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Molecular gas in low-metallicity starburst galaxies: Scaling relations and the CO-to-H$_2$ conversion factor

Published 18 Dec 2015 in astro-ph.GA | (1512.06153v1)

Abstract: We study the molecular content and the star formation efficiency of 21 Blue Compact Dwarfs (BCDs). We present CO(1-0) and (2-1) observations, further supplemented with additional CO measurements and multiwavelength ancillary data from the literature. We find the CO luminosity to be correlated with the stellar and HI masses, SFR tracers, the size of the starburst and its metallicity. BCDs appear offset from the Schmidt-Kennicutt (SK) law, showing extremely low ($\lesssim$0.1 Gyr) H2 and H2+HI depletion timescales. The departure from the SK law is smaller when considering H2+HI rather than H2 only, and is larger for BCDs with lower metallicity and higher specific SFR. Thus, the molecular fraction and H2 depletion timescale of BCDs is found to be strongly correlated with metallicity. Using this and assuming that the empirical correlation found between the specific SFR and galaxy-averaged H2 depletion timescale of more metal-rich galaxies extends to lower masses, we derive a metallicity-dependent CO-to-H2 conversion factor $\alpha_{CO, Z} \propto (Z/Z_{\odot}){-y}$, with $y=1.5(\pm 0.3)$ in qualitative agreement with previous determinations, dust-based measurements, and recent model predictions. Our results suggest that in vigorously star-forming dwarfs the fraction of H2 traced by CO decreases by a factor of about 40 from $Z \sim Z_{\odot}$ to $Z \sim 0.1 Z_{\odot}$, leading to a strong underestimation of the H2 mass in metal-poor systems when a Galactic $\alpha_{CO, MW}$ is considered. Adopting $\alpha_{CO, Z}$ we find that departures from the SK law are partially resolved. Our results suggest that starbursting dwarfs have shorter depletion gas timescales and lower molecular fractions compared to normal late-type disc galaxies even accounting for the molecular gas not traced by CO emission in metal-poor environments, raising additional constraints to model predictions (Abridged).

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