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Abstract Lyman-alpha-emitting galaxies (LAEs) are typically young, low-mass, star-forming galaxies with little extinction from interstellar dust. Their low dust attenuation allows their Lyαemission to shine brightly in spectroscopic and photometric observations, providing an observational window into the high-redshift Universe. Narrowband surveys reveal large, uniform samples of LAEs at specific redshifts that probe large-scale structure and the temporal evolution of galaxy properties. The One-hundred-deg2DECam Imaging in Narrowbands (ODIN) utilizes three custom-made narrowband filters on the Dark Energy Camera (DECam) to discover LAEs at three equally spaced periods in cosmological history. In this paper, we introduce the hybrid-weighted double-broadband continuum estimation technique, which yields improved estimation of Lyαequivalent widths. Using this method, we discover 6032, 5691, and 4066 LAE candidates atz= 2.4, 3.1, and 4.5 in the extended COSMOS field (∼9 deg2). We find that [Oii] emitters are a minimal contaminant in our LAE samples, but that interloping Green Pea–like [Oiii] emitters are important for our redshift 4.5 sample. We introduce an innovative method for identifying [Oii] and [Oiii] emitters via a combination of narrowband excess and galaxy colors, enabling their study as separate classes of objects. We present scaled median stacked spectral energy distributions for each galaxy sample, revealing the overall success of our selection methods. We also calculate rest-frame Lyαequivalent widths for our LAE samples and find that the EW distributions are best fit by exponential functions with scale lengths ofw0= 53 ± 1, 65 ± 1, and 59 ± 1 Å, respectively.
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This content will become publicly available on March 6, 2027
Bayesian Component Separation for DESI LAE Automated Spectroscopic Redshifts and Photometric Targeting
Abstract Lyα emitters (LAEs) are valuable high-redshift cosmological probes traditionally identified using specialized narrowband photometric surveys. In ground-based spectroscopy, it can be difficult to distinguish the sharp LAE peak from residual sky emission lines using automated methods, leading to misclassified redshifts. We present a Bayesian spectral component separation technique to automatically determine spectroscopic redshifts for LAEs while marginalizing over sky residuals. We use visually inspected spectra of LAEs obtained using the Dark Energy Spectroscopic Instrument (DESI) to create a data-driven prior and can determine redshift by jointly inferring sky residual, LAE, and residual components for each individual spectrum. We demonstrate this method on 881 spectroscopically observedz = 2–4 DESI LAE candidate spectra and determine their redshifts with >90% accuracy when validated against visually inspected redshifts. Using the Δχ2value from our pipeline as a proxy for detection confidence, we then explore potential survey design choices and implications for targeting LAEs with medium-band photometry. This method allows for scalability and accuracy in determining redshifts from DESI spectra, and the results provide recommendations for LAE targeting in anticipation of future high-redshift spectroscopic surveys.
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- Award ID(s):
- 2019786
- PAR ID:
- 10677540
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- the American Astronomical Society
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 999
- Issue:
- 2
- ISSN:
- 0004-637X
- Page Range / eLocation ID:
- 241
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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