Galaxy formation and evolution

Abstract:

While it is well established that supermassive black holes (SMBHs) coevolve with their host galaxy, it is currently less clear how lower-mass black holes, so-called intermediate-mass black holes (IMBHs), evolve within their dwarf galaxy hosts. In this paper, we present results on the evolution of a large sample of IMBHs from the NEWHORIZON zoom volume, which has a radius of 10 comoving Mpc. We show that occupation fractions of IMBHs in dwarf galaxies are at least 50 per cent for galaxies with stellar masses down to 106 M☉, but BH growth is very limited in dwarf galaxies. In NEWHORIZON, IMBHs growth is somewhat more efficient at high redshift z = 3 but in general, IMBHs do not grow significantly until their host galaxy leaves the dwarf regime. As a result, NEWHORIZON underpredicts observed AGN luminosity function and AGN fractions. We show that the difficulties of IMBHs to remain attached to the centres of their host galaxies plays an important role in limiting their mass growth, and that this dynamic evolution away from galactic centres becomes stronger at lower redshift.

Abstract:

Symbolic Regression (SR) algorithms attempt to learn analytic expressions which fit data accurately and in a highly interpretable manner. Conventional SR suffers from two fundamental issues which we address here. First, these methods search the space stochastically (typically using genetic programming) and hence do not necessarily find the best function. Second, the criteria used to select the equation optimally balancing accuracy with simplicity have been variable and subjective. To address these issues we introduce Exhaustive Symbolic Regression (ESR), which systematically and efficiently considers all possible equations—made with a given basis set of operators and up to a specified maximum complexity— and is therefore guaranteed to find the true optimum (if parameters are perfectly optimised) and a complete function ranking subject to these constraints. We implement the minimum description length principle as a rigorous method for combining these preferences into a single objective. To illustrate the power of ESR we apply it to a catalogue of cosmic chronometers and the Pantheon+ sample of supernovae to learn the Hubble rate as a function of redshift, finding 40 functions (out of 5.2 million trial functions) that fit the data more economically than the Friedmann equation. These low-redshift data therefore do not uniquely prefer the expansion history of the standard model of cosmology. We make our code and full equation sets publicly available.