The lifecycle of molecular clouds in nearby star-forming disc galaxies

Abstract

It remains a major challenge to derive a theory of cloud-scale ( 100 pc) star formation and feedback, describing how galaxies convert gas into stars as a function of the galactic environment. Progress has been hampered by a lack of robust empirical constraints on the giant molecular cloud (GMC) lifecycle. We address this problem by systematically applying a new statistical method for measuring the evolutionary timeline of the GMC lifecycle, star formation, and feedback to a sample of nine nearby disc galaxies, observed as part of the PHANGS-ALMA survey. We measure the spatially-resolved (∼ 100 pc) CO-to-Hα flux ratio and find a universal de-correlation between molecular gas and young stars on GMC scales, allowing us to quantify the underlying evolutionary timeline. GMC lifetimes are short, typically 10−30 Myr, and exhibit environmental variation, between and within galaxies. At kpc-scale molecular gas surface densities Σ H 2 8 M pc −2 , the GMC lifetime correlates with time-scales for galactic dynamical processes, whereas at Σ H 2 8 M pc −2 GMCs decouple from galactic dynamics and live for an internal dynamical time-scale. After a long inert phase without massive star formation traced by Hα (75−90 per cent of the cloud lifetime), GMCs disperse within just 1−5 Myr once massive stars emerge. The dispersal is most likely due to early stellar feedback, causing GMCs to achieve integrated star formation efficiencies of 4−10 per cent. These results show that galactic star formation is governed by cloud-scale, environmentally-dependent, dynamical processes driving rapid evolutionary cycling. GMCs and HII regions are the fundamental units undergoing these lifecycles, with mean separations of 100−300 pc in star-forming discs. Future work should characterise the multi-scale physics and mass flows driving these lifecycles.

Dynamical Equilibrium in the Molecular ISM in 28 Nearby Star-forming Galaxies

“…We do not expect these molecular structures to be decoupled from large-scale dynamics, and if this remains the case, these structures might "dissolve" over roughly a galactic dynamical timescale. This picture is in line with recent findings by Chevance et al (2020), namely that the lifetime of molecular clouds in some cases is driven by the timescales of galactic dynamical processes.…”

“…The result of this localized collapse is the star formation feedback that pressurizes the rest of the ISM, providing internal support. Although this is likely a violent process with alternating episodes of collapse and expansion (see, e.g., Kruijssen et al 2019;Rahner et al 2019;Schinnerer et al 2019;Chevance et al 2020), dynamical equilibrium is expected when considering the entire ISM across large spatial scales (? typical size of GMCs and starforming regions) and long timescales (?…”

“…Unlike á ñ P turb,120 pc , which is estimated on 120pc scales and then averaged over the kpc-scale aperture, our S SFR,1 kpc measurements are derived directly on kpc scale. Just like the molecular gas, we expect star formation to cluster on sub-kpc scales (e.g., Grasha et al 2018Grasha et al , 2019Schinnerer et al 2019;Chevance et al 2020). This will cause the S SFR,1 kpc values in Figure 8 to appear lower due to the inclusion of area without star formation, and thus it underestimates the actual SFR surface density relevant to feedback momentum injection.…”

“…, and its 16th-84th percentile range is 0.73dex.A direct estimation of C fb requires high-resolution data tracing SFR on matched∼10-100 pc scales. This is currently not available for our entire sample (c.f.,Kreckel et al 2018;Schinnerer et al 2019;Chevance et al 2020). However, we can obtain a rough estimate of C fb based on our high-resolution CO data, if we assume that the clustering of star formation matches that of the molecular gas.…”

An uncertainty principle for star formation – V. The influence of dust extinction on star formation rate tracer lifetimes and the inferred molecular cloud lifecycle

“…Instead, the method operates on arbitrary maps of tracers of gas and star formation. The development of the HEISENBERG code based on this method (Kruijssen et al 2018) has made it possible to empirically characterize the molecular cloud lifecycle across a dozen nearby galaxies (Kruijssen et al 2019;Chevance et al 2020;Ward et al 2020;Zabel et al 2020).…”