New insights into mechanisms that govern the formation of stars in galaxies have been gleaned by Peter Behroozi at the University of Arizona and colleagues in the US. This was done using the team’s UniverseMachine simulation framework, which generates millions of mock universes that evolve according to different rules of star formation. By comparing these universes to observations of the real thing, the scientists can work out which rules are correct.

Many of the mechanisms underlying galaxy formation and evolution remain shrouded in mystery. Astronomers now widely believe that important mechanisms are governed by the characteristics of dark matter haloes – huge gravitationally-bound structures that are believed to envelop galaxies including the Milky Way. While this framework has provided some important insights into galaxy formation, no theories yet exist for explaining how galaxies form and evolve from first principles. To uncover the relevant processes, astronomers use two types of model: semi-analytic models, which incorporate known physics; and empirical models with constraints based on astronomical observations.

As the two types of model have improved over the years, they have yielded increasingly similar results. There are still significant disagreements, however, particularly those related to processes that inhibit star formation in galaxies. Semi-analytic models predict that in older galaxies, the energy radiated from bright objects such as supernovae and supermassive black holes heat interstellar hydrogen, preventing it from collapsing under gravity to form new stars. Observations, however, show that star formation rates (SFRs) in such galaxies are generally far higher than heating would allow.

Mysterious correlation

To mimic these astronomical observations, empirical models incorporate a theoretical correlation between the SFR in a galaxy and properties of its dark matter halo. In their study, Behroozi’s team aimed to determine the physics mechanisms underlying this correlation using a simulation.

Their model is dubbed UniverseMachine and it first generates a “mock universe” of 12 million galaxies. An educated guess is made at how SFRs might depend on halo mass, assembly history, and galaxy age. After running the simulation from the early universe to the present day, an algorithm compares the resulting SFRs to observations. This comparison is then used to determine more accurate input values for the next mock universe. This cycle was then repeated, until the full range of SFRs, as measured in real observations, had been sampled.

Following two weeks of calculation on the University of Arizona’s Ocelote supercomputer, during which over 8 million mock universes were generated, Behroozi and colleagues uncovered a variety of new insights into star formation mechanisms. Among other discoveries, they concluded that the correlation between star formation and halo properties is indeed strong, but not perfect. In addition, the average fraction of interstellar hydrogen not forming stars decreases with age, which suggests that the heating of the gas is not solely responsible for preventing star formation.

These discoveries could now allow astronomers to draw new theories about the properties of star formation, enabling them to update semi-analytic models. In the future, Behroozi’s team now hope to expand UniverseMachine to explore other aspects of galaxy formation and evolution, including the diverse morphologies of individual galaxies.

The research is described in Monthly Notices of the Royal Astronomical Society.