ترجمه فارسی مقاله در جستجوی ساختارهای شیمیایی سینماتیک در هاله راه شیری با الگوریتم های خوشه بندی عمیق

• کتاب صدها نکته فارسی (خودمونی) – نسخه PDF — زبان ساده و کاربردی
  مشاهده نمونه نسخه نکات ساده
• کتاب صدها نکته رسمی فارسی – نسخه PDF — نگارش استاندارد و علمی
  مشاهده نمونه نسخه نکات رسمی
• کتاب صدها پرسش و پاسخ تشریحی – نسخه PDF
  — هر سؤال همراه با پاسخ کامل برای درک عمیق مفاهیم
  مشاهده نمونه نسخه پرسش و پاسخ
• کتاب پرسش و پاسخ چهارگزینه‌ای – نسخه یادگیری سریع
  — پاسخ‌ها بلافاصله بعد از سؤال برای مرور سریع
  مشاهده نمونه نسخه کوییز سریع
• کتاب پرسش و پاسخ چهارگزینه‌ای – نسخه خودآزمایی
  — پاسخ‌ها در انتهای بخش‌ها برای سنجش واقعی یادگیری
  مشاهده نمونه نسخه آزمونی

🎯 این بسته یک دورهٔ آموزشی کامل و چندلایه است؛ شامل ویدیوهای آموزشی، کتاب‌ها، تمرین‌ها و خودآزمایی.

چکیده

According to the lambda CDM scenario, galaxies are formed through the hierarchical accretion of building blocks. Our Galaxy is a privileged place to look for the remnants of accretion events through the study of the chemical and kinematic properties of its halo stellar populations. Due to its low density, the stellar halo holds the most favorable conditions for chemical tagging. However, chemical tagging alone often yields weak results due to both uncertainties in chemical abundances and to overlapping chemical properties among different populations. To overcome this problem, the use of chemical and kinematic properties can be combined. In this Thesis, we developed a machine learning algorithm, named the CREEK, which combines orbital and chemical properties of halo stars observed by two large public spectroscopic surveys, Gaia-ESO and APOGEE. The CREEK operates as follows: 1)Data selection: We selected halo stars from the APOGEE and Gaia-ESO surveys based both on their velocity and metallicity and we computed their orbital parameters. 2)Using kinematics: The selected data were passed to a Siamese Neural Network that established links between stars based on their kinematic similarities. 3)Using chemistry: The graph was passed through a Graph Neural Network (GNN) auto-encoder that took as input the selected abundances. The abundances were chosen to maximize homogeneity within stars from the same cluster while ensuring distinctiveness between stars from different clusters. Additionally, we prioritised elements with smallest errors. The GNN auto-encoder computed a mean of the abundances of all connected stars, weighted on the number of links of each star and mapped the chemical space into a more efficient representation in the latent space. 4)Recovering structures: Finally, OPTICS was applied to the latent space, providing groups based on the chemical similarities of the stars.

چکیده به فارسی (ترجمه ماشینی)