pyUPMASK: an improved unsupervised clustering algorithm
| Autor: | G. I. Perren, M. S. Pera, H. D. Navone, André Moitinho, Ruben Angel Vazquez |
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| Rok vydání: | 2021 |
| Předmět: |
STATISTICAL [METHODS]
Binary number FOS: Physical sciences 01 natural sciences Field (computer science) purl.org/becyt/ford/1 [https] 0103 physical sciences Code (cryptography) DATA ANALYSIS [METHODS] 010306 general physics 010303 astronomy & astrophysics computer.programming_language Physics Probabilistic classification Measure (data warehouse) Astronomy and Astrophysics purl.org/becyt/ford/1.3 [https] Python (programming language) Astrophysics - Astrophysics of Galaxies Space and Planetary Science GENERAL [OPEN CLUSTERS AND ASSOCIATIONS] Astrophysics of Galaxies (astro-ph.GA) Key (cryptography) computer Performance metric Algorithm INDIVIDUAL: NGC 2516 [OPEN CLUSTERS AND ASSOCIATIONS] |
| Zdroj: | CONICET Digital (CONICET) Consejo Nacional de Investigaciones Científicas y Técnicas instacron:CONICET |
| DOI: | 10.48550/arxiv.2101.01660 |
| Popis: | Aims. We present pyUPMASK, an unsupervised clustering method for stellar clusters that builds upon the original UPMASK package. The general approach of this method makes it plausible to be applied to analyses that deal with binary classes of any kind as long as the fundamental hypotheses are met. The code is written entirely in Python and is made available through a public repository. Methods. The core of the algorithm follows the method developed in UPMASK but introduces several key enhancements. These enhancements not only make pyUPMASK more general, they also improve its performance considerably. Results. We thoroughly tested the performance of pyUPMASK on 600 synthetic clusters affected by varying degrees of contamination by field stars. To assess the performance, we employed six different statistical metrics that measure the accuracy of probabilistic classification. Conclusions. Our results show that pyUPMASK is better performant than UPMASK for every statistical performance metric, while still managing to be many times faster. Fil: Pera, María Sol. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; Argentina Fil: Perren, Gabriel Ignacio. Instituto de Astrofísica de la Plata (conicet- Universidad Nacional de la Plata); Argentina Fil: Moitinho, A.. Instituto Superior Tecnico; Portugal Fil: Navone, Hugo Daniel. Universidad Nacional de Rosario; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Física de Rosario. Universidad Nacional de Rosario. Instituto de Física de Rosario; Argentina Fil: Vazquez, Ruben Angel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; Argentina |
| Databáze: | OpenAIRE |
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