Closing remarks and Outlook

TL;DR

This paper summarizes key highlights from the IAU Symposium 334, discussing advances in understanding the Milky Way's formation through observations, simulations, and data analysis techniques like deep learning.

Contribution

It provides an overview of recent progress and future prospects in Galactic astronomy, emphasizing new data and methods such as asteroseismology, Gaia measurements, and deep learning applications.

Findings

Identification of first stars fossil records in the halo.

Presentation of cosmological simulations for Milky Way formation.

Advancements in stellar age determination and data analysis methods.

Abstract

Some highlights are given of the IAU Symposium 334, Rediscovering our Galaxy, held in Potsdam, in July 2017: from the first stars fossil records found in the halo, the carbon-enhanced metal poor CEMP-no, to the cosmological simulations presenting possible scenarios for the Milky Way formation, passing through the chemo-dynamical models of the various components, thin and thick disks, box/peanut bulge, halo, etc. The domain is experiencing (or will be in the near future) huge improvements with precise and accurate stellar ages, provided by astero-seismology, precise stellar distances and kinematics (parallaxes and proper motions from GAIA), and the big data resulting from large surveys are treated with deep learning algorithms.