Context. We present the new release of the Padova N-body code for cosmological simulations of galaxy formation and evolution, EvoL. The basic Tree + SPH code is presented and analysed, together with an overview of the software architectures. Aims. EvoL is a flexible parallel Fortran95 code, specifically designed for simulations of cosmological structure formations on cluster, galactic and sub-galactic scales. Methods. EvoL is a fully Lagrangian self-adaptive code, based on the classical oct-tree by Barnes & Hut (1986, Nature, 324, 446) and on the smoothed particle hydrodynamics algorithm (SPH, Lucy 1977, AJ, 82, 1013. It includes special features like adaptive softening lengths with correcting extra-terms, and modern formulations of SPH and artificial viscosity. It is designed to be run in parallel on multiple CPUs to optimise the performance and save computational time. Results. We describe the code in detail, and present the results of a number of standard hydrodynamical tests.

We investigate the influence of the initial overdensities and masses of proto‐galaxies on their subsequent evolution (the star formation history in particular) to understand whether these key parameters are sufficient to account for the varied properties of the galactic populations. By means of fully hydrodynamical N‐body simulations performed with the code EvoL, we produce 12 self‐similar models of early‐type galaxies of different initial masses and overdensities, and follow their evolution from the early epochs (detachment from the linear regime and Hubble flow at z ≥ 20) down to the stage when mass assembly is complete, i.e. z ≤ 1 (in some cases the models are calculated up to z = 0). The simulations include radiative cooling, star formation, stellar energy feedback, re‐ionizing photo‐heating background and chemical enrichment of the interstellar medium; we do not consider the possible presence of active nuclei. We find a strong correlation between the initial properties of the proto‐haloes and their subsequent star formation histories. Massive (Mtot ≃ 1013 M⊙) haloes experience a single, intense burst of star formation (with rates ≥103 M⊙ yr−1) at early epochs, consistently with observations, with less pronounced dependence on the initial overdensity; intermediate‐mass (Mtot ≃ 1011 M⊙) haloes have histories that strongly depend on their initial overdensity, whereas low‐mass haloes (Mtot ≃ 109 M⊙) always have erratic, bursting like star‐forming histories, due to the ‘galactic breathing’ phenomenon. The model galaxies have morphological, structural and chemical properties resembling those of real galaxies, even though some disagreement still occurs, likely a consequence of some numerical choices. We conclude that total mass and initial overdensity drive the star formation histories of early‐type galaxies. The model galaxies belong to the so‐called quasi‐monolithic (or early hierarchical) scenario in the sense that the aggregation of lumps of dark and baryonic matter is completed very early on in their history. In this picture, nature seems to play the dominant role, whereas nurture has a secondary importance.

We present robo, a model and its companion code for the study of the interstellar medium (ISM). The aim is to provide an accurate description of the physical evolution of the ISM and to set the ground for an ancillary tool to be inserted in NBody-Tree-SPH (NB-TSPH) simulations of large-scale structures in the cosmological context or of the formation and evolution of individual galaxies. The ISM model consists of gas and dust. The gas chemical composition is regulated by a network of reactions that includes a large number of species (hydrogen and deuterium-based molecules, helium, and metals). New reaction rates for the charge transfer in H + and H 2 collisions are presented. The dust contains the standard mixture of carbonaceous grains (graphite grains and PAHs) and silicates. In our model dust are formed and destroyed by several processes. The model accurately treats the cooling process, based on several physical mechanisms, and cooling functions recently reported in the literature. The model is applied to a wide range of the input parameters, and the results for important quantities describing the physical state of the gas and dust are presented. The results are organized in a database suited to the artificial neural networks (ANNs). Once trained, the ANNs yield the same results obtained by ROBO with great accuracy. We plan to develop ANNs suitably tailored for applications to NB-TSPH simulations of cosmological structures and/or galaxies.