L-Galaxies, Munich Galaxy Formation Model

The Physics of Quenching

Project: The Physics behind quenching

A striking feature of the galaxy population in the local Universe is the large fraction of objects with negligible on-going star formation. In recent years, passive galaxies have been observed out to z=2, when the Universe was only 1/4 of its current age, with a tendency for quenching to happen predominately in objects that are surrounded by quenched companions, for objects with large masses and in denser environments. Theoretically, the energy released by central supermassive black holes and the impact of group forces on satellites offer potential explanations for these effects. However the details of these processes remain largely unknown, with any attempt to establish a direct relation between causes and effects being hampered by the strong correlations between numerous galaxy properties.

By combining a state-of-the-art model of galaxy formation, some of the most detailed observations of galaxies at multiple stages of the evolution of the Universe and advanced tools for statistical analysis of large datasets, we aim at gaining insight into the physical mechanisms responsible for one of the most fundamental trends in galaxy evolution.

Projects to be developed by Ben Hoyle and Bruno Henriques


With the advent of large sky surveys (e.g.: SDSS, 2DFGRS, COSMOS, ZCOSMOS, GOODS, DEEP, UKIDSS, ULTRAVISTA), observations have been able to robustly characterise the observed galaxy population out to at least z=2. A critical aspect of the observed galaxy evolution was the quenching of star formation in a significant fraction of the population. ETH had a paramount role in these discoveries, giving decisive contributions to unveil the rate and conditions at which galaxies stop forming stars (Baldry et al. 2006; Peng et al. 2010, 2012; Carollo et al. 2013; Wetzel et al. 2013). Quenching has been observed to happen predominantly in massive galaxies, in high density environments, for larger bulge masses, larger halo masses and larger velocity dispersions. In addition passive and active galaxies have been observed to preferentially cluster together, with passive central galaxies hosting predominantly passive satellites and vice-versa (an effect called conformity).

The main goal of this project is to better understand the physics responsible for the aforementioned observed trends. We will extend the analysis of fundamental statistics of quenching in the observed galaxy population and used them to constrain galaxy formation models. The joint study will be used to to discriminate between the importance of different physical mechanisms in shaping global properties of the galaxy population.

In doing so, this project will combine unique ETH expertise and open up new possibilities in galaxy formation studies. The in-depth knowledge about observational data sets will be combined with a privileged access to one of the most widely use simulations of galaxy formation: the L-Galaxies model of galaxy formation and evolution (latest version: Henriques et al. 2015). This will grant students access to the latest developments on the code being produced by a group of more than 10 people. As part of the software available, there is an MCMC sampling package which allows full exploration of large parameter spaces giving us a powerful tool to understand the complex inter-connection between the processes involved in galaxy formation.

  1. Learn the concepts of PCA and machine learning:
  2. The first main goal of this project is to develop the adequate tools in order to identify trends in data using principal component analysis and machine learning. These will be used to:

    • Revisit the Knobel et al. 2014 analysis and try to determine how many independent quenching trends are and exactly where and when it happens in observations.
    • Define a set of fundamental characteristics that fully describe observed relations.
    • Use this study to construct a new set of constraints for theoretical models and to pinpoint possible weaknesses of observational analysis (e.g. in terms of purity and completeness of the available data sets).

  3. Identify quenching mechanisms:
  4. The trends identified as the fundamental properties of the observed population will be used as constraints for galaxy formation theory in the second phase of this project. This will be divided into the following objectives:

    • Test quenching mechanisms currently present in simulations of galaxy formation in terms of global trends.
    • Analyse the details of different quenching mechanisms identifying potential problems. Do the two separate mechanisms, AGN and environment, fully explain the observational trends?
    • Characterise how these mechanisms interplay to produce observed properties and how this results in observed correlations.
    • Use the acquired knowledge to develop models that better represent the observable Universe.

  5. Find the connection with the underlying large scale distribution of dark matter:
  6. The last phase of this project will focus on using machine learning to find additional relations in galaxy formation models, namely to connect queching mechanisms to the underlying large scale distribution of dark matter. Key points to be addressed include:

    • Determine the conditions responsible for black hole growth and AGN feedback in terms of local density and the overall impact of direct accretion and mergers.
    • Characterise environment as a function of cosmic time and identify the relative impact of different processes in removing the fuel for star formation.
    • Analyse the origins of conformity and its spatial distribution, understanding how it relates to the hot gas content of galaxies.
    • Explore the role of cold flows vs hot accretion in feeding central black holes, stripping satellites and on the global quenching picture.