Stellar Masses of Giant Clumps in CANDELS and Simulated Galaxies Using Machine Learning

Stellar Masses of Giant Clumps in CANDELS and Simulated Galaxies Using Machine Learning. Huertas-Company, M., Guo, Y., Ginzburg, O., Lee, C. T., Mandelker, N., Metter, M., Primack, J. R., Dekel, A., Ceverino, D., Faber, S. M., Koo, D. C., Koekemoer, A., Snyder, G., Giavalisco, M., & Zhang, H. arXiv e-prints, 2006:arXiv:2006.14636, June, 2020.

Paper abstract bibtex

A significant fraction of high redshift star-forming disc galaxies are known to host giant clumps, whose formation, nature and role in galaxy evolution are yet to be understood. In this work we first present a new automated method based on deep neural networks to detect clumps in galaxy images and show that it is more sensitive and faster than previous proposed methods. We then use this method to systematically detect clumps in the rest-frame optical and UV images of a complete sample of \${\textbackslash}sim1500\$ star forming galaxies at \$110{\textasciicircum}\{7\}M_{\textbackslash}odot\$) off-centered clump but only \${\textbackslash}sim2-5{\textbackslash}%\$ of the total galaxy stellar mass is in those clumps. We also show indications that the contribution of clumps to the stellar mass is more important in extended and low mass galaxies. The simulations explored in this work overall reproduce the shape of the observed clump stellar mass function when confronted under the same conditions although tend to lie in the lower limit of the confidence intervals of the observations. This agreement suggests that most of the observed clumps are formed in-situ through violent disk instabilities.

@article{huertas-company_stellar_2020,
	title = {Stellar {Masses} of {Giant} {Clumps} in {CANDELS} and {Simulated} {Galaxies} {Using} {Machine} {Learning}},
	volume = {2006},
	url = {http://adsabs.harvard.edu/abs/2020arXiv200614636H},
	abstract = {A significant fraction of high redshift star-forming disc galaxies are 
known to host giant clumps, whose formation, nature and role in galaxy
evolution are yet to be understood. In this work we first present a new
automated method based on deep neural networks to detect clumps in
galaxy images and show that it is more sensitive and faster than
previous proposed methods. We then use this method to systematically
detect clumps in the rest-frame optical and UV images of a complete
sample of \${\textbackslash}sim1500\$ star forming galaxies at \$110{\textasciicircum}\{7\}M\_{\textbackslash}odot\$) off-centered clump but only \${\textbackslash}sim2-5{\textbackslash}\%\$ of the
total galaxy stellar mass is in those clumps. We also show indications
that the contribution of clumps to the stellar mass is more important in
extended and low mass galaxies. The simulations explored in this work
overall reproduce the shape of the observed clump stellar mass function
when confronted under the same conditions although tend to lie in the
lower limit of the confidence intervals of the observations. This
agreement suggests that most of the observed clumps are formed in-situ
through violent disk instabilities.},
	urldate = {2020-06-29},
	journal = {arXiv e-prints},
	author = {Huertas-Company, M. and Guo, Y. and Ginzburg, O. and Lee, C. T. and Mandelker, N. and Metter, M. and Primack, J. R. and Dekel, A. and Ceverino, D. and Faber, S. M. and Koo, D. C. and Koekemoer, A. and Snyder, G. and Giavalisco, M. and Zhang, H.},
	month = jun,
	year = {2020},
	keywords = {Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics},
	pages = {arXiv:2006.14636},
}

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