Using self-organizing maps to identify potential halo white dwarfs.

Area of Science:

• * Astronomy and Astrophysics
• * Computational Astrophysics

Background:

• * White dwarfs are stellar remnants crucial for understanding galactic evolution.
• * Distinguishing between disk and halo white dwarf populations is key to galactic structure studies.

Purpose of the Study:

• * To classify white dwarf populations in the solar neighborhood using an unsupervised approach.
• * To develop a framework for analyzing future large white dwarf datasets.
• * To investigate the astrophysical implications of white dwarf populations for dark matter.

Main Methods:

• * Merged Monte Carlo (MC) simulations of white dwarf populations with observational catalogs.
• * Employed a competitive learning algorithm, specifically a self-organized map (SOM), for unsupervised classification.
• * Utilized MC simulated stars as tracers to identify clusters within the composite catalog.

Main Results:

• * Achieved satisfactory classification of disk and halo white dwarf populations.
• * Demonstrated the efficacy of the MC simulation and SOM approach for analyzing large white dwarf datasets.
• * Identified potential astrophysical implications, including the contribution of halo white dwarfs to baryonic dark matter.

Conclusions:

• * The developed methodology provides a robust framework for future white dwarf population studies.
• * The findings support the hypothesis that old, dim halo white dwarfs could constitute a fraction of galactic baryonic dark matter.
• * This research has significant implications for understanding galactic composition and dark matter content.