Monolithic or hierarchical star formation? A new statistical analysis
Marios Kampakoglou, Roberto Trotta, Joe Silk (Oxford Astrophysics)

TL;DR
This study compares hierarchical and monolithic galaxy formation models using a Bayesian approach, finding hierarchical models fit observations better but both scenarios require specific feedback mechanisms and cannot be conclusively distinguished yet.
Contribution
It introduces a new statistical framework incorporating systematics to compare galaxy formation scenarios with observational data.
Findings
Hierarchical models fit data better with high supernova outflow efficiency.
Monolithic models need metal-poor winds and alternative reionization mechanisms.
Dust correction schemes critically influence the favored formation scenario.
Abstract
We consider an analytic model of cosmic star formation which incorporates supernova feedback, gas accretion and enriched outflows, reproducing the history of cosmic star formation, metallicity, supernovae type II rates and the fraction of baryons allocated to structures. We present a new statistical treatment of the available observational data on the star formation rate and metallicity that accounts for the presence of possible systematics. We then employ a Bayesian Markov Chain Monte Carlo method to compare the predictions of our model with observations and derive constraints on the 7 free parameters of the model. We find that the dust correction scheme one chooses to adopt for the star formation data is critical in determining which scenario is favoured between a hierarchical star formation model, where star formation is prolonged by accretion, infall and merging, and a monolithic…
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