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The MUSE Deep Lensed Field on the Hubble Frontier Field MACS~J0416 by Vanzella, E.; Caminha, G. B.; Rosati, P.; Mercurio, A.; Castellano, M.; Meneghetti, M.; Grillo, C.; Sani, E.; Bergamini, P.; Calura, F.; Caputi, K.; Cristiani, S.; Cupani, G.; Fontana, A.; Gilli, R.; Grazian, A.; Gronke, M.; Mignoli, M.; Nonino, M.; Pentericci, L.; Tozzi, P.; Treu, T.; Balestra, I.; Dijkstra, M. is a scholarly article available to read on EtoBox.

What is The MUSE Deep Lensed Field on the Hubble Frontier Field MACS~J0416 about?

Context: A census of faint and tiny star forming complexes at high redshift is key to improving our understanding of reionizing sources, galaxy growth and the formation of globular clusters. Aims: We present the MUSE Deep Lensed Field (MDLF) program. Methods: We describe Deep MUSE observations of 17.1 hours integration on a single pointing over the Hubble Frontier Field galaxy cluster MACS~J0416. Results: We confirm spectroscopic redshifts for all 136 multiple images of 48 source galaxies at 0.9<z<6.2. Within those galaxies, we securely identify 182 multiple images of 66 galaxy components that we use to constrain our lens model. We identify 116 clumps belonging to background high-z galaxies; the majority of them are multiple images and span magnitude, size and redshift intervals of [-18,-10], [~400-3] parsec and 1<z<6.6, respectively, with the most magnified ones probing possible single gravitationally bound star clusters. The depth of the MDLF combined with lensing magnification lead us to reach a detection limit for unresolved emission lines of a few 10$^{-20}$ erg/s/cm2, after correction for lensing magnification. Ultraviolet high-ionization metal lines (and HeII1640) are detect

Author

Vanzella, E.; Caminha, G. B.; Rosati, P.; Mercurio, A.; Castellano, M.; Meneghetti, M.; Grillo, C.; Sani, E.; Bergamini, P.; Calura, F.; Caputi, K.; Cristiani, S.; Cupani, G.; Fontana, A.; Gilli, R.; Grazian, A.; Gronke, M.; Mignoli, M.; Nonino, M.; Pentericci, L.; Tozzi, P.; Treu, T.; Balestra, I.; Dijkstra, M.

Published

2020

Language

EN