From short-lived to long-lived clouds: impact of star formation models on giant molecular cloud evolution in simulations of an NGC 300-like galaxy
Multi-wavelength observations of molecular and ionized gas indicate that GMCs are short-lived, generally dispersing within one or two dynamical timescales. To investigate the physical origin of these short lifetimes and the role of star formation prescriptions, we conduct radiation-hydrodynamic simulations of an NGC 300-like disk galaxy with RAMSES-RT. We compare two distinct star formation models, one based on a local gravo-thermo-turbulent (GTT) condition and the other employing sink particles, to examine how star formation and feedback collectively regulate GMC evolution. The sink-particle-based model yields bursty yet self-regulated global star formation rates of $0.1$-$0.5$ $M_{\odot}\,yr^{-1}$ and produces GMC lifetimes of $\sim20$-$30$ Myr, with star formation efficiencies (SFEs) per free-fall time of a few percent, consistent with observations. In contrast, the GTT model generates a population of long-lived clouds with lifetimes $\gtrsim200$ Myr, owing to the extremely low SFEs per free-fall time $(\lesssim3\times10^{-3})$, which renders stellar feedback ineffective. With both models, cloud-cloud mergers extend the lifetimes of GMCs and increase their integrated SFEs by lengthening the star-forming duty cycle, while having only a minor impact on instantaneous efficiencies. On galactic scales, both models broadly reproduce the observed KS relation within its scatter, yielding gas depletion times of a few Gyr. In comparison, an extreme feedback model with the supernova energy boosted by a factor of five, combined with the GTT star formation model, excessively suppresses star formation and produces much longer depletion times ($6$-$20$ Gyr) for this isolated system. These results demonstrate that GMC lifecycles are strongly governed by the adopted star formation model, highlighting the need for improved prescriptions that realistically capture clump-scale star formation.