Lyman Break Galaxies at z~1.8-2.8: GALEX/NUV Imaging of the Subaru Deep Field
Chun Ly (1), Matthew A. Malkan (1), Tommaso Treu (2), Jong-Hak Woo, (1), Thayne Currie (3), Masao Hayashi (4), Nobunari Kashikawa (5), Kentaro, Motohara (4), Kazuhiro Shimasaku (4), and Makiko Yoshida (4) ((1) UCLA, (2), UCSB, (3) CfA, (4) U. Tokyo, Japan, (5) NAOJ)

TL;DR
This study identifies and analyzes a large sample of Lyman break galaxies at redshifts 1.8-2.8 using Subaru and GALEX data, revealing a higher UV luminosity function than previous surveys and supporting the idea of z~2 as the peak epoch of star formation.
Contribution
It provides a large photometric sample of LBGs at z~2, compares their UV luminosity function with other galaxy populations, and discusses implications for star formation history.
Findings
UV luminosity function is 1.7 times higher than z~2 BXs and z~3 LBGs.
Evidence suggests different photometric selections yield overlapping but distinct galaxy populations.
Star formation rate density peaks at z~2, indicating this as the epoch of maximum star formation.
Abstract
Abridged: A photometric sample of ~7100 V<25.3 Lyman break galaxies (LBGs) has been selected by combining Subaru/Suprime-Cam BVRci'z' data with deep GALEX/NUV imaging of the Subaru Deep Field. Follow-up spectroscopy confirmed 24 LBGs at 1.5<z<2.7. Among the optical spectra, 12 have Ly-alpha emission with rest-frame equivalent widths of ~5-60AA. The success rate for identifying LBGs as NUV-dropouts at 1.5<z<2.7 is 86%. The rest-frame UV (1700AA) luminosity function (LF) is constructed from the photometric sample with corrections for stellar contamination and z<1.5 interlopers. The LF is 1.7+/-0.1 times higher than those of z~2 BXs and z~3 LBGs. Three explanations were considered, and it is argued that significantly underestimating low-z contamination or effective comoving volume is unlikely: the former would be inconsistent with the spectroscopic sample at 93% confidence, and the second…
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.
