classification and an Optimized Red-Sequence Technique for the Determination of Galaxy Clusters Galaxies galaxy clusters statistical classification Gaussian mixture models Mahalanobis distance red sequence technique principal eigenvector |
| title | Statistical Classification and an Optimized Red-Sequence Technique for the Determination of Galaxy Clusters |
| title_full | Statistical Classification and an Optimized Red-Sequence Technique for the Determination of Galaxy Clusters |
| title_fullStr | Statistical Classification and an Optimized Red-Sequence Technique for the Determination of Galaxy Clusters |
| title_full_unstemmed | Statistical Classification and an Optimized Red-Sequence Technique for the Determination of Galaxy Clusters |
| title_short | Statistical Classification and an Optimized Red-Sequence Technique for the Determination of Galaxy Clusters |
| title_sort | statistical classification and an optimized red sequence technique for the determination of galaxy clusters |
| topic | galaxy clusters statistical classification Gaussian mixture models Mahalanobis distance red sequence technique principal eigenvector |
| url | https://www.mdpi.com/2075-4434/13/3/52 |
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