ERGO-ML I: Inferring the assembly histories of IllustrisTNG galaxies
  from integral observable properties via invertible neural networks
  A fundamental prediction of the LambdaCDM cosmology is the hierarchical
build-up of structure and therefore the successive merging of galaxies into
more massive ones. As one can only observe galaxies at one specific time in
cosmic history, this merger history remains in principle unobservable. By using
the TNG100 simulation of the IllustrisTNG project, we show that it is possible
to infer the unobservable stellar assembly and merger history of central
galaxies from their observable properties by using machine learning techniques.
In particular, in this first paper of ERGO-ML (Extracting Reality from Galaxy
Observables with Machine Learning), we choose a set of 7 observable integral
properties of galaxies (i.e. total stellar mass, redshift, color, stellar size,
morphology, metallicity, and age) to infer, from those, the stellar ex-situ
fraction, the average merger lookback times and mass ratios, and the lookback
time and stellar mass of the last major merger. To do so, we use and compare a
Multilayer Perceptron Neural Network and a conditional Invertible Neural
Network (cINN): thanks to the latter we are also able to infer the posterior
distribution for these parameters and hence estimate the uncertainties in the
predictions. We find that the stellar ex-situ fraction and the time of the last
major merger are well determined by the selected set of observables, that the
mass-weighted merger mass ratio is unconstrained, and that, beyond stellar
mass, stellar morphology and stellar age are the most informative properties.
Finally, we show that the cINN recovers the remaining unexplained scatter and
secondary cross-correlations. Our tools can be applied to large galaxy surveys
in order to infer unobservable properties of galaxies' past, enabling empirical
studies of galaxy evolution enriched by cosmological simulations.
