The Hubble Space Telescope GOODS NICMOS Survey: Overview and the Evolution of Massive Galaxies at 1.5 < z < 3
C.J. Conselice, A.F.L. Bluck, F. Buitrago, A.E. Bauer, R., Gr\"utzbauch, R.J. Bouwens, S. Bevan, A. Mortlock, M. Dickinson, E. Daddi, H., Yan, Douglas Scott, S.C. Chapman, R.-R. Chary, H.C. Ferguson, M. Giavalisco,, N. Grogin, G. Illingworth, S. Jogee, A.M. Koekemoer

TL;DR
This study uses deep near-infrared imaging from the Hubble Space Telescope to analyze the properties, selection methods, and evolution of massive galaxies at redshifts 1.5 to 3, revealing their bimodal color distribution and rapid mass assembly during this epoch.
Contribution
It provides a comprehensive analysis of high-redshift massive galaxies, comparing various selection techniques and demonstrating their effectiveness in identifying these galaxies and their evolutionary trends.
Findings
Single colour selection finds up to 70% of massive galaxies.
Combining multiple selection methods captures nearly all massive galaxies.
Number density of massive galaxies increases eightfold from z=3 to z=1.5.
Abstract
We present the details and early results from a deep near-infrared survey utilising the NICMOS instrument on the Hubble Space Telescope centred around massive M_* > 10^11 M_0 galaxies at 1.7 < z < 2.9 found within the Great Observatories Origins Deep Survey (GOODS) fields. The GOODS NICMOS Survey (GNS) was designed to obtain deep F160W (H-band) imaging of 80 of these massive galaxies, as well as other colour selected objects such as Lyman-break drop-outs, BzK objects, Distant Red Galaxies, EROs, Spitzer Selected EROs, BX/BM galaxies, as well as sub-mm galaxies. We present in this paper details of the observations, our sample selection, as well as a description of features of the massive galaxies found within our survey fields. This includes: photometric redshifts, rest-frame colours, and stellar masses. We furthermore provide an analysis of the selection methods for finding massive…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
