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Automated mining of the ALMA archive in the COSMOS field (A3 COSMOS): Cold molecular gas evolution

Published online by Cambridge University Press:  04 June 2020

Liu Daizhong
Affiliation:
Max-Planck-Institut für Astronomie, Königstuhl 17, D-69117 Heidelberg, Germany email: dzliu@mpia.de
A3 COSMOS Team
Affiliation:
https://sites.google.com/view/a3cosmos/team
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Abstract

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We present new constraints on the cosmic cold molecular gas evolution out to redshift 6 based on systematic mining of the public ALMA archive in the COSMOS field (A3 COSMOS). Our A3 COSMOS dataset contains ∼ 700 galaxies (0.3 ≲ z ≲ 6) with high-confidence ALMA detection and multi-wavelength SEDs. Combining with ∼ 1,200 CO-observed galaxies at 0 ≲ z ≲ 4 (75% at z < 0.1) in the literature, we parameterize galaxies’ molecular gas depletion time and gas fraction each as a function of stellar mass, offset from the star-forming main-sequence and cosmic age. We propose a new functional form which provides a better fit and implies a “downsizing” effect and “mass-quenching”. By adopting galaxy stellar mass functions and applying our gas fraction function, we obtain a cosmic cold molecular gas density evolution in agreement with recent CO blind field surveys as well as semi-analytic modeling. These together provide us a coherent picture of galaxy cold molecular gas, SFR and stellar mass evolution.

Type
Contributed Papers
Copyright
© International Astronomical Union 2020

References

Béthermin, M., et al. 2017, A&A, 607, A89Google Scholar
da Cunha, E., Charlot, S., & Elbaz, D. 2008, MNRAS, 388, 1595CrossRefGoogle Scholar
Decarli, R., et al. 2016, ApJ, 833, 6910.3847/1538-4357/833/1/69CrossRefGoogle Scholar
Decarli, R., et al. 2019, arXiv e-prints,arXiv:1903.09164Google Scholar
Genzel, R., et al. 2015, ApJ, 800, 2010.1088/0004-637X/800/1/20CrossRefGoogle Scholar
Groves, B. A., et al. 2015, ApJ, 799, 9610.1088/0004-637X/799/1/96CrossRefGoogle Scholar
Hughes, T. M., et al. 2017, MNRAS, 468, L10310.1093/mnrasl/slx033CrossRefGoogle Scholar
Liu, , et al. 2019, ApJS, 244, 42Google Scholar
Lutz, D., et al. 2011, A&A, 532, A90Google Scholar
Magdis, G. E., et al. 2011, ApJL, 740, L1510.1088/2041-8205/740/1/L15CrossRefGoogle Scholar
Magdis, G. E., et al. 2012, ApJ, 760, 6CrossRefGoogle Scholar
Magnelli, B., et al. 2012, A&A, 548, A22Google Scholar
Magnelli, B., et al. 2014, A&A, 561, A86Google Scholar
Popping, G., et al. 2019, arXiv e-prints,arXiv:1903.09158Google Scholar
Riechers, D. A., et al. 2019, ApJ, 872, 7CrossRefGoogle Scholar
Saintonge, A., et al. 2017, ApJS, 233, 22CrossRefGoogle Scholar
Santini, P., et al. 2010, A&A, 518, L154Google Scholar
Santini, P., et al. 2014, A&A, 562, A30Google Scholar
Schinnerer, E., et al. 2016, ApJ, 833, 112CrossRefGoogle Scholar
Scoville, N., et al. 2014, ApJ, 783, 8410.1088/0004-637X/783/2/84CrossRefGoogle Scholar
Scoville, N., et al. 2016, ApJ, 820, 8310.3847/0004-637X/820/2/83CrossRefGoogle Scholar
Scoville, N., et al. 2017, ApJ, 837, 15010.3847/1538-4357/aa61a0CrossRefGoogle Scholar
Speagle, J. S., et al. 2014, ApJS, 214, 1510.1088/0067-0049/214/2/15CrossRefGoogle Scholar
Tacconi, L. J., et al. 2018, ApJ, 853, 17910.3847/1538-4357/aaa4b4CrossRefGoogle Scholar
Walter, F., et al. 2014, ApJ, 782, 79CrossRefGoogle Scholar