Galaxy formation and evolution

Abstract:

Wide-area imaging surveys are one of the key ways of advancing our understanding of cosmology, galaxy formation physics, and the large-scale structure of the Universe in the coming years. These surveys typically require calculating redshifts for huge numbers (hundreds of millions to billions) of galaxies – almost all of which must be derived from photometry rather than spectroscopy. In this paper, we investigate how using statistical models to understand the populations that make up the colour–magnitude distribution of galaxies can be combined with machine learning photometric redshift codes to improve redshift estimates. In particular, we combine the use of Gaussian mixture models with the high-performing machine-learning photo-z algorithm GPz and show that modelling and accounting for the different colour–magnitude distributions of training and test data separately can give improved redshift estimates, reduce the bias on estimates by up to a half, and speed up the run-time of the algorithm. These methods are illustrated using data from deep optical and near-infrared data in two separate deep fields, where training and test data of different colour–magnitude distributions are constructed from the galaxies with known spectroscopic redshifts, derived from several heterogeneous surveys.

Abstract:

ABSTRACT We study how star formation is regulated in low-mass field dwarf galaxies ($10^5 \le M_{\star } \le 10^6 \, \mbox{M}_\mathrm{\odot }$), using cosmological high-resolution ($3 \, \mathrm{pc}$) hydrodynamical simulations. Cosmic reionization quenches star formation in all our simulated dwarfs, but three galaxies with final dynamical masses of $3 \times 10^{9} \, \mbox{M}_\mathrm{\odot }$ are subsequently able to replenish their interstellar medium by slowly accreting gas. Two of these galaxies reignite and sustain star formation until the present day at an average rate of $10^{-5} \, \mbox{M}_\mathrm{\odot } \, \text{yr}^{-1}$, highly reminiscent of observed low-mass star-forming dwarf irregulars such as Leo T. The resumption of star formation is delayed by several billion years due to residual feedback from stellar winds and Type Ia supernovae; even at z = 0, the third galaxy remains in a temporary equilibrium with a large gas content but without any ongoing star formation. Using the ‘genetic modification’ approach, we create an alternative mass growth history for this gas-rich quiescent dwarf and show how a small $(0.2\, \mathrm{dex})$ increase in dynamical mass can overcome residual stellar feedback, reigniting star formation. The interaction between feedback and mass build-up produces a diversity in the stellar ages and gas content of low-mass dwarfs, which will be probed by combining next-generation H i and imaging surveys.