Comparing SCUBA-2 and ALMA Selections of Faint Dusty Star-forming Galaxies in A2744

Author:

McKay S. J.ORCID,Barger A. J.ORCID,Cowie L. L.ORCID

Abstract

Abstract We make a comparison of deep SCUBA-2 450 and 850 μm imaging on the massive lensing cluster field A2744 with Atacama Large Millimeter/submillimeter Array (ALMA) 1.2 mm data. Our primary goal is to assess how effective the wider-field SCUBA-2 sample, in combination with red JWST priors, is for finding faint dusty star-forming galaxies (DSFGs) compared to the much more expensive mosaicked ALMA observations. We cross-match our previously reported direct (>5σ) SCUBA-2 sample and red JWST NIRCam prior-selected (>3σ) SCUBA-2 sample to direct ALMA sources from the DUALZ survey. We find that roughly 95% are confirmed by ALMA. The red priors also allow us to probe deeper in the ALMA image. Next, by measuring the 450 and 850 μm properties of the full ALMA sample, we show that 46/69 of the ALMA sources are detected at 850 μm and 24/69 are detected at 450 μm in the SCUBA-2 images, with a total detection fraction of nearly 75%. All of the robust (>5σ) ALMA sources that are not detected in at least one SCUBA-2 band lie at 1.2 mm fluxes ≲0.6 mJy and are undetected primarily due to the higher SCUBA-2 flux limits. We also find that the SCUBA-2 detection fraction drops slightly beyond z = 3, which we attribute to the increasing 1.2 mm to 850 μm and 1.2 mm to 450 μm flux ratios combined with the ALMA selection. The results emphasize the power of combining SCUBA-2 data with JWST colors to map the faint DSFG population.

Funder

National Aeronautics and Space Administration

Wisconsin Alumni Research Foundation

Publisher

American Astronomical Society

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3