Journals / Turkish Journal of Astronomy and Astrophysics / 2022 / Cilt: 3 - Sayı: 3

Detailed Analysis of Selected Open Clusters Discovered by Machine Learning Method

Makine Öğrenmesi Yöntemiyle Keşfedilen Seçilmiş Açık Kümelerin Detaylı Analizi

Pages
1–5
Publication Date
2022-12-31
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Beginning from stars with cluster membership probability $P\geq 0.5$, the extinction (${A}_{\rm G}$; 0.12, 0.10, 0.35, 0.10 magnitude), distances ($d$; 576, 450, 690, 645) of open clusters UPK 51, UPK 350, UPK 579 and UPK 599, respectively. parsec), metallicity ($Z$; 0.015, 0.005, 0.008, 0.010) and ages ($\log t$; 8.20, 8.50, 8.60, 7.30 dex) were determined.

📌 3 Key Takeaways

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Objective & Hypothesis:In this study astrometric and astrophysical parameters were determined using Gaia and 2MASS data of UPK 51, UPK 350, UPK 579 and UPK 599 open clusters, which were discovered by machine learning method starting from the astrometric and photometric data in the Gaia Early Data Release 3 (Gaia EDR3).
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Key Findings & Data:In the study, the cluster membership probabilities were calculated using the Gaussian Mixture Model (GMM) for the proper motion components and trigonometric parallaxes of the stars in the open cluster direction.
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Conclusion & Impact:Beginning from stars with cluster membership probability $P\geq 0.5$, the extinction (${A}_{\rm G}$; 0.12, 0.10, 0.35, 0.10 magnitude), distances ($d$; 576, 450, 690, 645) of open clusters UPK 51, UPK 350, UPK 579 and UPK 599, respectively. parsec), metallicity ($Z$; 0.015, 0.005, 0.008, 0.010) and ages ($\log t$; 8.20, 8.50, 8.60, 7.30 dex) were determined.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Open Clusters: UPK51 UPK350 UPK579 UPK599 · Stars: H-R Diagram · Milky Way Galaxy

Abstract

In this study astrometric and astrophysical parameters were determined using Gaia and 2MASS data of UPK 51, UPK 350, UPK 579 and UPK 599 open clusters, which were discovered by machine learning method starting from the astrometric and photometric data in the Gaia Early Data Release 3 (Gaia EDR3). In the study, the cluster membership probabilities were calculated using the Gaussian Mixture Model (GMM) for the proper motion components and trigonometric parallaxes of the stars in the open cluster direction. Beginning from stars with cluster membership probability $P\geq 0.5$, the extinction (${A}_{\rm G}$; 0.12, 0.10, 0.35, 0.10 magnitude), distances ($d$; 576, 450, 690, 645) of open clusters UPK 51, UPK 350, UPK 579 and UPK 599, respectively. parsec), metallicity ($Z$; 0.015, 0.005, 0.008, 0.010) and ages ($\log t$; 8.20, 8.50, 8.60, 7.30 dex) were determined.

Özet

Bu çaılşmada Gaia uydusunun erken üçüncü veri sürümü (Gaia EDR3) kataloğundaki astrometrik ve fotometrik verilerinden itibaren makine öğrenmesi yöntemiyle keşfedilen ve literatürde detaylı çalışması bulunmayan UPK 51, UPK 350, UPK 579 ve UPK 599 açık kümelerinin Gaia EDR3 ve 2MASS verileri kullanılarak astrometrik ve astrofizik parametreleri tayin edilmiştir. Çalışmada, küme doğrultusundaki yıldızların öz hareket bileşenleri ve trigonometrik paralaksları Gaussian Mixture Modeli (GMM) kullanılarak kümeye üyelik olasılıkları hesaplanmıştır. Kümeye üyelikleri P ≥ 0.5 olan yıldızlardan itibaren UPK 51, UPK 350, UPK 579 ve UPK 599 açık kümelerinin, sırasıyla, sönümlemeleri (AG; 0.12, 0.10, 0.35, 0.10), uzaklıkları (d; 576, 450, 690, 645), ağır element bollukları (Z; 0.015, 0.005, 0.008, 0.010) ve yaşları (log t; 8.20, 8.50, 8.60, 7.30) tayin edilmiştir.

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Cite This Article

AKBULUT, B., ÜLGEN, E. K., AK, T. (2022). Detailed Analysis of Selected Open Clusters Discovered by Machine Learning Method. Turkish Journal of Astronomy and Astrophysics, Cilt: 3 - Sayı: 3, 1–5. https://ajindex.com/articles/870933

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References (1)

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