Emission-Line Galaxies from the Hubble Space Telescope Probing Evolution and Reionization Spectroscopically (PEARS) Grism Survey. II: The Complete Sample
Nor Pirzkal, Barry Rothberg, Chun Ly, Sangeeta Malhotra, James E., Rhoads, Norman A. Grogin, Tomas Dahlen, Gerhardt R. Meurer, Jeremy R. Walsh,, Nimish P. Hathi, Seth H. Cohen, Andrea Bellini, Benne W. Holwerda, Amber N., Straughn, Matthew Mechtley, Rogier A. Windhorst

TL;DR
This paper analyzes HST PEARS grism data to identify and characterize emission-line galaxies at 0<z<1.5, revealing insights into their luminosity functions, morphologies, and galaxy evolution trends.
Contribution
It provides a comprehensive analysis of emission-line regions in galaxies using PEARS data, including detection methods and correlations with galaxy properties, advancing understanding of galaxy evolution.
Findings
Luminosity function flattening with increasing redshift
Star forming regions often show disturbed morphologies
Number density of massive star forming galaxies decreases at lower redshifts
Abstract
We present a full analysis of the Probing Evolution And Reionization Spectroscopically (PEARS) slitess grism spectroscopic data obtained with the Advanced Camera for Surveys on HST. PEARS covers fields within both the Great Observatories Origins Deep Survey (GOODS) North and South fields, making it ideal as a random survey of galaxies, as well as the availability of a wide variety of ancillary observations to support the spectroscopic results. Using the PEARS data we are able to identify star forming galaxies within the redshift volume 0< z<1.5. Star forming regions in the PEARS survey are pinpointed independently of the host galaxy. This method allows us to detect the presence of multiple emission line regions (ELRs) within a single galaxy. 1162 Ha, [OIII] and/or [OII] emission lines have been identified in the PEARS sample of ~906 galaxies down to a limiting flux of ~1e-18 erg/s/cm^2.…
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.
