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Bayesian Component Separation for DESI LAE Automated Spectroscopic Redshifts and Photometric Targeting

Ana Sofía M. Uzsoy, Andrew K. Saydjari, Arjun Dey, Anand Raichoor, Douglas P. Finkbeiner, Eric Gawiser, Kyoung-Soo Lee, Steven Ahlen, Davide Bianchi, David Brooks, Todd Claybaugh, Andrei Cuceu, Axel de la Macorra, Peter Doel, Andreu Font-Ribera, Jaime E. Forero-Romero, Enrique Gaztañaga, Satya Gontcho A Gontcho, Gaston Gutierrez, Mustapha Ishak, Robert Kehoe, David Kirkby, Anthony Kremin, Martin Landriau, Laurent Le Guillou, Aaron Meisner, Ramon Miquel, John Moustakas, Nathalie Palanque-Delabrouille, Francisco Prada, Ignasi Pérez-Ràfols, Graziano Rossi, Eusebio Sanchez, David Schlegel, Michael Schubnell, Hee-Jong Seo, David Sprayberry, Gregory Tarlé, Benjamin Alan Weaver, Hu Zou

Abstract

Lyman Alpha Emitters (LAEs) are valuable high-redshift cosmological probes traditionally identified using specialized narrow-band 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 observed $z = 2-4$ DESI LAE candidate spectra and determine their redshifts with $>$90% accuracy when validated against visually inspected redshifts. Using the $Δχ^2$ value 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.

Bayesian Component Separation for DESI LAE Automated Spectroscopic Redshifts and Photometric Targeting

Abstract

Lyman Alpha Emitters (LAEs) are valuable high-redshift cosmological probes traditionally identified using specialized narrow-band 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 observed DESI LAE candidate spectra and determine their redshifts with 90% accuracy when validated against visually inspected redshifts. Using the value 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.

Paper Structure

This paper contains 25 sections, 32 equations, 11 figures.

Table of Contents

  1. Introduction
  2. Data
  3. DESI
  4. The LAE Samples
  5. ODIN LAEs
  6. Subaru Suprime-Cam LAEs
  7. Method Overview
  8. Prior generation
  9. Sky residual prior
  10. LAE prior
  11. Spectroscopic redshift determination
  12. Uncertainty quantification
  13. Results & Validation
  14. Redshift determination for Subaru LAE targets
  15. Injection-recovery tests
  16. ...and 10 more sections

Figures (11)

  • Figure 1: Filter transmission curves (normalized to a maximum transmission of 1) for the N419, N501, and N673 narrow-band filters from ODIN (red curves), I427, I464, I484, I505, and I527 medium-band filters from the Subaru Suprime Cam (blue curves), and the DECam g band (green curve).
  • Figure 2: Top five eigenvectors from the data-driven covariance matrix of ODIN LAE targets shifted to be at $z = 2.45$. Eigenvectors are scaled by the square root of their respective eigenvalues.
  • Figure 3: An example decomposition of a $z \approx 3.16$ LAE spectrum into its constituent sky residual, LAE, and residual components. Note the different vertical scaling in each panel.
  • Figure 4: $|\Delta \chi^2|$ vs. redshift residual (new $z$ - VI $z$) for 881 Subaru LAE targets that were not used to create the LAE prior (blue points). (top) Orange points denote LAE targets with "LBG" in the VI comments. (bottom) The same plot shown only between -0.005 $<$ New z - VI z $<$ 0.005. 812 (92.3%) of the 881 Subaru LAE targets are contained within this range.
  • Figure 5: $\Delta \chi^2$ vs. $\chi^2 (\text{sky + LAE($z$) + res})$, colored by redshift residual (new z - VI z), for 881 Subaru LAE targets. Colorbar limits are set to $\pm$0.01, with redshift residuals beyond those values set to the darkest colors.
  • ...and 6 more figures