Cosmic structure formation is hierarchical in the Lambda cold dark matter cosmology. Therefore, merger is an inevitable and crucial physical process. On galactic scales, mergers also impact on many key aspects of galaxy evolution, such as mass assembly, star formation, chemistry, morphology, and active galactic nucleus (AGN) activity.
Despite the widely accepted importance of galaxy interactions, quantifying the impact of mergers has been problematic and much controversy still exists. Over the coming years, the combination of deep and wide high-resolution imaging surveys and the great strides made in image classification using machine learning means that major breakthroughs can be made. On the theoretical front, in recent years it has become possible to make cosmological hydrodynamical simulations with increasingly more realistic galaxy populations. These simulations allow us to make consistent and quantitative comparisons between observations and theory in a cosmological context, which is not only crucial for properly interpreting observations but also providing insights and feedback into our theory.
In summary, galaxy mergers touch on a wide range of topics within the study of galaxies. It is also one of the fields most evidently benefitting from the growing application of machine learning in astronomy, at a time when data volumes are exploding. This workshop is a timely opportunity to bring these varied specialties together.
This workshop aims to address the following goals under three themes:
1. Merger identification - What is the optimal or most effective way of identifying mergers at various masses and cosmic epochs (machine learning vs visual classification vs morphological statistics)?
2. Star formation and the interstellar medium (ISM) - How is the physics of star formation and the ISM different in mergers versus non-mergers?
3. AGN triggering and feedback - What is the role of mergers in triggering black hole growth, feedback, and morphological transformation?